{"paper_id":"4bb9a8e0-6962-4e75-8895-2824ae2ad953","body_text":"Multi‑omics analysis identifies different molecular subtypes with unique outcomes in early-stage poorly differentiated lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi‑omics analysis identifies different molecular subtypes with unique outcomes in early-stage poorly differentiated lung adenocarcinoma Bing Liu, Wei Tao, Xuantong Zhou, Li-Di Xu, Yanrui Luo, Xin Yang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6082622/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2025 Read the published version in Molecular Cancer → Version 1 posted 7 You are reading this latest preprint version Abstract Introduction: Early-stage poorly differentiated lung adenocarcinoma (LUAD) is plagued by a high risk of postoperative recurrence, and its prognostic heterogeneity complicates treatment and surveillance planning. We conducted this integrative multi-omics study to identify those patients with a truly high risk of adverse outcomes. Methods: Whole-exome, RNA and whole methylome sequencing were carried out on 101 treatment-naïve early-stage poorly differentiated LUADs. Integrated analyses were conducted to disclose molecular characteristics and explore molecular subtyping. Functional validation of key molecules was carried out through in vitro and in vivo experiments. Results: Recurrent tumors exhibited significantly higher ploidy (p = 0.024), the fraction of the genome altered (FGA, p = 0.042), and aneuploidy (p = 0.022) compared to non-recurrent tumors, as well as a higher frequency of CNVs. Additionally, recurrent tumors showed hypomethylation at both the global level and in CpG island regions. Integrative transcriptomic and methylation analyses identified three molecular subtypes (C1, C2, and C3), with the C1 subtype presenting the worst prognosis (p = 0.024). Although frequently mutated genes showed similar mutation frequencies across the three subtypes, the C1 subtype exhibited the highest tumor mutation burden (TMB), mutant-allele tumor heterogeneity (MATH), aneuploidy, and HLA loss of heterozygosity (HLA-LOH), along with relatively lower immune cell infiltration. Furthermore, GINS1 and CPT1C were found to promote LUAD progression, and their high expression correlated with a poor prognosis. Conclusions: This multi-omics study identified three integrative subtypes with distinct prognostic implications, paving the way for more precise management and postoperative monitoring of early-stage poorly differentiated LUAD. Multi-omics poorly differentiated lung adenocarcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung adenocarcinoma (LUAD), the predominant pathological subtype of lung cancer, demonstrates significant histological heterogeneity. In 2020, the pathology committee of the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system for invasive LUAD, where any tumor with 20% or more high-grade patterns (solid, micropapillary, and complex glandular patterns) was classified as a poorly differentiated (Grade 3) invasive pulmonary adenocarcinoma[1], which accounted for 34-55% of all resected LUADs and predicted the worst survival outcome[2]. While this pattern-based grading system represents a significant advancement, it faces limitations in prognostic stratification across the heterogeneous spectrum of disease outcomes, particularly in the poorly differentiated group. Notably, only ~ 30% of patients with early-stage poorly differentiated LUAD experience postoperative recurrence[3], underscoring the critical need for additional molecular or biological parameters to complement the current grading system. Such multi-dimensional refinement would enable a more precise prognostic evaluation and formulate proper plans for treatment and surveillance. Omics studies are capable of providing multi-dimensional and high-resolution molecular information, thereby, assisting in identifying molecular characteristics and refining disease subtypes in solid tumors. Over the past decade, large-scale omics studies have significantly advanced our understanding of lung adenocarcinoma (LUAD) by delineating comprehensive mutational profiles, establishing molecular classification systems based on actionable driver mutations (e.g., EGFR and ALK), and uncovering critical roles of epigenetic regulation (e.g., chromatin modifications) and post-transcriptional processes (e.g., alternative splicing) in tumor pathogenesis[4]. Moreover, several transcriptomic stratifications have been also established, which are associated with specific genomic alterations for targeted therapy or immunotherapy, and describe different clinical outcomes[5, 6]. These studies have further refined the molecular classification for the personalized treatment of LUADs. However, since the clinicopathological phenotype still dominates the routine framework of clinical diagnosis, treatment and prognostic evaluation, incorporating an evolving understanding of molecular profiling with histopathological development is an urgent need for precise decision-making. Based on this requirement, adding a molecular classification could more accurately judge the prognosis within a certain pathological domain with a negative survival impact. Recent studies have shown that clustered molecular signatures can contribute to prognostic discrepancies in ovarian cancer and glioma presenting with poorly differentiated features[7-9]. Therefore, elucidating the molecular landscape of early-stage poorly differentiated LUAD could lay a foundation for comprehending the high-risk molecular characteristics of this heterogeneous entity and developing reliable prognostic biomarkers and precise strategies. Pioneering studies have explored the mutational features of poorly differentiated (Grade 3) LUADs, which showed that they had a greater proportion of ALK rearrangements and KRAS mutations than those with Grades 1-2[10]. Additionally, other studies have shown that micropapillary or solid predominant poorly differentiated LUADs have a high tumor mutational burden (TMB), fraction of genome altered (FGA) and copy number amplifications (CNV)[11]. These studies indicate that poorly differentiated LUADs have relatively specific genomic characteristics. Some recent studies have evaluated the correlation between histological grade and PD-L1 expression and immune cell infiltration, and found that poor differentiated tumors exhibited higher PD-L1 expression and more T lymphocyte infiltration[12]. These results suggest that patients with poor differentiated tumor are more likely to benefit from immunotherapy. Despite these studies accelerating our understanding of the genomic and immune microenvironmental features of poor differentiated LUAD to some extent, comprehensive and in-depth molecular characterization of this disease entity remains elusive, especially from a multi-omics perspective. In this study, we conducted an integrative multi-omics analysis of genomic, epigenetic (methylation) and transcriptomic data from 101 early-stage poorly differentiated LUAD tumors and their paired normal tissues. Our study delineated the comprehensive characteristics of this aggressive disease entity and identified molecular subtypes with distinct prognoses, which could facilitate precise treatment and postoperative monitoring. Materials and methods Collection of clinical specimens and public dataset We enrolled 101 treatment-naïve patients with early-stage poorly differentiated LUAD who underwent radical resection between July 2012 and December 2017 at Peking University Cancer Hospital & Institute. All patients were confirmed as pathological T1-3N0M0 stage (stage I-II) according to the 8 th edition of the lung cancer staging system[13] and did not received neoadjuvant therapy, including chemotherapy, targeted therapy or immunotherapy. All hematoxylin and eosin (HE) stained slides were reviewed according to the 2015 WHO classification of lung cancer and the new grading system proposed by the IASLC pathology committee[1, 14]. All tumors were identified as grade 3 invasive LUAD. Detailed clinical information of the individual patients is listed in Table S1 . As for specimen collection, primary tumor specimens and paired normal tissues were collected immediately after resection and then snap-frozen and stored at -80 ℃ at the Biobank of Peking University Cancer Hospital & Institute until further processing. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Peking University Cancer Hospital & Institute (Institutional Review Board No. 2024KT65). Written informed consent was obtained from each patient before surgery. RNA read count files for 566 patients from the TCGA LUAD project were obtained from the TCGA legacy archive (https://gdc.cancer.gov/about-data/publications/pancanatlas). Corresponding clinical data for these patients were retrieved from cBioPortal (https://www.cbioportal.org/). For comparative analysis with the HG cohort, only patients with early stage (TNM stage I and II) from the TCGA cohort were included, referred to as the TCGA cohort. A second independent cohort (GSE31210)[15] consisting of more than 200 early-stage LUAD cases was utilized to further validate the transcriptomic subtyping. Nucleic acid extraction Genomic DNA and total RNA was extracted from pairs of tumor specimens and normal tissues in Genecast Biotechnology Co., Ltd. (Wuxi, China). Briefly, DNA and RNA was extracted from fresh frozen tissue blocks using the AllPrep DNA/RNA Mini Kit (80204, Qiagen) according to the manufacturer's protocol. A total of 101 patients had nucleic acid extracted. Seventy-nine patients had both RNA and DNA extracted, while 22 patients had only RNA or DNA. Whole-exome sequencing (WES) and Genomic data processing Extracted DNA was quantified by Qubit dsDNA HS Assay kit (Life Technologies, California, USA). Then DNA was fragmented into 150–200 bp by using Covaris M220 Focused-ultrasonicator™ Instrument (Covaris, Massachusetts, USA). Library construction and whole-exome capture of genomic DNA were performed using the KAPA Hyper Prep Kit (Illumina platforms) (KAPA Biosystems, Wilmington, MA) and Twist Human Core Exome kit (Twist Bioscience, San Francisco, USA) following the manufacturer’s instruction. The captured DNA was then sequenced on an Illumina NovaSeq 6000 platform with 100-bp paired-end sequencing. The average sequencing depth was 229-fold for tumors and 200-fold for normal tissues. All raw Illumina sequence data were demultiplexed and converted to fastq files, with the subsequent trimming of adaptors, contamination, and low-quality nucleotides to obtain clean data by using Trimmomatic (version 0.36)[16]. Sentieon[17] (version 202112.04) was used to align the clean reads to the human reference genome (hg19) by the bwa mem algorithm with default parameters. The raw BAM files obtained were subjected to various processing steps including sorting, removal of duplicate reads, local realignment, and base quality score recalibration (BQSR) by using Sentieon tools. These steps were performed to generate final BAM files, which were used for subsequent analysis such as coverage and depth statistics, as well as mutation calling analysis. Somatic mutation calling Somatic nucleotide variants (SNVs) and insertions/deletions (InDels) were detected for each paired sample using GATK Mutect2[18] (version 4.1.9.0). The resulting variant calls in VCF format were subsequently annotated with ANNOVAR[19]. High-confidence somatic mutations were retained based on the following stringent criteria: total sequencing depth of ≥ 40X, at least 4 supporting reads, variant allele frequency (VAF) ≥ 0.05, classification as nonsynonymous variants (resulting in amino acid changes), and a maximum population frequency of < 0.02 in the 1000 Genomes Project, Exome Aggregation Consortium (ExAC), and Genome Aggregation Database (gnomAD). The top 30 frequently mutated genes in the HG cohort were subsequently analyzed for mutual exclusivity and co-occurrence using the maftools R package[20] (version 2.12.05). Tumor mutational burden (TMB) and Mutant-allele tumor heterogeneity (MATH) score calculation Tumor mutational burden (TMB) for the HG cohort was quantified as the total number of somatic nonsynonymous variants within the entire covered exome region (sequencing depth > 40X), expressed in mutations per megabase (Mb). The mutant-allele tumor heterogeneity (MATH) score was computed using all somatic variants with a variant allele frequency (VAF) ranging from 0.05 to 1, applying the formula: 100 × median absolute deviation (MAD) / median of the VAF[21]. Somatic copy number alteration calling FACETS[22] (version 0.6.2) was utilized to identify somatic copy number variant (CNV), as well as to determine tumor purity and ploidy. The total fraction of genome altered (FGA) was calculated as the percentage of a tumor genome showing a copy number different from the whole genome based on the CNV segment file for each tumor. Genomic Identification of Significant Targets in Cancer, version 2.0 (GISTIC2, version 2.0.23)[23] was employed to analyze focal genomic regions that exhibited significant amplification or deletion across all or Recurrence/Recurrence-free subgroup tumors. The aneuploidy score was also calculated based on the total number of altered arms for each tumor as previously suggested[24]. For the gene-wise result from GISTIC2, spearman correlation coefficients were computed to assess the correlation between gene level CNV and mRNA abundances, with an FDR threshold of less than 0.05. Chromosomal number instability (CNI) and microsatellite instability (MSI) score calculation We utilized CNVkit (version 0.9.2)[25] to detect copy number variations (CNVs) in tumor samples from each patient. The \"--reference\" parameter was employed to specify a copy number baseline derived from the Genecast normal database, serving as a negative control. After correction for GC content and length of target region using proprietary algorithms for each region, the read counts were transformed into log2 ratios and converted into Z-score based on Gaussian transformations versus a normal control group. The target regions that satisfied the Z-score greater than the 95 th percentile plus twice-times absolute standard deviation of the normal control group were retained, and the Z-score was summed as the CNI score[26]. MSIsensor2 (version v0.1) (https://github.com/niu-lab/msisensor2) was used with default parameters to detect microsatellite instability (MSI) score. HLA genotyping and HLA-LOH analysis Reads from regions of HLA genes were extracted from normal BAM files using SAMtools (version 1.3)[27] and subsequently analyzed by HLA-HD software (version 1.2.0.1)[28] to identify Human Leukocyte Antigen class I (HLA-I) genotypes, employing the following parameters: minimum tag size set to 50 and cutting rate set to 0.95. Loss of heterozygosity (LOH) in HLA genes was determined using LOHHLA software (version 1.1.6)[29]. Neoantigen prediction Neoantigens were predicted using the netMHC-4.0 (version 4.0a) algorithm, incorporating somatic SNVs, InDels, and HLA genotypes[30]. Predicted results meeting the criteria of a binding affinity (Aff) <= 500 for the mutant (mut) and an Aff(mut)/Aff(wild) ratio < 1 were identified as neoantigens. The neoantigen burden (TNB) for each tumor sample was determined by summing the number of predicted binder mutations per Mb (whole covered exome region was same to TMB). Pathway alteration analysis The somatic mutation genes were categorized into 10 canonical oncogenic signaling (COS) pathways and 8 DNA damage repair (DDR) pathways, based on previous research conducted separately[31]. The COS pathway comprised 335 genes and included pathways such as cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGF signaling, p53, and β-catenin/Wnt. The DDR pathway encompassed mismatch repair (MMR), base excision repair (BER), checkpoint factors (CPF), Fanconi anemia (FA), homologous recombination repair (HRR), nucleotide excision repair (NER), nonhomologous end-joining (NHEJ), and DNA translesion synthesis (TLS). It involved a total of 233 genes. If a mutated gene was found in a specific pathway, it was inferred that the patient had a mutation in that pathway. The mutation frequency within a cohort was calculated as the number of patients with mutations in that pathway divided by the total number of patients. Comparison of the mutation frequency of each pathway across different cohorts or groups was performed using Fisher’s exact test. Mutational signatures analysis and comparison We performed mutational signature analysis for the HG cohort using the DeconstructSigs R package (v1.8.0)[32]. Thirty COSMIC cancer signatures (https://cancer.sanger.ac.uk/signatures/signatures_v2/) were considered, and the contributions (weights) of these signatures in each tumor were normalized to a range between 0 and 1. The weight values of signatures between the Re and Rf groups were compared using the Wilcoxon rank-sum test. RNA sequencing (RNA-seq) and data processing The quantity of extracted RNA was measured using a Qubit 3.0 Fluorometer, while the quality was assessed using the Agilent 2100 Bioanalyzer system assay. After the rRNA removal from the total RNA, the cDNA library was constructed using the SMARTer Stranded Total RNA-Seq Kit v2 (634412, Takara). Following PCR enrichment and purification of adapter-ligated fragments, the libraries were paired-end sequenced (PE150) using the Illumina NovaSeq 6000 Sequencing System. The average sequencing depth was 56 million reads for both tumor and normal tissue samples. Cutadapt (version 4.4) (https://doi.org/10.14806/ej.17.1.200) was utilized to remove the last 3 bases from each read. Trimmomatic (version 0.36)[16] was utilized to eliminate reads that contained adaptors, poly-N sequences, and low-quality reads using default parameters. The resulting trimmed reads were then aligned to the human hg19 reference transcriptome using hisat2 (version 2.1.0)[33]. The alignment data in BAM format was sorted and indexed using SAMtools (version 1.3)[27]. Differential gene expression and pathway enrichment analysis To estimate the expression level of each gene, featureCounts (version 1.6.5)[34] were applied. Transcripts per million (TPM) values were calculated by normalizing the read counts, dividing them by the gene length and the total number of reads mapped to protein-coding genes. In total, 18,071 genes were initially profiled. DEGs (Differential Gene Expression) between different groups were identified using the DESseq2 package (version 1.38.3)[35] in the R software, with the criteria of |log2 (Fold Change)| > 1 and a Bonferroni-adjusted p-value < 0.05. Gene Set Enrichment Analysis (GSEA, version 4.3.3) was employed to perform pathway enrichment analysis among different groups[36]. Molecular Signatures Database (MSigDB, version 7.1) of hallmark gene sets (H), curated gene sets (C2), ontology gene sets (C5), oncogenic signature gene sets (C6), and immunologic signature gene sets (C7) were used in GSEA analysis. Differential gene enrichment analysis between groups was performed using the clusterProfiler R package using Reactome databases. The analysis included all differentially expressed genes, as well as those that were upregulated and downregulated. A significance threshold of p-value < 0.05 was applied. Immune cell infiltration and gene expression signatures analysis To evaluate the tumor microenvironment, the ESTIMATE R package (v1.0.13)[37] was employed with default settings to derive the ImmuneScore and StromalScore from gene expression data. A higher ImmuneScore indicates a greater immune cell infiltration within the tumor. To predict responses to immune checkpoint blockade (ICB) therapy in our HG cohort, we utilized an 18-gene T cell-inflamed gene expression profile (GEP)[38]. The GEP score for each sample was calculated as a weighted sum of these 18 genes, normalized against 11 housekeeping genes. Signature enrichment scores for 28 immune cell subsets within the tumor microenvironment[39] and 14 functional states derived from the CancerSEA database[40] were calculated using the gene set variation analysis (GSVA) R package (version 1.42.0)[41]. Whole methylome sequencing (WMS) and data processing After the DNA extraction, WMS (Whole-methylome sequencing) libraries were generated using the NEBNext Enzymatic Methyl-seq Kit from New England Biolabs, according to the manufacturer's instructions. The quantification of the libraries was carried out using the Qubit dsDNA HS Assay Kit from Thermo Fisher Scientific. The libraries were then subjected to paired-end sequencing with a read length of 100 base pairs on the NovaSeq 6000 platform from Illumina. The average sequencing depth was 9-fold for both tumor and normal tissue samples. The raw methylation sequencing reads were processed using Trimmomatic (version 0.36)[16] to remove adaptors and eliminate low-quality reads. The clean reads were then aligned to the human reference genome (hg19) and deduplicated using BisMark (version 0.23.0)[42]. SAMtools (version 1.3)[27] and BamUtil (https://github.com/statgen/bamUtil) were used for sorting and overlap-clipping of mapped reads. Reads with mapping quality below 20 were filtered out by SAMtools. The methylation status of each CpG site was extracted from a sorted bam file using the bismark methylation extractor function from BisMark. The beta value for each CpG site was calculated as the ratio of methylated CpGs to the sum of methylated and unmethylated CpGs in each sample. To examine genome-wide methylation patterns, the genome was divided into 1,846 non-overlapping 1-Mb segments after excluding regions that overlapped with Duke blacklisted regions or the hg19 gap track38[43]. Regions in 1-Mb segment and CpG island (https://genome.ucsc.edu/cgi-bin/hgTables) were then used to calculate the mean methylation. Tumor fraction was also extracted from the whole-methylome sequencing data using ichorCNA (version 0.2.0)[44], with normal copy number variation (CNV) files as a reference. Furthermore, an updated plasma aneuploidy score (PAscore) was calculated to summarize chromosome arm-level copy number alternation by adapting a previously described approach, using 30 PBMC profiles as reference baseline[45]. Differentially methylation region (DMRs) analysis To identify differentially methylated regions (DMRs) between recurrence and non-recurrence tumors, we utilized the DSS package (version 2.47.1)[46], with the results obtained from bismark methylation extractor as input. The following parameters were configured for DSS analysis: smoothing set to TRUE, smoothing span set to 500, minimum number of CpGs (minCG) set to 3, minimal length (minlen) set to 50, delta set to 0.1, distance threshold for merging (dis.merge) set to 100, percentage of significant probes (pct sig) set to 0.5, and p value <0.05 was considered as statistical significant. DMRs were categorized as hyperDMRs or hypoDMRs based on the direction of methylation change. The average methylation levels in both directions were calculated using the smoothened CpG methylation levels. ChIPseeker (version 1.32.0)[47] was used for DMR annotation. Association analysis between gene expression and promoter methylation To investigate the relationship between gene expression and DNA methylation at the gene level, we categorized the gene expression levels into deciles based on the ascending distribution of log2-transformed TPM values[48]. The methylation level of a gene was determined by calculating the average methylation level of the CpGs within the nearest gene interval to the transcription start site (TSS). The criteria for selecting the interval were as follows: 1) If more than three CpGs were found within a 200bp range of the TSS, we assigned the average DNA methylation value of the adjacent CpGs in the TSS200 region to the corresponding gene; 2) If there were fewer than three CpGs within the TSS200 region, we calculated the average of the CpGs located in both TSS200 and the first exon of the gene; 3) If there were fewer than three CpGs in TSS200 or first exon, we calculated the average of the CpGs located in the upstream 1500bp region extending towards the first exon of the gene. Genes with fewer than three CpGs in the interval from TSS1500 to the first exon were excluded from the analysis. Consensus Clustering for RNA datasets Prior to clustering analysis, we selected the top 500, 1,000, 1,500, 2,000, 2,500, 3,000, and 3,500 most variable coding genes based on their median absolute deviation (MAD) from the differentially expressed genes (DEGs) between tumor and normal samples, using the CancerSubtypes R package (version 1.20.0)[49]. Consensus clustering was then performed on these gene subsets using the ConsensusClusterPlus R package (version 1.62.0)[50]. The partitioning around medoids (PAM) algorithm with Spearman distance was employed for clustering. Due to the limited number of samples (n=82), the maximum number of clusters was set to six. To identify the most representative samples within each cluster, silhouette scores were computed for all samples using the CancerSubtypes R package. A two-cluster solution based on the top 3,500 most variably expressed genes (MAD-ranked) was selected due to its relatively higher average silhouette value, distinct separation between clusters in the consistent heatmap, and significant association with patient recurrence-free survival (RFS). Tumors exhibiting a silhouette value less than 0, as well as those from patients lacking recurrence-free survival (RFS) data (n=2), were excluded from both the survival analysis and subsequent single-sample transcriptomic classifier analysis. Construction and validation of the single-sample transcriptomic classifier Building on previously published studies[51], we developed a Spearman nearest-centroid classifier specifically tailored for lung adenocarcinoma (LUAD) patients, utilizing RNA sequencing-derived gene expression data. The construction of the classifier involved first calculating the mean expression levels of samples within each predefined subtype (as described in the aforementioned section). Subsequently, for each individual sample, we determined the Spearman correlation between its gene expression profile and the centroid expression profiles of the subtypes. Each sample was then assigned to the subtype with the highest Spearman correlation coefficient. Samples exhibiting a maximal correlation value below 0.2 were deemed to have a weak association with any subtype and were excluded from the subsequent survival analysis. To validate the robustness of this RNA-based classification method, we applied it to transcriptomic data from the TCGA cohort. The transcripts per million (TPM) values were calculated from raw read counts using the same method as described for the HG cohort. Consensus Clustering for DNA methylome datasets To profile the DNA methylation patterns across 98 tumor samples, we first calculated the average DNA methylation value for CpG sites located within a region of +/- 2000 base pairs of the transcription start site (TSS). This approach allows us to capture the DNA methylation status in the promoter regions of genes, which often play a critical role in regulating gene expression[52]. The subtyping method was similar to that used for RNA datasets. For this analysis, the distance metric applied was 'euclidean,' and the clustering algorithm employed was 'k-means.' A two-cluster solution based on the top 1,000 most variably expressed genes (ranked by median absolute deviation) was selected. Tumors with a silhouette value less than 0, as well as those from patients without recurrence-free survival (RFS) data, were excluded from the survival analysis. Integrate analysis of RNA and WMS subtyping We utilized the NetworkD3 package (version 0.4) in R to generate a Sankey diagram that illustrates the relationships among RNA subtypes, WMS subtypes, and patient recurrence status. Our analysis revealed that the WMS subgroup effectively partitioned the RNA R1 subgroup into two distinct clusters. In contrast, nearly all samples from the RNA R2 subgroup remained within the same category in the WMS clustering analysis and exhibited fewer recurrences. To integrate the clustering results from both RNA and WMS omics data, we reclassified the samples as follows: samples belonging to both the RNA R1 and WMS M2 subgroups were combined into subtype C1; those belonging to the RNA R1 and WMS M1 subgroups were designated as subtype C2; and samples within the RNA R2 subgroup were retained as subtype C3. Identification of stably expressed differentially expressed genes (DEGs) across three integrative subtypes and their prognostic evaluation Differentially expressed gene (DEG) lists were obtained for each pairwise comparison of the three subtypes (FDR < 0.05 and |log2FC| > 1). For each subtype, candidate gene lists for both high expression and low expression were generated by taking the intersection of relevant comparisons. For example, in the C1 subtype, genes highly expressed in C1 were identified by intersecting C1vsC2_up and C1vsC3_up, while genes with low expression in C1 were identified by intersecting C1vsC2_down and C1vsC3_down. To obtain a list of stably expressed DEGs in each subtype, gene filtering was performed. Initially, genes were filtered based on their Transcripts Per Million (TPM) values, retaining those with TPM > 1 in more than 50% of samples within the relevant subtype. Subsequently, genes were further filtered based on their coefficient of variation (CV). The CV of TPM values was calculated within each specific subtype, and genes were ranked in ascending order according to their CV values. The top 20 genes with the lowest CV, indicating stable expression within the subtype, were selected. If fewer than 20 genes met this criterion, all available genes were included. To assess the prognostic impact of highly and stably expressed genes in the C1 subtype, we calculated p-values and hazard ratios (HRs) for each gene across percentiles from 0.1 to 0.9 in both the HG and TCGA cohorts. Bubble plots were generated to visualize the HRs and p-values for recurrence-free survival (RFS) in the HG cohort and disease-free survival (DFS) in the TCGA cohort. Additionally, we utilized the surv_cutpoint function from the survminer R package to estimate the optimal cutoff for each gene. Cell Culture SPCA-1 was purchased from Chinese Academy of Sciences Cell Bank (Shanghai, China) and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (HyClone, South Logan, UT, USA), penicillin (100 U/mL), and streptomycin (100 μg/mL). HEK293T was purchased from the American Type Culture Collection (ATCC) (Manassas, VA) and cultured in DMEM supplemented with 10% fetal bovine serum (HyClone, South Logan, UT, USA), penicillin (100 U/mL), and streptomycin (100 μg/mL). All cells were maintained at 37℃ in a humidified cell incubator with 5% CO 2 . Lentivirus infections shRNA oligos targeting GINS1 or CPT1C and a non-targeting oligo control were engineered into pSIH-puro plasmid. The target sequences for short hairpin RNA were as followed: shGINS1 #1: 5’-CAAGTTCTGGAGGAGATGAAA-3’; shGINS1 #2: 5’- CTTGCCAAATGCATTACGATT-3’; shCPT1C #1: 5’-CTCACGTTTCTGGAATG ACTT-3’; shCPT1C #2: 5’- CCTGCTGATGACCATGGTTAT-3’. For pSIH-puro lentivirus production, the packaging plasmids vSVG, pLP1 and pLP2 were used. The indicated packaging plasmids and lentiviral vectors were co-transfected into HEK293T cells. After 48h transfection, the supernatant containing lentivirus particles was collected and stored in aliquots at -80 °C. For lentivirus infection, cells were first treated with polybrene (5 µg/mL) (TR-1003, Sigma), then infected with the indicated lentivirus. Stable cell populations were established by selecting with puromycin (2 μg/mL) (540222, Sigma). RNA extraction and qRT-PCR Total RNA was extracted with TRIzol reagent (Thermo Fisher Scientific). The cDNAs were obtained using Quantscript RT kit (Tiangen, Beijing, China) according to the manufacture’s protocol. Real-time RT-PCR was performed by using SYBR Premix Ex TaqTM II (TaKaRa, Japan) on Step-one plus real-time PCR system (Applied Biosystems, Foster City, CA, USA), according to the manufacturer’s instructions. The sequences of primers for qRT-PCR were as followed: GINS1 Forward primer-ACGAGGATGGACTCAGACAAG; GINS1 Reverse primer-TGCAGCGTCGATTT CTTAACA; CPT1C Forward primer-GGATGGCACTGAAGAGGA AA; CPT1C Reverse primer- TCCTGGAAAAGGCATCTCTC; GAPDH Forward primer-CCGGGAAACTGTGGCGTGATGG; GAPDH Reverse primer-AGGTGGAGGAG TGGGTGTCGCTGTT. Antibodies and reagents Antibodies used in this study were as follows: Anti-GINS1 (PA562341, Invitrogen), Anti-CPT1C (66072, Proteintech), Anti-β-actin (A5316, Sigma). Secondary antibodies included HRP Goat Anti-Mouse (926-80010, LI-COR) and HRP Goat Anti-Rabbit (926-80011, LI-COR). Transwell assay SPCA-1 cells (8×10 4 per insert) were suspended in FBS-free RPMI1640 and seeded into the upper chambers with or without pre-coated matrigel (BD, Franklin Lakes, NJ, USA). The bottom chambers were added with RPMI 1640 medium supplemented with 10% FBS. After 24 hr incubation, the migratory or invasive cells were methanol-fixed and stained with crystal violet. Cells in three randomly selected fields were photographed and statistically analyzed. Cell viability and colony formation assays Cell viability was quantified by CCK-8 assays. SPCA-1 cells with indicated treatment were seeded into 96-well plates (4×10 4 cells/mL; 100 µL/well). Cell Proliferation Reagent CCK-8 (#CK04, Dojindo Molecular Technologies, Japan) was added to the cell culture medium at a ratio of 1:10. After 1 hr of incubation at 37℃, absorbance at 450 nm was measured using a microplate reader (BioTek). For colony formation assays, SPCA-1 cells with indicated treatment were seeded into 6-well plates (4×10 3 cells/well), followed by incubation at 37°C for 9-12 days until the development of visible colonies. Colonies were stained with crystal violet staining solution and counted. Xenograft tumor model 5-week-old, female BALB/c nude mice were purchased from Vital River (Beijing, China). 2×10 6 SPCA-1 cells with stable GINS1/CPT1C depletion or control cells were subcutaneously injected into the flanks of mice in separate groups. Each group was composed of 5 mice, randomly chosen. Following 3 weeks, the mice were sacrificed. Tumors were weighed and analyzed by a two-tailed, unpaired Student’s t test. The Institutional Animal Care and Use Committee of Peking University Cancer Hospital and Institute approved all animal experiments. Quantification and statistical analysis All data statistics and figure generation were conducted using R version 4.1.2. Various statistical tests were employed based on the type of variables being compared. Continuous variables were compared using either the two-sided Wilcoxon rank-sum test or the Kruskal-Wallis rank-sum test. Categorical variables were compared using either the chi-square test or the two-sided Fisher's exact test. Survival analyses were performed using the Kaplan–Meier method and two-sided log-rank tests were used to compare survival curves (R packages survival and survminer). Univariate and multivariate Cox regression analyses were conducted using the survival and survminer packages. To evaluate the predictive ability of each Cox module at specific time points (first year, third year, and fifth year), recurrence prediction analyses were performed using the survival and timeROC packages. Chi-square proportion calculated using R package rms was used to assess the relative contribution of each variable to survival risk. Statistical significance was defined as p < 0.05. Results Mutational landscape of early-stage poorly differentiated LUAD Totally, 101 patients were enrolled in this study (the HG cohort). The median age of the cohort was 60 years (range: 34-80 years). Sixty-three patients (62.4%) were male, and thirty-eight patients were female (37.6%). At the end of the follow-up period (median follow-up: 43.2±20.1 months), 33 (32.7%) patients experienced recurrence. Detailed individual patient information is listed in Table S1 . Totally, WES, WMS and RNA-seq was successfully conducted on matched tumor-normal tissue pairs from 97, 98 and 82 patients in this cohort, respectively ( Figure 1A ). Among them, 78 patients had WES, RNA-seq and WMS data, simultaneously ( Figure 1B ). We first investigated the mutational landscape of the HG cohort ( Table S2 ), and the top 30 mutated genes are shown in Figure 1C . The most frequently mutated genes were EGFR (51%), TP53 (45%), MUC16 (28%), RYR2 (28%), TTN (26%) and KRAS (19%). These findings differed from a recent proteogenomic study of unselected Chinese LUAD patients[53]. In that cohort, the mutational frequencies of MUC16 , RYR2 and TTN were lower, and were not among the top 10 mutated genes. Somatic mutational signatures were also investigated in the HG cohort ( Table S3 ), and our analysis identified Signatures 1, 3, 4, 6, 7, 13, and 24 as significant contributors ( Figure 1D ). Some of these signatures have been reported to be associated with lung cancer. For instance, Signature 4 is associated with tobacco smoking and is likely due to DNA damage caused by tobacco smoke-derived mutagens[54]. The co-occurrence and mutual exclusivity patterns of the most mutated genes were further investigated ( Figure 1E ). Notably, EGFR mutations were mutually exclusive with mutations in many other genes, including TTN , KRAS , RYR1 and MUC16 , which was consistent with previous studies[55, 56]. As revealed by FACETS, significant CNVs were observed in the HG cohort ( Figure S1 ). Using GISTIC 2.0, remarkable focal level copy number gains were observed at 1q21.1, 7p11.2, 10q11.21, 14q13.3, 16q11.2, 19q12 and 20q13.33 ( Figure 1F ; Table S4 ). Additionally, losses were detected at 1p11.2, 2q21.2, 9p21.3, 16q11.2, and 16p13.2 ( Figure 1G ; Table S4 ). Recurrence-associated molecular events at genomic, epigenomic and transcriptomic levels Through multi-dimensional omics data, we explored the interactions between the genome, epigenome, and transcriptome ( Tables S5-7 ). We initially assessed the impact of CNVs on mRNA expression levels. As illustrated in Figure 2A , CNVs have the potential to influence gene expression either positively or negatively, in both cis and trans modes. A comprehensive analysis of 16,455 CNV-mRNA pairs revealed significant correlations: 588 pairs exhibited significant cis effects, whereas 6,569 CNVs influenced the expression of 4,075 mRNAs in trans (Spearman correlation analysis, FDR < 0.05). We estimated tumor purity in 97 patients using the FACETS algorithm for WES data and the ichorCNA R package for DNA methylation data ( Table S8 ). As depicted in Figure 2B , the tumor purity estimates derived from these two methods exhibited a strong correlation (Pearson’s correlation coefficient = 0.69, p < 0.001), indicating the robustness and reliability of these computational approaches for assessing tumor purity. We subsequently investigated the correlation between the epigenome and transcriptome using whole methylome sequencing (WMS) and RNA-seq data. As illustrated in Figure 2C , DNA methylation exhibited an inverse relationship with gene expression in the first three to four deciles, after which it remained low across higher expression deciles within the promoters and first exons of both tumor (Spearman correlation; R = -0.51, p < 0.001) and normal tissues (Spearman correlation; R = -0.46, p < 0.001). To identify the determinants of recurrence in this specific disease entity, we analyzed the clinicopathological and molecular differences between recurrent and non-recurrent cases in the HG cohort. Among the clinicopathological variables examined, visceral pleural invasion (VPI, p = 0.001) and pathological T stage (pT stage, p = 0.022) were significantly correlated with recurrence. Other factors such as age, smoking status, lymphovascular invasion (LVI), tumor size, receipt of adjuvant therapy, tumor mutational burden (TMB), and mutant-allele tumor heterogeneity (MATH) were not significantly associated with recurrence ( Figure 2D ; Figure S2A ; Table S8 ). We also compared the frequently mutated genes between recurrent and non-recurrent cases and found mutations of some frequently mutated genes, including EGFR , TP53 , RYR2 and MUC16 , were not correlated with recurrence ( Figure S2B ). Conversely, mutations in STK11 , XIRP2 , MXRA5 , and ZNF536 were more frequent in the recurrent cases. Previous studies have also indicated that mutations in EGFR could not determine the prognosis of LUAD[57], whereas mutations in STK11 and MXRA5 have been associated with poor prognosis[58, 59]. Additionally, analysis of the 10 canonical oncogene signaling pathways and DNA damage response (DDR) pathways in recurrent and non-recurrent cases revealed no significant differences between the two groups ( Figure S2C ; Table S9 ). Next, we investigated the potential association between mutational signatures and recurrence, and found no significant differences in mutational signatures between recurrent and non-recurrent cases ( Figure 2E ). Next, we investigated the differences in copy number variations (CNVs) and observed that recurrent tumors exhibited a higher frequency of CNVs, including both amplifications and deletions ( Figure 2F; Table S10 ). According to our analyses, several molecular features related to chromosomal instability, such as abnormal ploidy, FGA and aneuploidy, were enriched in recurrent tumors, suggesting that chromosomal instability indicates a worse prognosis. In addition to the intrinsic properties of tumor cells, immune cell populations are crucial prognostic determinants of LUAD. Using single-sample Gene Set Enrichment Analysis (ssGSEA), we compared the enrichment scores of 28 immune cell subsets between recurrent and non-recurrent cases ( Table S11 ). As shown in Figure 2G , recurrent tumors showed significantly lower enrichment scores for central memory CD4+ T cells and immature dendritic cells (p < 0.05). Previous studies have demonstrated that activated CD8+ T cells, effector memory CD8+ T cells and macrophages are associated with LUAD prognosis, which was not observed in the present study. Based on the MSigDB database(C2), gene set enrichment analysis (GSEA) of recurrent and non-recurrent cases demonstrated that recurrent cases were significantly enriched in pathways, including the adenylate cyclase activating pathway, tumorigenesis by ret c634r, aml methylation cluster 7 dn, DNA methylation, peptide hormone biosynthesis, and inflammatory response tgfb1 (nominal p-value < 0.05, Figure S2D ; Table S12 ). We investigated methylation differences between recurrent and non-recurrent cases using WMS data. Our analysis revealed a global pattern of hypomethylation in 1M-bp regions in recurrent cases compared to that in non-recurrent cases. Specifically, among the 10,569 differentially methylated regions (DMRs) identified, 95.7% exhibited global hypomethylation, and 77.8% of the CpG island (CGI) regions displayed similar trends ( Figure 2H ; Tables S13 , 14 ). Patient subtyping based on multiple molecular dimensions Next, we explored the molecular subtyping of early-stage poorly differentiated LUADs using different omics datasets. Initially, bulk RNA-seq data from 82 patients were analyzed to explore the transcriptomic subtypes of early-stage poorly differentiated LUADs. A total of 3,725 genes were differentially expressed between tumor and normal tissue (|(log2FC)| > 1, FDR < 0.05). Of these, the expression of 2,197 genes was upregulated and the expression of 1,528 genes was downregulated in tumor tissues ( Figure 3A ; Table S15 ). Through unsupervised consensus clustering of the top 3,500 differentially expressed genes (DEGs), we identified two distinct transcriptomic groups in the HG cohort: R1 (n = 38) and R2 (n = 44) ( Figure 3B ). To enable the application of these transcriptomic classes in future research and clinical settings, we developed a single-sample classifier based on the clustering results. This classifier was constructed by using an approach adopted in a recent study of non-muscle invasive bladder cancer[51], wherein a group label was assigned to the transcriptomic profile of a tumor based on its correlation with group-specific mean expression profiles (detailed in the Methods section). We applied this classifier to both the HG (n = 82) and TCGA cohorts (n = 394), and reclassified the tumor samples into R1 and R2 subtypes ( Table S8 , 16 ). The reclassification resulted in R1 (n = 56) and R2 (n = 26) in the HG cohort. The R1 subtype exhibited significantly poorer recurrence-free survival (RFS) in the HG cohort (p = 0.018; log-rank test, Figure 3C ) and worse disease-free survival (DFS) in TCGA cohort (p = 0.005; log-rank test, Figure 3I ) than the R2 subtype. The same trend was also observed in another independent validation cohort comprising more than 200 early-stage LUAD cases ( Figure S3A ). We subsequently analyzed the WMS data from 98 patients to explore the methylation subtyping of early-stage poorly differentiated LUAD. Figure 3D shows methylation levels of all gene promoter regions (TSS +/-2kb) between tumor and normal tissues ( Table S17 ). Unsupervised consensus clustering based on the top 1000 gene promoter regions was used to identify methylation subtypes, and two subtypes were identified: M1 (n = 67) and M2 (n = 31) ( Figure 3E ). The M2 subtype exhibited a significantly inferior RFS compared to the M1 subtype (p = 0.029; log-rank test; Figure 3F ). The emergence of cancer necessitates molecular alterations in both the transcriptome and epigenome. We further leveraged data from 78 patients who underwent both RNA-seq and WMS to develop integrative subtyping. As shown in Figure 3G , tumors classified under the R1 subtype were further divided into two subsets based on methylation profiling, termed C1 (26/53, R1&M2) and C2 (27/53, R1&M1) in integrative subtyping. Most tumors predicted to be of the R2 subtype were enriched in the M1 subtype (23/25, 92.00%), with only four (16%) recurring after surgery; thus, the R2 subtype was designated as the C3 subtype in integrative subtyping. Consequently, the 78 patients included in integrative subtyping were classified into three groups: C1 (n = 26), C2 (n = 27), and C3 (n = 25). Survival analysis revealed significant differences in prognosis among the three subtypes, with C1 subtype patients exhibiting the shortest recurrence-free survival (RFS) and C3 subtype patients demonstrating the longest RFS (p = 0.024; log-rank test; Figure 3H , Table S8 ). Univariate Cox regression analysis of the selected clinical features, as shown in Figure S3B , identified age, tumor size, STAS, VPI, and pT stage as significant prognostic factors (all p < 0.05). In multivariate Cox regression analysis, integrative subtyping remained an independent prognostic indicator for recurrence-free survival (RFS) (p < 0.05, Figure S3C ). We then performed a time-dependent receiver operating characteristic (ROC) analysis to assess the predictive accuracy for recurrence at 1, 3 and 5 years using a Cox model that incorporated integrative subtyping along with other prognostic clinical features. The predictive accuracy, indicated by the area under the curve (AUC), was approximately 0.7 when using integrative subtyping alone or in combination with another clinical feature ( Figure S3D ). Further analysis combining integrative subtyping with the five aforementioned clinical factors yielded AUC values of 0.74, 0.80, and 0.74 for the first, third, and fifth years, respectively ( Figure S3E ). These results suggest that the integrative model can more effectively predict the progression of early-stage poorly differentiated lung adenocarcinomas (LUADs), particularly at the 3-year mark. Notably, integrative subtyping emerged as the most significant predictor of RFS, accounting for 38.9% of the variance based on ANOVA of the Cox proportional hazards model ( Figure S3F ), which represents the largest Chi-square value relative to the total Chi-square values of all variables included in the model. Clinicopathological and molecular characteristics of the three integrative subtypes The three integrative subtypes identified using RNA-seq and WMS data exhibited different prognoses, suggesting that they are different disease subsets. Therefore, we further investigated their characteristics in depth. We initially compared the clinicopathological characteristics of the three integrative subtypes. As shown in Figure 4A , the C3 subtype included more males (p = 0.005, Fisher’s exact test) and smokers (p = 0.014, Fisher’s exact test) than the C2 subtype, but there was no significant difference between subtypes C1 and C2 and subtypes C1 and C3. In terms of pT stage, the C1 subtype enriched more patients with T2 or T3 stage disease than the C3 subtype (p = 0.013, Fisher’s exact test). Additionally, tumor size decreased in subtypes C1, C2, and C3, but the difference was not statistically significant. Other clinical characteristics, including age, STAS, VPI, LVI, pTNM stage, and adjuvant therapy, showed no significant differences among the three subtypes ( Figure 4A , Figure S4A ). We further explored whether the three integrative subtypes have significantly different molecular characteristics. As shown in Figure 4B , we observed increased TMB and MATH in the C1 subtype (all p < 0.05, Wilcoxon test), indicating that patients exhibiting the C1 subtype could harbor more mutations and intra-tumor heterogeneity (ITH). Parameters related to chromosome stability, such as aneuploidy, ploidy, FGA, MSI, CNI, and PA score, were also higher in the C1 subtype (all p < 0.05, Wilcoxon test; Figure 4B and Figure S4B ). Additionally, we observed that the C1 subtype was significantly more likely to exhibit HLA loss of heterozygosity (HLA-LOH) and a higher tumor neoantigen burden (TNB) than subtypes C2 and C3 (all p < 0.05, Fisher’s exact test). Increased HLA-LOH levels are usually accompanied by tumor immune evasion. Together, these results indicate that the C1 subtype has significant genomic instability, mutation burden, and ITH. To further investigate molecular heterogeneity among the three integrative subtypes, we compared the SNV and CNV profiles, focusing on the top 50 most frequently mutated genes and CNVs ( Table S2 , Table S6 ). While frequently mutated genes, such as EGFR , TP53 , KRAS , RYR2, and TTN , showed similar mutation frequencies across subtypes, several other genes showed significant differences ( Figure 4C ). For example, SEC16B and FSIP2 mutations were more common in the C1 subtype, whereas AHNAK2 mutations were enriched in the C3 subtype (p < 0.05, Fisher’s exact test). Additionally, STK11 mutations were more common in the C1 subtype than in the C3 subtype (p < 0.05, Fisher’s exact test). Next, we compared the CNV among these three subtypes and found that deletions of many pivotal cancer-associated genes were more common in the C1 subtype, including CDKN2A , MLLT3 , CD274 , JAK2 , NOTCH1 and NFIB ( Figure 4D ). We further compared 10 canonical oncogene signaling pathways and the DDR pathway between these three subtypes ( Table S18 ). We found that the C1 subtype showed a higher mutational rate for the Notch and BER pathways than the C2 subtype (p < 0.05), and the C2 subtype showed a higher mutational rate for the PI3K pathway than the C3 (p < 0.05, Figure S4C ). Based on the RNA-seq data, analysis of the enrichment scores for 28 distinct immune cell subsets among the three integrative subtypes ( Table S11 ) revealed significant disparities in the majority of immune cell types between these subtypes ( Figure 4E ). Notably, there was a progressive increase in the proportions of nearly all these cell types from subtype C1 to C2 and further to C3. For instance, the C3 subtype tumors exhibited the highest abundance of various immune effector cells, including activated CD8+ T cells, natural killer cells, and mast cells (all p < 0.05, Wilcoxon test), compared to the C1 and C2 subtype tumors. Furthermore, our analysis revealed that the C3 subtype demonstrated elevated levels of CD274 (PD-L1) expression and a higher Gene Expression Profiling (GEP) score ( Figure 4F , G ; Table S19 ). Notably, both the immune and stromal scores, as estimated by the ESTIMATE algorithm, were significantly higher in subtype C3 than subtypes C1 and C2 ( Figure 4H , I ). These findings suggest that the C3 subtype is characterized by an immunologically active tumor microenvironment (TME). Additionally, an analysis of functional states across 25 cancer types revealed significant enrichment of cell cycle and DNA repair functions in subtype C1, whereas subtype C3 was significantly enriched for quiescence and stemness-related functions ( Figure S4D ; Table S20 ). The exploration of representative differentially expressed genes in the three integrative subtypes Next, we identified DEGs that were stably expressed across the three integrative subtypes ( Figure S5A ; Table S21 ). To evaluate the prognostic significance of highly and stably expressed genes in the C1 subtype, we calculated the p-values and hazard ratios (HRs) for each gene across percentiles from 0.1 to 0.9 in both the HG and TCGA cohorts ( Figure S5B , C ; Table S22 ). Among these genes, GINS complex subunit 1 ( GINS1) and Carnitine palmitoyltransferase 1C ( CPT1C) were of particular interest, because their high expression was significantly associated with poor prognosis in both cohorts ( Figure 5A , B ). Previous studies have highlighted their pivotal roles in promoting tumor progression[60, 61]. We utilized the surv_cutpoint function from the survminer R package to determine the optimal cutoff for each gene and found that higher expression levels of both genes were associated with shorter recurrence-free survival (RFS) in the HG cohort (GINS1: HR = 2.23, p = 0.050; CPT1C: HR = 4.68, p = 0.002, Figure 5C , D ). To further explore the biological functions of GINS1 and CPT1C in LUAD progression, we knocked down the expression of endogenous GINS1 and CPT1C in SPCA-1 cells using lentiviruses encoding shRNAs. We found that the downregulation of both GINS1 and CPT1C markedly attenuated cell migration and invasion in vitro ( Figure 5E-H and Figure S5D, E ). In addition to inhibiting pro-metastatic function, the knockdown of GINS1 and CPT1C strikingly restrained cell proliferation, as demonstrated by the reduced proliferative ability in CCK-8 and colony formation assays ( Figure 5I-L and Figure S5F , G ). We further performed the xenograft tumor model to consolidate the biological functions of GINS1 and CPT1C in vivo . As shown in Figure 5M and N , compared to the control group, the downregulation of GINS1 and CPT1C markedly attenuated tumor progression in vivo, accompanied by a marked reduction in tumor size and weight. Potential prognostic and predictive significance of the integrative subtyping In current clinical practice, adjuvant chemotherapy remains a cornerstone in the treatment of LUAD; however, its use in early-stage LUAD remains controversial. In the HG cohort, 37 patients received adjuvant chemotherapy after surgery. These patients tended to exhibit a worse prognosis than those who did not receive postoperative treatment, although the difference showed only a marginal statistical significance (p = 0.059, Figure 6A ). We explored the potential of integrative subtyping as a possible indicator for assessing adjuvant chemotherapy efficacy. As shown in Figure 6B-D , the survival analysis suggested limited benefit from adjuvant chemotherapy in both C1 and C3 subtypes, with the C3 subtype exhibiting a particularly unfavorable outcome (p = 0.038). In contrast, a trend toward improved prognosis was observed in patients classified as the C2 subtype who received adjuvant chemotherapy, although this difference did not reach statistical significance (p = 0.301). These preliminary findings indicated that integrative subtyping might offer some clinical utility in evaluating heterogeneous responses to adjuvant chemotherapy in early-stage poorly differentiated LUAD, warranting further investigation to assess its potential value in therapeutic decision-making. In summary, each integrative subtype exhibits unique clinical features, molecular characteristics, and TME, as shown in Figure 6E . The C1 subtype is characterized by high genomic instability, low immune infiltration and high tumor heterogeneity. The C2 subtype displays low tumor heterogeneity and HLA-LOH, fewer neoantigens and moderate immune activation, and it may benefit from adjuvant chemotherapy. The C3 subtype exhibits substantial immune infiltration, high GEP score, high TNB, and low frequency of HLA-LOH, suggesting a potential benefit from immunotherapy. Discussion Early-stage poorly differentiated LUADs exhibit a poor prognosis, with approximately 30% of them experiencing recurrence[3]. Precise management of this population is a challenge in clinical practice. Adding molecular dimensional features and stratification to the new grading system could render the prognostic evaluation more precise. In this study, we performed integrative multi-omics analysis, including genomics, epigenomics and transcriptomics, in an early-stage poorly differentiated lung adenocarcinoma cohort (n=101). Our analysis of the multi-platform dataset revealed comprehensive molecular characteristics of this special disease entity. Furthermore, three molecular subtypes were identified based on the transcriptomic and methylation data. These integrative subtypes demonstrated distinct clinicopathological and molecular features with prognostic significance, enabling further precise stratification of early-stage poorly differentiated LUADs. Consistent with previous genomic studies in the Chinese population[53, 62], EGFR (51%) and TP53 (45%) were also the predominant mutated genes in early-stage poorly differentiated LUAD. However, the mutational frequencies of MUC 16 (28%), RYR2 (28%) and TTN (26%) were significantly higher in early-stage poorly differentiated LUAD than in the unselected Chinese LUAD patients. MUC16 mutations have been found in several solid tumors, including melanoma and breast cancer[63-65]. Several recent studies have revealed that MUC16 mutations are associated with TMB in solid tumors[66]. A recent study reported that RYR2 was one of the most mutational genes which were almost always shared by primary lung cancers and brain metastasis lesions[67]. Previous studies have revealed that the mutation of RYR2 was a significant biomarker associated with high TMB in LUAD[68]. As another gene with high-frequency mutations, TTN mutations were associated with the therapeutic efficacy of immune checkpoint blockade in advanced non-small cell lung cancer[69, 70]. Moreover, TTN mutations may function as crucial intra-tumoral drivers for micropapillary/solid components, as shown in another LUAD cohort[71]. In addition to oncogenic gene mutations, remarkable CNVs were also found in early-stage poorly differentiated LUADs. These results indicated that poorly differentiated LUADs had relatively special mutational landscape and chromosome structure variations, which may lead to the transition of growth patterns during the development of LUAD. Accurate risk classification plays a pivotal role in guiding treatment strategies and enhancing patient outcomes. While the current grading system categorizes poorly differentiated LUADs as a homogeneous group, our study revealed three distinct molecular subtypes through transcriptomic and methylation profiling. These subtypes exhibit unique molecular signatures that correlate with specific clinical, pathological, and prognostic characteristics. The C1 subtype displayed the highest levels of TMB, MATH, aneuploidy and HLA-LOH levels, coupled with relatively reduced immune cell infiltration. These factors likely contribute to increased genomic instability and impaired anti-tumor immune responses and lead to its poor prognosis. Multiple studies have demonstrated that high TMB is associated with a poor prognosis of LUADs[72]. Aneuploidy is a hall mark of cancer, and patients with high aneuploidy often show a poor prognosis[73]. Lower PD-L1 expression further implies that these tumors might rely less on the PD-L1/PD-1 pathway for immune escape, instead of utilizing alternative mechanisms such as HLA-LOH. In contrast to the C1 subtype, the C3 subtype exhibited lower genomic instability, as evidenced by reduced TMB, MATH and FGA. Notably, this subtype also demonstrated the highest level of immune cell infiltration, which may contribute to its more favorable prognosis. Furthermore, the elevated immune infiltration and higher GEP scores suggest that this subtype may be particularly responsive to immunotherapy, making it a promising candidate for such treatment strategies. For the C2 subtype, its molecular characteristics was intermediate and featured with low heterogeneity, low LOH and less neo antigens. In this study, we found the C1 and C3 subtypes did not benefit from adjuvant chemotherapy, especially for the later one. However, patients with C2 subtype receiving adjuvant chemotherapy tended to exhibit better prognosis, implying adjuvant chemotherapy was a potential treatment modality for the C2 subtype. However, these are preliminary findings that require further validation in an additional large-scale cohort. We will continue to accumulate more data on this aspect and validate the results through both retrospective and prospective cohort studies. Currently, several molecular classification systems exist for unselected LUAD patients, yet they remain largely disconnected from pathological diagnostics. The integration of classical morphological information and molecular data remains a severe challenge for clinical utility. The integrative subtypes identified in this study offer a practicable approach to further enhance prognostic evaluation in poorly differentiated LUAD, building upon the current pathological diagnostic framework. To identify early-stage LUAD with a high recurrence risk, we recommend first using the histological grading system to select poorly differentiated cases, then performing molecular subtyping exclusively on these tumors. This stratified approach will facilitate more precise prognostic assessment and guide adjuvant therapy decisions. Given the pressing demand for innovative treatment methods, the DEGs identified among the three integrative subtypes were selected for further studies to illustrate their potential as new therapeutic targets. Our study found that GINS1 and CPTIC exhibited high expression in the C1 subtype and were associated with a higher hazard ratio of recurrence in both the HG and TCGA cohorts, acting as potential therapeutic targets. GINS1 , part of helicase at DNA replication forks, is involved in DNA replication initiation and elongation[74, 75]. Previous studies have shown that GINS1 plays essential roles in tumorigenesis and progression, including NSCLC[60]. In addition to DNA replication, GINS1 is associated with tumor metastasis, sorafenib resistance, doxorubicin resistance, and B-cell proliferation, suggesting essential role of GINS1 in tumor progression, drug resistance and immune microenvironment[75, 76]. CPT1C , a rate-limiting enzyme in fatty acid oxidation, fuels tumor growth under metabolic stress and acts as a prognostic marker in many tumors[77, 78]. Several studies have reported that CPT1C , a regulator of lipid metabolic reprogramming, is pivotal for the proliferation and metastasis of various tumors[61, 79]. Moreover, dysregulation of CPT1C can lead to plasma membrane remodeling and anthracycline resistance in breast cancer[80]. Collectively, all the evidence showed that both GINS1 and CPT1C are pivotal for tumor progression and are associated with drug resistance, which is consistent with the pro-proliferative and pro-metastatic functions of GINS1 / CPT1C in our studies and indicates potential drug targets to enhance adjuvant chemotherapy in LUADs. Moreover, our studies found that downregulated genes in both GINS1-high expression and C1 subtype samples were significantly enriched in pathways including \"Neutrophil degranulation\", \"Signaling by Interleukins\", \"Arachidonic acid metabolism\" and \"PD-1 signaling\" ( Figure S6A, B ; Table S23 ). These findings suggest that GINS1 may promote tumor progression by suppressing T cell and neutrophil functions, thereby inducing an immunosuppressive microenvironment and contributing to the aggressive phenotype of the C1 subtype[81-83]. Notably, arachidonic acid metabolism, a critical metabolic pathway in tumor progression, has been shown to enhance antitumor immune responses by activating CD8+ T cells in colorectal cancer[84]. Therefore, subsequent studies on elucidating how GINS1 modulates arachidonic acid metabolism to influence C1 subtype transformation, may provide a stronger theoretical foundation for developing therapeutic strategies targeting arachidonic acid metabolism inhibitors. Additionally, we observed that downregulated genes in both CPT1C-high and C1 subtype samples were significantly enriched in pathways such as \"Chemokine receptors bind chemokines\", \"Neutrophil degranulation\", \"Phosphorylation of CD3 and TCR zeta chains\", \"PD-1 signaling\" and \"Biosynthesis of specialized proresolving mediators (SPMs)\" ( Figure S6A, C ; Table S23 ). These results suggest that CPT1C may similarly promote tumor progression by creating an immunosuppressive microenvironment that contributes to the aggressive phenotype of the C1 subtype[82, 83, 85, 86]. Of particular interest, SPMs, a class of bioactive lipid mediators derived from polyunsaturated fatty acids (PUFAs), play crucial roles in actively resolving inflammation and inducing surrounding immune-infiltrating cells such as tumor-associated macrophages, representing a promising direction for anticancer therapy[87, 88]. While CPT1C is known to participate in fatty acid oxidation[77], its specific impact on SPMs and the function of SPMs in early-stage poorly differentiated lung adenocarcinoma remain unclear. Therefore, future studies may focus on elucidating the mechanism by which CPT1C regulates SPMs, with the goal of developing novel SPM-based therapeutic strategies. While this study provides some valuable insights, several limitations should be acknowledged. First, we actually made substantial efforts to screen early-stage LUAD from our institution between 2012 and 2017, and screened out 101 poorly differentiated cases. Although this is the largest multi-omics cohort of early-stage poorly differentiated LUAD with long-term follow-up to date, the sample size remains limited. This constrained statistical power may explain why certain clinically apparent trends—particularly regarding differential responses to adjuvant chemotherapy across the three subtypes—failed to reach statistical significance. Consequently, these preliminary findings warrant validation through larger-scale prospective studies. Second, owing to the scarcity of public multi-omics data encompassing transcriptomic and methylation profiles for poorly differentiated LUAD, we could not externally validate our integrative molecular subtyping system or its prognostic value. Future multi-center studies focusing on early-stage poorly differentiated LUAD cohorts will be essential to verify the clinical relevance and generalizability of our classification. Third, we performed comprehensive and in-depth analyses of the molecular characteristics of poorly differentiated LUAD. However, we did not profile the proteome or metabolome, which could reflect the biological characteristics of this special disease entity more directly. Therefore, we will perform proteomic and metabolomic analyses on poorly differentiated LUAD in the future to establish a more comprehensive and reliable subtyping. In summary, through integrated analyses, we delineate a genomic, epigenomic and transcriptomic landscape and uncover three molecular subtypes with distinct prognoses of early-stage poorly differentiated LUAD. Our study not only provides important insight into the relatively specific biology of this subset of LUAD but also reveals its molecular heterogeneity, which can potentially facilitate their precise treatment and postoperative monitoring. Declarations Acknowledgement We thank all the participants and family members for participating in this study. Authors’ contributions BL, WT, XTZ, LDX, SY and NW contributed to the conception and design of the study. BL, WT, XTZ, YRL, LDX, XY, ELZ and YSH acquired and analyzed the data. MH, YGZ, XRC, YQW and TYG provided patient materials and scientific discussion. BL, WT, XTZ and NW drafted the initial version of the Stage 1 protocol. All authors contributed to revisions of the Stage 1 protocol, preparation of the Stage 2 manuscript, and approved the submitted version. Funding This study was supported by the Capital’s funds for health improvement and research (2024-1-1023), the National Natural Science Foundation of China (No. 82303583, 82373082), the Science Foundation of Peking University Cancer Hospital (2022-11), the National Key R&D Program of China (No. 2022YFC2406804), the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0501702) and the National Ten-thousand Talent Program. Data availability The raw data can be obtained from the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number PRJCA026561 (https://ngdc.cncb.ac.cn/gsa). To protect patient privacy, data access can be obtained through a request to the data access committee. Access to the data will be restricted to noncommercial entities. Institutional review board statement This study was approved by the Ethics Committee of Peking University Cancer Hospital & Institute (Institutional Review Board No. 2024KT65). Consent for publication Not applicable. Conflict of Interest The authors declare no conflict of interest. References Moreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, Cooper WA, Yoshida A, Bubendorf L, Papotti M, et al: A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol 2020, 15: 1599-1610. Ruan Y, Cao W, Han J, Yang A, Xu J, Zhang T: Prognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis. World J Surg Oncol 2024, 22: 302. Xu L, Su H, Hou L, Wang F, Xie H, She Y, Gao J, Zhao S, Dai C, Xie D, et al: The IASLC Proposed Grading System Accurately Predicts Prognosis and Mediastinal Nodal Metastasis in Patients With Clinical Stage I Lung Adenocarcinoma. Am J Surg Pathol 2022, 46: 1633-1641. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014, 511: 543-550. Chen J, Yang H, Teo ASM, Amer LB, Sherbaf FG, Tan CQ, Alvarez JJS, Lu B, Lim JQ, Takano A, et al: Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet 2020, 52: 177-186. Fukui T, Shaykhiev R, Agosto-Perez F, Mezey JG, Downey RJ, Travis WD, Crystal RG: Lung adenocarcinoma subtypes based on expression of human airway basal cell genes. Eur Respir J 2013, 42: 1332-1344. Burdett NL, Willis MO, Alsop K, Hunt AL, Pandey A, Hamilton PT, Abulez T, Liu X, Hoang T, Craig S, et al: Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer. Nat Genet 2023, 55: 437-450. Davidson NR, Barnard ME, Hippen AA, Campbell A, Johnson CE, Way GP, Dalley BK, Berchuck A, Salas LA, Peres LC, et al: Molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms. bioRxiv 2023. Linazi G, Maimaiti A, Abulaiti Z, Shi H, Zhou Z, Aisa MY, Kang Y, Abulimiti A, Dilimulati X, Zhang T, et al: Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment. Heliyon 2024, 10: e36989. Deng C, Zheng Q, Zhang Y, Jin Y, Shen X, Nie X, Fu F, Ma X, Ma Z, Wen Z, et al: Validation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association With Common Driver Mutations. J Thorac Oncol 2021, 16: 1684-1693. Caso R, Sanchez-Vega F, Tan KS, Mastrogiacomo B, Zhou J, Jones GD, Nguyen B, Schultz N, Connolly JG, Brandt WS, et al: The Underlying Tumor Genomics of Predominant Histologic Subtypes in Lung Adenocarcinoma. J Thorac Oncol 2020, 15: 1844-1856. Dong ZY, Zhang C, Li YF, Su J, Xie Z, Liu SY, Yan LX, Chen ZH, Yang XN, Lin JT, et al: Genetic and Immune Profiles of Solid Predominant Lung Adenocarcinoma Reveal Potential Immunotherapeutic Strategies. J Thorac Oncol 2018, 13: 85-96. Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT: The Eighth Edition Lung Cancer Stage Classification. Chest 2017, 151: 193-203. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, et al: The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol 2015, 10: 1243-1260. Okayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S, et al: Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res 2012, 72: 100-111. Bolger AM, Lohse M, Usadel B: Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30: 2114-2120. Aldana R, Freed D: Data Processing and Germline Variant Calling with the Sentieon Pipeline. Methods Mol Biol 2022, 2493: 1-19. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G: Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 2013, 31: 213-219. Wang K, Li M, Hakonarson H: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010, 38: e164. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP: Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018, 28: 1747-1756. Mroz EA, Tward AD, Hammon RJ, Ren Y, Rocco JW: Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med 2015, 12: e1001786. Arora A, Shen R, Seshan VE: FACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing. Methods Mol Biol 2022, 2493: 89-105. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G: GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol 2011, 12: R41. Taylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC, Schumacher SE, Wang C, Hu H, Liu J, et al: Genomic and Functional Approaches to Understanding Cancer Aneuploidy. Cancer Cell 2018, 33: 676-689.e673. Talevich E, Shain AH, Botton T, Bastian BC: CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing. PLoS Comput Biol 2016, 12: e1004873. Weiss GJ, Beck J, Braun DP, Bornemann-Kolatzki K, Barilla H, Cubello R, Quan W, Jr., Sangal A, Khemka V, Waypa J, et al: Tumor Cell-Free DNA Copy Number Instability Predicts Therapeutic Response to Immunotherapy. Clin Cancer Res 2017, 23: 5074-5081. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25: 2078-2079. Kawaguchi S, Higasa K, Shimizu M, Yamada R, Matsuda F: HLA-HD: An accurate HLA typing algorithm for next-generation sequencing data. Hum Mutat 2017, 38: 788-797. McGranahan N, Rosenthal R, Hiley CT, Rowan AJ, Watkins TBK, Wilson GA, Birkbak NJ, Veeriah S, Van Loo P, Herrero J, Swanton C: Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution. Cell 2017, 171: 1259-1271.e1211. Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M: NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol 2017, 199: 3360-3368. Chen K, Yang A, Carbone DP, Kanu N, Liu K, Wang R, Nie Y, Shen H, Bai J, Wu L, et al: Spatiotemporal genomic analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers. Cell Rep 2022, 40: 111047. Rosenthal R, McGranahan N, Herrero J, Taylor BS, Swanton C: DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol 2016, 17: 31. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL: Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 2019, 37: 907-915. Liao Y, Smyth GK, Shi W: featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30: 923-930. Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15: 550. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102: 15545-15550. Scire J, Huisman JS, Grosu A, Angst DC, Lison A, Li J, Maathuis MH, Bonhoeffer S, Stadler T: estimateR: an R package to estimate and monitor the effective reproductive number. BMC Bioinformatics 2023, 24: 310. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, et al: IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest 2017, 127: 2930-2940. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017, 18: 248-262. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L, Luo T, Yan H, Long Z, et al: CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res 2019, 47: D900-d908. Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013, 14: 7. Krueger F, Andrews SR: Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 2011, 27: 1571-1572. Bie F, Wang Z, Li Y, Guo W, Hong Y, Han T, Lv F, Yang S, Li S, Li X, et al: Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization. Nat Commun 2023, 14: 6042. Adalsteinsson VA, Ha G, Freeman SS, Choudhury AD, Stover DG, Parsons HA, Gydush G, Reed SC, Rotem D, Rhoades J, et al: Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 2017, 8: 1324. Leary RJ, Sausen M, Kinde I, Papadopoulos N, Carpten JD, Craig D, O'Shaughnessy J, Kinzler KW, Parmigiani G, Vogelstein B, et al: Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci Transl Med 2012, 4: 162ra154. Feng H, Conneely KN, Wu H: A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res 2014, 42: e69. Yu G, Wang LG, He QY: ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 2015, 31: 2382-2383. Anastasiadi D, Esteve-Codina A, Piferrer F: Consistent inverse correlation between DNA methylation of the first intron and gene expression across tissues and species. Epigenetics Chromatin 2018, 11: 37. Xu T, Le TD, Liu L, Su N, Wang R, Sun B, Colaprico A, Bontempi G, Li J: CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization. Bioinformatics 2017, 33: 3131-3133. Wilkerson MD, Hayes DN: ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010, 26: 1572-1573. Lindskrog SV, Prip F, Lamy P, Taber A, Groeneveld CS, Birkenkamp-Demtröder K, Jensen JB, Strandgaard T, Nordentoft I, Christensen E, et al: An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nat Commun 2021, 12: 2301. Ando M, Saito Y, Xu G, Bui NQ, Medetgul-Ernar K, Pu M, Fisch K, Ren S, Sakai A, Fukusumi T, et al: Chromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers. Nat Commun 2019, 10: 2188. Xu JY, Zhang C, Wang X, Zhai L, Ma Y, Mao Y, Qian K, Sun C, Liu Z, Jiang S, et al: Integrative Proteomic Characterization of Human Lung Adenocarcinoma. Cell 2020, 182: 245-261.e217. Hudson KM, Klimczak LJ, Sterling JF, Burkholder AB, Kazanov MD, Saini N, Mieczkowski PA, Gordenin DA: Glycidamide-induced hypermutation in yeast single-stranded DNA reveals a ubiquitous clock-like mutational motif in humans. Nucleic Acids Res 2023, 51: 9075-9100. Kim Y, Lee B, Shim JH, Lee SH, Park WY, Choi YL, Sun JM, Ahn JS, Ahn MJ, Park K: Concurrent Genetic Alterations Predict the Progression to Target Therapy in EGFR-Mutated Advanced NSCLC. J Thorac Oncol 2019, 14: 193-202. Duan J, Xu J, Wang Z, Bai H, Cheng Y, An T, Gao H, Wang K, Zhou Q, Hu Y, et al: Refined Stratification Based on Baseline Concomitant Mutations and Longitudinal Circulating Tumor DNA Monitoring in Advanced EGFR-Mutant Lung Adenocarcinoma Under Gefitinib Treatment. J Thorac Oncol 2020, 15: 1857-1870. Takamochi K, Oh S, Matsunaga T, Suzuki K: Prognostic impacts of EGFR mutation status and subtype in patients with surgically resected lung adenocarcinoma. J Thorac Cardiovasc Surg 2017, 154: 1768-1774.e1761. Zheng J, Deng Y, Huang B, Chen X: Prognostic implications of STK11 with different mutation status and its relationship with tumor-infiltrating immune cells in non-small cell lung cancer. Front Immunol 2024, 15: 1387896. He Y, Chen X, Liu H, Xiao H, Kwapong WR, Mei J: Matrix-remodeling associated 5 as a novel tissue biomarker predicts poor prognosis in non-small cell lung cancers. Cancer Biomark 2015, 15: 645-651. Li M, Shi M, Hu C, Chen B, Li S: MALAT1 modulated FOXP3 ubiquitination then affected GINS1 transcription and drived NSCLC proliferation. Oncogene 2021, 40: 3870-3884. Chen Y, Zhou Y, Han F, Zhao Y, Tu M, Wang Y, Huang C, Fan S, Chen P, Yao X, et al: A novel miR-1291-ERRα-CPT1C axis modulates tumor cell proliferation, metabolism and tumorigenesis. Theranostics 2020, 10: 7193-7210. Chen YJ, Roumeliotis TI, Chang YH, Chen CT, Han CL, Lin MH, Chen HW, Chang GC, Chang YL, Wu CT, et al: Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression. Cell 2020, 182: 226-244.e217. Yu Y, Lin D, Li A, Chen Y, Ou Q, Hu H, Yao H: Association of Immune Checkpoint Inhibitor Therapy With Survival in Patients With Cancers With MUC16 Variants. JAMA Netw Open 2020, 3: e205837. Li J, Liu B, Ye Q, Xiao X, Yan S, Guan W, He L, Wang C, Yu Z, Tai Z, et al: Comprehensive genomic analysis of primary malignant melanoma of the esophagus reveals similar genetic patterns compared with epithelium-associated melanomas. Mod Pathol 2022, 35: 1596-1608. Yin G, Liu L, Yu T, Yu L, Feng M, Zhou C, Wang X, Teng G, Ma Z, Zhou W, et al: Genomic and transcriptomic analysis of breast cancer identifies novel signatures associated with response to neoadjuvant chemotherapy. Genome Med 2024, 16: 11. Wang X, Yu X, Krauthammer M, Hugo W, Duan C, Kanetsky PA, Teer JK, Thompson ZJ, Kalos D, Tsai KY, et al: The Association of MUC16 Mutation with Tumor Mutation Burden and Its Prognostic Implications in Cutaneous Melanoma. Cancer Epidemiol Biomarkers Prev 2020, 29: 1792-1799. Duan H, Ren J, Wei S, Yang Z, Li C, Wang Z, Li M, Wei Z, Liu Y, Wang X, et al: Integrated analyses of multi-omic data derived from paired primary lung cancer and brain metastasis reveal the metabolic vulnerability as a novel therapeutic target. Genome Med 2024, 16: 138. Wang C, Liang H, Lin C, Li F, Xie G, Qiao S, Shi X, Deng J, Zhao X, Wu K, Zhang X: Molecular Subtyping and Prognostic Assessment Based on Tumor Mutation Burden in Patients with Lung Adenocarcinomas. Int J Mol Sci 2019, 20 . Su C, Wang X, Zhou J, Zhao J, Zhou F, Zhao G, Xu X, Zou X, Zhu B, Jia Q: Titin mutation in circulatory tumor DNA is associated with efficacy to immune checkpoint blockade in advanced non-small cell lung cancer. Transl Lung Cancer Res 2021, 10: 1256-1265. Jia Q, Wang J, He N, He J, Zhu B: Titin mutation associated with responsiveness to checkpoint blockades in solid tumors. JCI Insight 2019, 4 . Li J, Xiong S, He P, Liang P, Li C, Zhong R, Cai X, Xie Z, Liu J, Cheng B, et al: Spatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma. Neoplasia 2024, 54: 101013. Li L, Li J: Correlation of tumor mutational burden with prognosis and immune infiltration in lung adenocarcinoma. Front Oncol 2023, 13: 1128785. Lakhani AA, Thompson SL, Sheltzer JM: Aneuploidy in human cancer: new tools and perspectives. Trends Genet 2023, 39: 968-980. Ilves I, Petojevic T, Pesavento JJ, Botchan MR: Activation of the MCM2-7 helicase by association with Cdc45 and GINS proteins. Mol Cell 2010, 37: 247-258. Kingsley G, Skagia A, Passaretti P, Fernandez-Cuesta C, Reynolds-Winczura A, Koscielniak K, Gambus A: DONSON facilitates Cdc45 and GINS chromatin association and is essential for DNA replication initiation. Nucleic Acids Res 2023, 51: 9748-9763. Liang J, Yao N, Deng B, Li J, Jiang Y, Liu T, Hu Y, Cao M, Hong J: GINS1 promotes ZEB1-mediated epithelial-mesenchymal transition and tumor metastasis via β-catenin signaling in hepatocellular carcinoma. J Cell Physiol 2024, 239: e31237. Fadó R, Zagmutt S, Herrero L, Muley H, Rodríguez-Rodríguez R, Bi H, Serra D, Casals N: To be or not to be a fat burner, that is the question for cpt1c in cancer cells. Cell Death Dis 2023, 14: 57. Li J, Zheng W, Wu J, Zhang J, Lv B, Li W, Liu J, Zhang X, Huang T, Luo Z: CPT1C-mediated fatty acid oxidation facilitates colorectal cancer cell proliferation and metastasis. Acta Biochim Biophys Sin (Shanghai) 2023, 55: 1301-1309. Zhao H, Cheng X, Yan L, Mi F, Wang W, Hu Y, Liu X, Fan Y, Min Q, Wang Y, et al: APC/C-regulated CPT1C promotes tumor progression by upregulating the energy supply and accelerating the G1/S transition. Cell Commun Signal 2024, 22: 283. Muley H, Valencia K, Casas J, Moreno B, Botella L, Lecanda F, Fadó R, Casals N: Cpt1c Downregulation Causes Plasma Membrane Remodelling and Anthracycline Resistance in Breast Cancer. Int J Mol Sci 2023, 24 . Mollinedo F: Neutrophil Degranulation, Plasticity, and Cancer Metastasis. Trends Immunol 2019, 40: 228-242. Shi RY, Zhou N, Xuan L, Jiang ZH, Xia J, Zhu JM, Chen KM, Zhou GL, Yu GP, Zhang J, et al: Trafficking circuit of CD8(+) T cells between the intestine and bone marrow governs antitumour immunity. Nat Cell Biol 2024, 26: 1346-1358. Lin X, Kang K, Chen P, Zeng Z, Li G, Xiong W, Yi M, Xiang B: Regulatory mechanisms of PD-1/PD-L1 in cancers. Mol Cancer 2024, 23: 108. Cui L, Liu R, Han S, Zhang C, Wang B, Ruan Y, Yu X, Li Y, Yao Y, Guan X, et al: Targeting Arachidonic Acid Metabolism Enhances Immunotherapy Efficacy in ARID1A-Deficient Colorectal Cancer. Cancer Res 2025, 85: 925-941. Wu W, Zhou Q, Masubuchi T, Shi X, Li H, Xu X, Huang M, Meng L, He X, Zhu H, et al: Multiple Signaling Roles of CD3ε and Its Application in CAR-T Cell Therapy. Cell 2020, 182: 855-871.e823. Velasco Cárdenas RM, Brandl SM, Meléndez AV, Schlaak AE, Buschky A, Peters T, Beier F, Serrels B, Taromi S, Raute K, et al: Harnessing CD3 diversity to optimize CAR T cells. Nat Immunol 2023, 24: 2135-2149. Lavy M, Gauttier V, Poirier N, Barillé-Nion S, Blanquart C: Specialized Pro-Resolving Mediators Mitigate Cancer-Related Inflammation: Role of Tumor-Associated Macrophages and Therapeutic Opportunities. Front Immunol 2021, 12: 702785. Serhan CN: Pro-resolving lipid mediators are leads for resolution physiology. Nature 2014, 510: 92-101. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigures.pdf Supplementarytables.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 May, 2025 Read the published version in Molecular Cancer → Version 1 posted Editorial decision: Accepted 12 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 05 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6082622\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":439840402,\"identity\":\"3ecad17e-a0b4-46d3-b51d-4001e4d1a6ff\",\"order_by\":0,\"name\":\"Bing Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bing\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":439840404,\"identity\":\"67911763-b0b2-4e54-9ef8-20869609e6ab\",\"order_by\":1,\"name\":\"Wei Tao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wei\",\"middleName\":\"\",\"lastName\":\"Tao\",\"suffix\":\"\"},{\"id\":439840406,\"identity\":\"2d415812-fa39-477c-9503-39bdf6686e25\",\"order_by\":2,\"name\":\"Xuantong Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xuantong\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":439840407,\"identity\":\"2e30291a-3f40-4582-bc05-f8fed636568f\",\"order_by\":3,\"name\":\"Li-Di Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Li-Di\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":439840408,\"identity\":\"a92c5ce5-8e69-4d95-90ad-bc3cd20fdcd2\",\"order_by\":4,\"name\":\"Yanrui Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yanrui\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"},{\"id\":439840409,\"identity\":\"0abb581d-591b-430b-b39a-063103813363\",\"order_by\":5,\"name\":\"Xin Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xin\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":439840410,\"identity\":\"1aa6445a-e048-4445-83c6-6eb6fe7a9aca\",\"order_by\":6,\"name\":\"Qingjie Min\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingjie\",\"middleName\":\"\",\"lastName\":\"Min\",\"suffix\":\"\"},{\"id\":439840411,\"identity\":\"e5674f29-25ae-4d39-a62f-a2775c85ed2f\",\"order_by\":7,\"name\":\"Miao Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Miao\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":439840412,\"identity\":\"d645fa2a-6b41-4660-be46-b45b328f2fec\",\"order_by\":8,\"name\":\"Yuge Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuge\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":439840413,\"identity\":\"13c1fb1c-8a48-4abf-bc1f-1469d3b775f8\",\"order_by\":9,\"name\":\"Xinrun Cui\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinrun\",\"middleName\":\"\",\"lastName\":\"Cui\",\"suffix\":\"\"},{\"id\":439840414,\"identity\":\"a5382bcf-eb7c-4fea-a4bf-6b6eb362195e\",\"order_by\":10,\"name\":\"Yaqi Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yaqi\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":439840415,\"identity\":\"48e503e6-771e-4915-a56b-3289cc906d61\",\"order_by\":11,\"name\":\"Tongyang Gong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tongyang\",\"middleName\":\"\",\"lastName\":\"Gong\",\"suffix\":\"\"},{\"id\":439840416,\"identity\":\"d4a78bfb-1210-4d0e-9763-12738bfbe10a\",\"order_by\":12,\"name\":\"Enli Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Enli\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":439840417,\"identity\":\"0711594c-5ae9-4e43-a10c-0c8a3abcb6f0\",\"order_by\":13,\"name\":\"Yu S. Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yu\",\"middleName\":\"S.\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":439840418,\"identity\":\"95e62b53-ad62-45c3-9890-d2b5c18352e6\",\"order_by\":14,\"name\":\"Weizhi Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Genecast Biotechnology Co, Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weizhi\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":439840419,\"identity\":\"bdbcce07-a0e7-4ae6-a0f0-7f81fa4eae94\",\"order_by\":15,\"name\":\"Shi Yan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shi\",\"middleName\":\"\",\"lastName\":\"Yan\",\"suffix\":\"\"},{\"id\":439840420,\"identity\":\"5d626336-d39f-4820-a96c-48001694a40d\",\"order_by\":16,\"name\":\"Nan Wu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYNCCigPMYJqHeC1nSNbC2HaAgXgt5uxnj0nzzrvDrjsjgfHB2zYGeXNCWix78tKkebc9Yza7kcBsOLeNwXBnAwEtBgdyzG7zbjsM0sImzdvGkGBwgJCW82+AWuaAtbD/Jk7LDZAtDRBbmInU8sb855xjQL+cedgsOeechOEGwg7LMTZ4U3Mn2ex48sEPb8ps5AnaAgPJwNhpANISRKoHAjvilY6CUTAKRsGIAwAk/UDk92WXiQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Peking University Cancer Hospital \\u0026 Institute\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Nan\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-02-22 01:23:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6082622/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6082622/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12943-025-02333-7\",\"type\":\"published\",\"date\":\"2025-05-01T15:57:13+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":80306315,\"identity\":\"a2db711e-df43-40ec-b268-2f356b4f8043\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:22:04\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":822744,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenomic features of early-stage poorly differentiated LUAD.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Next-generation sequencing platforms applied for the paired normal and tumor tissue in the HG cohort including whole exome sequencing (WES), whole methylome sequencing (WMS) and RNA-seq. Patient numbers were indicated for each platform.\\u003c/p\\u003e\\n\\u003cp\\u003e(B) Venn plot to show the overlapping amounts of patients analyzed with three platforms and available clinical parameters.\\u003c/p\\u003e\\n\\u003cp\\u003e(C) Mutational landscape of the HG cohort, displaying the top 30 mutated genes. Clinical parameters including age, gender, smoking status, pTNM stage, visceral pleural invasion (VPI), lymphovascular invasion (LVI) and spread through air spaces (STAS), and molecular biomarkers including TNB, TMB, MATH, aneuploidy score, purity, ploidy, FGA and HLA-LOH status are indicated.\\u003c/p\\u003e\\n\\u003cp\\u003e(D) Mutational signature analysis of the HG cohort. Signature analysis was performed using the deconstructSigs R package. The x-axis displays 30 signatures from the COSMIC database, and the y-axis representing the weight score of each sample for the corresponding signature. Signature presented in more than 25% samples (Age, BRCA, Smoking, MMR deficiency, APOBEC, Aflatoxin and Ultraviolet) are marked in red.\\u003c/p\\u003e\\n\\u003cp\\u003e(E) The co-occurrence and mutually exclusive analysis for the top 30 mutated genes. Negative log transformed p values were indicated with red represents mutually exclusive and blue represents co-occurrence. The determination of an event is based on the odds ratio: an odds ratio \\u0026gt; 1 indicates **Co-occurrence**, while an odds ratio \\u0026lt; 1 indicates **Mutual exclusivity**. The intensity of the color reflects the significance of the p-value, with darker colors indicating smaller p-values. For both co-occurrence and mutually exclusive events, the smaller the p-value, the higher the corresponding -log10(p-value) in the label, indicating a stronger confidence level for each event.\\u003c/p\\u003e\\n\\u003cp\\u003e(F, G) GISTIC amplification (F) and deletion (G) plot to visualize focal-level copy number variations (CNV)s frequencies. The chromosome is oriented vertically from top to bottom, and GISTIC q-values at each region are plotted from left to right on a log scale. The green line represents the significance threshold (q-value = 0.25).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder41.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/6ec17dd80e458d94f236fc18.jpg\"},{\"id\":80305826,\"identity\":\"0da1aa5e-8ecd-48c8-b368-047efb0a7e64\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:04\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":794084,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAnalysis of recurrence-associated molecular features in the HG cohort.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) \\u0026nbsp;Correlation plot illustrating the relationship between WES-based gene-level CNV and RNA-seq derived RNA expression across the entire genome. Significant (FDR \\u0026lt; 0.05) positive and negative correlations are indicated in red and blue, respectively. Genes are ordered by chromosomal location on both the x and y axis. Diagonal line indicates potential cis-effects of CNA on mRNA.\\u003c/p\\u003e\\n\\u003cp\\u003e(B) \\u0026nbsp;Correlation analysis between tumor purity as measured by WES and WMS. Tumor purity for WMS was calculated using ichorCNA, while Facets software was employed for WES-based estimates. The Pearson correlation line is plotted in blue and its 95% confidence interval (CI) is indicated in the grey shadow. Pearson’s correlation coefficient (R) and significant p-value are shown.\\u003c/p\\u003e\\n\\u003cp\\u003e(C) \\u0026nbsp;Correlation analysis between methylation levels and RNA expression levels for both normal samples (blue) and tumor samples (red). The x-axis displays ten groups ranked by increasing expression levels. Gene expression levels were determined by calculating the median expression for each gene across all samples, log2-transforming the values, sorting the genes by increasing median expression, and dividing them into deciles. Methylation levels, representing the average methylation of CpG sites within specific regions (detailed in the Methods), as shown on the y-axis. Spearman’s correlation coefficient (R) and p-value are indicated.\\u003c/p\\u003e\\n\\u003cp\\u003e(D) \\u0026nbsp;Comparison of clinical and molecular features between recurrence (Re) and recurrence-free (Rf) patients. Clinical parameters including age, smoking status, VPI, LVI, pT stage and receipt of adjuvant therapy were analyzed. Molecular features such as ploidy and fraction of genome altered (FGA) were also assessed.\\u003c/p\\u003e\\n\\u003cp\\u003e(E) Mutational signature comparison between Re and Rf groups. Signature analysis was performed using the deconstructSigs R package. The x-axis displays 30 signatures from the COSMIC database, and the y-axis representing the weight score of each sample for the corresponding signature.\\u003c/p\\u003e\\n\\u003cp\\u003e(F) CNV frequencies comparison between Re and Rf groups. CNV were calculated using GISTIC2 software. The x-axis represents genomic chromosome positions, while the y-axis depicts CNV mutation frequencies for amplifications (AMP) (upper panel) and deletions (DEL) (down panel).\\u003c/p\\u003e\\n\\u003cp\\u003e(G) \\u0026nbsp;Comparison of immune profiles between Re and Rf groups. The enrichment scores of 28 immune cell subsets within the tumor microenvironment was quantitatively assessed using the GSVA R package and the ssGSEA method. RNA data in TPM format served as input. The x-axis represents samples, while the y-axis represents immune cell subsets. Each square indicates an enrichment score.\\u003c/p\\u003e\\n\\u003cp\\u003e(H) \\u0026nbsp;Methylation comparison between Re and Rf groups. Average methylation level within a 1Mb interval were depicted as blue rings for Rf and Re groups, respectively. Differential methylation regions (DMR)s representing the absolute methylation level difference between Re vs Rf are highlighted in orange (hypermethylation) and green (hypomethylation). DMRs located within CpG islands are denoted as CGI DMRs. Differential methylation analysis was conducted using DSS software, excluding CpG sites with depth \\u0026lt; 5.\\u003c/p\\u003e\\n\\u003cp\\u003eNote: For D, E and G, statistical comparison between RFS groups was performed using the fisher's test for categorical variables, and Wilcoxon test for continuous variables. ns: p \\u0026gt;0.05 (not marked on the figures E and G), *: 0.01 \\u0026lt; p \\u0026lt;= 0.05, **: 0.001 \\u0026lt; p \\u0026lt;= 0.01, ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder42.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/2a4856146507e8c06c99623e.jpg\"},{\"id\":80305827,\"identity\":\"05019aaf-75c4-41d3-8853-be54bf720d54\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:04\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":608468,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMolecular subtyping of the HG cohort based on transcriptomic and methylation data.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Volcano plot of differentially expressed genes (DEGs) between tumor (T) and normal samples (N). DEGs were identified using DESeq2 with an adjusted p-value (FDR) \\u0026lt; 0.05 and absolute log2 fold change \\u0026gt; 1. A total of 3725 DEGs were identified, with 2197 upregulated and 1528 downregulated in tumors.14345 genes non-significant.\\u003c/p\\u003e\\n\\u003cp\\u003e(B) Heatmap of clustering consistency in 82 tumor samples based on the top 3500 MAD DEGs. Tumor samples were clustered into two groups (R1 and R2) based on their gene expression patterns (detailed in method).\\u003c/p\\u003e\\n\\u003cp\\u003e(C) Kaplan-Meier survival curves of recurrence-free survival (RFS) for the single-sample transcriptomic classifier predicted two subtypes R1 (red) and R2 (blue). Hazard ratio, 95% CI, as well as p value were displayed.\\u003c/p\\u003e\\n\\u003cp\\u003e(D) Heatmap comparing methylation levels in the promotor region of 55638 gene between tumor (T) and normal samples (N). Average methylation levels of all CpG sites within ±2kb of the transcript start site for each gene are visualized, ranging from full methylation (depicted in red) to no methylation (in blue).\\u003c/p\\u003e\\n\\u003cp\\u003e(E) Heatmap of methylation clustering consistency in 98 tumor samples. Tumor samples were clustered into two groups (M1 and M2) based on their methylation profiles.\\u003c/p\\u003e\\n\\u003cp\\u003e(F) Kaplan-Meier survival curves of RFS for the methylation-based two subtypes M1 (blue) and M2 (red). Hazard ratio, 95% CI, as well as p value were displayed.\\u003c/p\\u003e\\n\\u003cp\\u003e(G) Sankey plot of RNA-predicted subtypes, methylation-clustering based subtypes, and relapse status.\\u003c/p\\u003e\\n\\u003cp\\u003e(H) Kaplan-Meier survival curves of RFS for combined RNA and methylation subtypes. Based on the results in G, the R1 group was further divided into C1 (R1 \\u0026amp; M2) and C2 (R1 \\u0026amp; M1), and the original R2 group was labeled as C3. The C3 group had the best RFS, followed by C2 and C1.\\u003c/p\\u003e\\n\\u003cp\\u003eKaplan-Meier survival curves of DFS for the single-sample transcriptomic classifier predicted subtypes in the TCGA early-stage cohort.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder43.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/ac5bbab6bb6752f6e76bd410.jpg\"},{\"id\":80305828,\"identity\":\"e020c5e5-b788-4ef0-ba05-ea9d555b33ea\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:04\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":846251,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eComparison of the clinicopathological and molecular characteristics of the three integrative subtypes.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Three subtypes C1 (red), C2 (yellow) and C3 (blue) were compared based on clinicopathological factors.\\u003c/p\\u003e\\n\\u003cp\\u003e(B) Comparison of molecular features, including TMB, MATH, aneuploidy, ploidy (WES), PAscore (WMS) and HLA-LOH status among the three subtypes C1, C2 and C3.\\u003c/p\\u003e\\n\\u003cp\\u003e(C, D) Comparison of top 50 mutated genes (SNV) and copy number variations (CNV). The left panel (C) shows the mutation frequency of the top 50 mutated genes in each subgroup(C1, C2 and C3). The y-axis represents the genes, and the x-axis represents the frequency (red: mutation, blue: wild type (WT)). Genes are sorted by mutation frequency from highest to lowest. The right panel (D) shows the comparison of gene CNV status between subgroups. The y-axis represents the genes, and the x-axis represents the frequency. Genes with a total copy number greater than the gene-level median ploidy were considered gains (light red). Genes with more than twice the median ploidy were considered amplifications (red). Genes with less than the median ploidy were considered losses (light blue). Genes with a total copy number of 0 were considered deletions (blue). Genes with no copy number variants were set to none (white). \\\"Group comparison\\\" denotes comparisons between subgroups: \\\"All\\\" represents an overall comparison to assess whether the presence of mutations in genes differs across the three subgroups, while \\\"C1 vs C2,\\\" etc., represents whether mutation frequencies differ between two specific subgroups.\\u003c/p\\u003e\\n\\u003cp\\u003e(E) The box plot showing statistical differences in immune cell enrichment score among three subtypes C1, C2 and C3. The enrichment scores of 28 immune cell subsets within the tumor microenvironment was quantitatively assessed using the GSVA R package and the ssGSEA method. The x-axis represents immune cell subtypes, and the y-axis represents the enrichment score of each subtype.\\u003c/p\\u003e\\n\\u003cp\\u003e(F-I) The box plot showing a statistical difference in GEP score (detailed in the Methods), PD-L1 (CD274) gene expression, total tumor immune score and stromal score (estimated by ESTIMATE, detailed in the Methods) among three subtypes C1, C2 and C3.\\u003c/p\\u003e\\n\\u003cp\\u003eNote: To calculate p-values between molecular subtypes, fisher's test was utilized for categorical variables, and Wilcoxon test for continuous variables. ns: p \\u0026gt;0.05 (not marked on the figures A, B and E), *: 0.01 \\u0026lt; p \\u0026lt;= 0.05, **: 0.001 \\u0026lt; p \\u0026lt;= 0.01, ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder44.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/51186c1891d614668eb9c85c.jpg\"},{\"id\":80305824,\"identity\":\"203f7229-fb37-49b9-abd4-5e2721fb4d1f\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:04\",\"extension\":\"jpg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":635648,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eExploration the biological functions of GINS1 and CPT1C by\\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003e in vitro \\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003eand \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003ein vivo\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003eexperiments.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A, B) Box plots showing statistical differences in GINS1 (A) and CPT1C (B) gene expression among normal and three subtypes (C1, C2 and C3).\\u003c/p\\u003e\\n\\u003cp\\u003e(C, D) Kaplan-Meier survival analysis of RFS comparing high and low expression levels of GINS1 (C) and CPT1C (D) gene (cutoff for each gene is detailed in the Methods). Hazard ratio, number of patients in each group, as well as p value are displayed.\\u003c/p\\u003e\\n\\u003cp\\u003e(E, G) The GINS1 (E) and CPT1C (G) knockdown efficiency was analyzed by western blotting in SPCA-1 cells.\\u003c/p\\u003e\\n\\u003cp\\u003e(F, H) Transwell assay was performed to evaluate the effect of GINS1 (F) and CPT1C (H) depletion on migration and invasion ability of SPCA-1 cells (n=3). Representative images (left) and quantitative results (right). Scale bar, 200µm. ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\\n\\u003cp\\u003e(I, K) Colony formation assay was performed to evaluate the effect of GINS1 (I) and CPT1C (K) depletion on proliferation ability of SPCA-1 cells (n=3). **: 0.001 \\u0026lt; p \\u0026lt;= 0.01, ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\\n\\u003cp\\u003e(J, L) CCK-8 assay was performed to evaluate the effect of GINS1 (J) and CPT1C (L) depletion on cell viability of SPCA-1 cells (n=3). ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\\n\\u003cp\\u003e(M, N) Images (left) and weight (right) of xenograft tumors derived from control SPCA-1 cells and SPCA-1 cells with GINS1 (M) or CPT1C (N) depletion (n = 5). ***: p \\u0026lt;= 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder45.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/ded6862b254607ce5baff576.jpg\"},{\"id\":80305829,\"identity\":\"1f8dafe0-4892-44e4-8a8e-74d69e714167\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:04\",\"extension\":\"jpg\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":476115,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePotential prognostic and predictive significance of the integrative subtyping.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e(A) Kaplan-Meier analysis of RFS in patients who received adjuvant therapy compared to those who did not.\\u003c/p\\u003e\\n\\u003cp\\u003e(B-D) Evaluation of the Impact of adjuvant therapy on RFS within different subgroups defined by the combined subtype (C1, C2 and C3).\\u003c/p\\u003e\\n\\u003cp\\u003e(F) Summary of clinicopathological and molecular features for each subtype, with potential therapeutic implications.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Binder46.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/9975b19d711af9b09ceeca0f.jpg\"},{\"id\":81988009,\"identity\":\"35831596-ea69-44bf-b558-55eea3b48f60\",\"added_by\":\"auto\",\"created_at\":\"2025-05-05 16:07:21\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":10047109,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/624c6b56-67ed-4492-bec6-ea41143c4778.pdf\"},{\"id\":80306316,\"identity\":\"cce00ad5-b6e0-49cc-8fdf-18e83746a525\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:22:04\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2069512,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryfigures.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/e2a742544ce5a50afa415320.pdf\"},{\"id\":80305844,\"identity\":\"9fd54770-c240-49f2-a696-464dcd9d22bf\",\"added_by\":\"auto\",\"created_at\":\"2025-04-10 10:14:19\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":182988787,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarytables.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6082622/v1/8e7f1eefd84913c3af9f6b8c.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Multi‑omics analysis identifies different molecular subtypes with unique outcomes in early-stage poorly differentiated lung adenocarcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eLung adenocarcinoma (LUAD), the predominant pathological subtype of lung cancer, demonstrates significant histological heterogeneity. In 2020, the pathology committee of the International Association for the Study of Lung Cancer (IASLC) proposed a novel grading system for invasive LUAD, where any tumor with 20% or more high-grade patterns (solid, micropapillary, and complex glandular patterns) was classified as a poorly differentiated (Grade 3) invasive pulmonary adenocarcinoma[1], which accounted for 34-55% of all resected LUADs and predicted the worst survival outcome[2].\\u0026nbsp;While this pattern-based grading system represents a significant advancement, it faces limitations in prognostic stratification across the heterogeneous spectrum of disease outcomes, particularly in the poorly differentiated group. Notably, only ~ 30% of patients with early-stage poorly differentiated LUAD experience postoperative recurrence[3], underscoring the critical need for additional molecular or biological parameters to complement the current grading system. Such multi-dimensional refinement would enable a more precise prognostic evaluation and formulate proper plans for treatment and surveillance.\\u003c/p\\u003e\\n\\u003cp\\u003eOmics studies are capable of providing multi-dimensional and high-resolution molecular information, thereby, assisting in identifying molecular characteristics and refining disease subtypes in solid tumors. Over the past decade, large-scale omics studies have significantly advanced our understanding of lung adenocarcinoma (LUAD) by delineating comprehensive mutational profiles, establishing molecular classification systems based on actionable driver mutations (e.g., EGFR and ALK), and uncovering critical roles of epigenetic regulation (e.g., chromatin modifications) and post-transcriptional processes (e.g., alternative splicing) in tumor pathogenesis[4]. Moreover, several transcriptomic stratifications have been also established, which are associated with specific genomic alterations for targeted therapy or immunotherapy, and describe different clinical outcomes[5, 6]. These studies have further refined the molecular classification for the personalized treatment of LUADs. However, since the clinicopathological phenotype still dominates the routine framework of clinical diagnosis, treatment and prognostic evaluation, incorporating an evolving understanding of molecular profiling with histopathological development is an urgent need for precise decision-making. Based on this requirement, adding a molecular classification could more accurately judge the prognosis within a certain pathological domain with a negative survival impact. Recent studies have shown that clustered molecular signatures can contribute to prognostic discrepancies in ovarian cancer and glioma presenting with poorly differentiated features[7-9]. Therefore, elucidating the molecular landscape of early-stage poorly differentiated LUAD could lay a foundation for comprehending the high-risk molecular characteristics of this heterogeneous entity and developing reliable prognostic biomarkers and precise strategies.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePioneering studies have explored the mutational features of poorly differentiated (Grade 3) LUADs, which showed that they had a greater proportion of \\u003cem\\u003eALK\\u003c/em\\u003e rearrangements and \\u003cem\\u003eKRAS\\u003c/em\\u003e mutations than those with Grades 1-2[10]. Additionally, other studies have shown that micropapillary or solid predominant poorly differentiated LUADs have a high tumor mutational burden (TMB), fraction of genome altered (FGA) and copy number amplifications (CNV)[11]. These studies indicate that poorly differentiated LUADs have relatively specific genomic characteristics. Some recent studies have evaluated the correlation between histological grade and PD-L1 expression and immune cell infiltration, and found that poor differentiated tumors exhibited higher PD-L1 expression and more T lymphocyte infiltration[12]. These results suggest that patients with poor differentiated tumor are more likely to benefit from immunotherapy. Despite these studies accelerating our understanding of the genomic and immune microenvironmental features of poor differentiated LUAD to some extent, comprehensive and in-depth molecular characterization of this disease entity remains elusive, especially from a multi-omics perspective.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, we conducted an integrative multi-omics analysis of genomic, epigenetic (methylation) and transcriptomic data from 101 early-stage poorly differentiated LUAD tumors and their paired normal tissues. Our study delineated the comprehensive characteristics of this aggressive disease entity and identified molecular subtypes with distinct prognoses, which could facilitate precise treatment and postoperative monitoring. \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCollection of clinical specimens and public dataset\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe enrolled 101 treatment-na\\u0026iuml;ve patients with early-stage poorly differentiated LUAD who underwent radical resection between July 2012 and December 2017 at Peking University Cancer Hospital \\u0026amp; Institute. All patients were confirmed as pathological T1-3N0M0 stage (stage I-II) according to the 8\\u003csup\\u003eth\\u003c/sup\\u003e edition of the lung cancer staging system[13] and did not received neoadjuvant therapy, including chemotherapy, targeted therapy or immunotherapy. All hematoxylin and eosin (HE) stained slides were reviewed according to the 2015 WHO classification of lung cancer and the new grading system proposed by the IASLC pathology committee[1, 14]. All tumors were identified as grade 3 invasive LUAD. Detailed clinical information of the individual patients is listed in \\u003cstrong\\u003eTable S1\\u003c/strong\\u003e. As for specimen collection, primary tumor specimens and paired normal tissues were collected immediately after resection and then snap-frozen and stored at -80 ℃ at the Biobank of Peking University Cancer Hospital \\u0026amp; Institute until further processing. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Peking University Cancer Hospital \\u0026amp; Institute (Institutional Review Board No. 2024KT65). Written informed consent was obtained from each patient before surgery.\\u003c/p\\u003e\\n\\u003cp\\u003eRNA read count files for 566 patients from the TCGA LUAD project were obtained from the TCGA legacy archive (https://gdc.cancer.gov/about-data/publications/pancanatlas). Corresponding clinical data for these patients were retrieved from cBioPortal (https://www.cbioportal.org/). For comparative analysis with the HG cohort, only patients with early stage (TNM stage I and II) from the TCGA cohort were included, referred to as the TCGA cohort. A second independent cohort (GSE31210)[15] consisting of more than 200 early-stage LUAD cases was utilized to further validate the transcriptomic subtyping.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNucleic acid extraction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGenomic DNA and total RNA was extracted from pairs of tumor specimens and normal tissues in Genecast Biotechnology Co., Ltd. (Wuxi, China). Briefly, DNA and RNA was extracted from fresh frozen tissue blocks using the AllPrep DNA/RNA Mini Kit (80204, Qiagen) according to the manufacturer\\u0026apos;s protocol. A total of 101 patients had nucleic acid extracted. Seventy-nine patients had both RNA and DNA extracted, while 22 patients had only RNA or DNA.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWhole-exome sequencing (WES) and Genomic data processing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eExtracted DNA was quantified by Qubit dsDNA HS Assay kit (Life Technologies, California, USA). Then DNA was fragmented into 150\\u0026ndash;200 bp by using Covaris M220 Focused-ultrasonicator\\u0026trade; Instrument (Covaris, Massachusetts, USA). Library construction and whole-exome capture of genomic DNA were performed using the KAPA Hyper Prep Kit (Illumina platforms) (KAPA Biosystems, Wilmington, MA) and Twist Human Core Exome kit (Twist Bioscience, San Francisco, USA) following the manufacturer\\u0026rsquo;s instruction. The captured DNA was then sequenced on an Illumina NovaSeq 6000 platform with 100-bp paired-end sequencing. The average sequencing depth was 229-fold for tumors and 200-fold for normal tissues.\\u003c/p\\u003e\\n\\u003cp\\u003eAll raw Illumina sequence data were demultiplexed and converted to fastq files, with the subsequent trimming of adaptors, contamination, and low-quality nucleotides to obtain clean data by using Trimmomatic (version 0.36)[16]. Sentieon[17] (version 202112.04) was used to align the clean reads to the human reference genome (hg19) by the bwa mem algorithm with default parameters. The raw BAM files obtained were subjected to various processing steps including sorting, removal of duplicate reads, local realignment, and base quality score recalibration (BQSR) by using Sentieon tools. These steps were performed to generate final BAM files, which were used for subsequent analysis such as coverage and depth statistics, as well as mutation calling analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSomatic mutation calling\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSomatic nucleotide variants (SNVs) and insertions/deletions (InDels) were detected for each paired sample using GATK Mutect2[18] (version 4.1.9.0). The resulting variant calls in VCF format were subsequently annotated with ANNOVAR[19]. High-confidence somatic mutations were retained based on the following stringent criteria: total sequencing depth of \\u0026ge; 40X, at least 4 supporting reads, variant allele frequency (VAF) \\u0026ge; 0.05, classification as nonsynonymous variants (resulting in amino acid changes), and a maximum population frequency of \\u0026lt; 0.02 in the 1000 Genomes Project, Exome Aggregation Consortium (ExAC), and Genome Aggregation Database (gnomAD). The top 30 frequently mutated genes in the HG cohort were subsequently analyzed for mutual exclusivity and co-occurrence using the maftools R package[20] (version 2.12.05). \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTumor mutational burden (TMB) and Mutant-allele tumor heterogeneity (MATH) score calculation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTumor mutational burden (TMB) for the HG cohort was quantified as the total number of somatic nonsynonymous variants within the entire covered exome region (sequencing depth \\u0026gt; 40X), expressed in mutations per megabase (Mb). The mutant-allele tumor heterogeneity (MATH) score was computed using all somatic variants with a variant allele frequency (VAF) ranging from 0.05 to 1, applying the formula: 100 \\u0026times; median absolute deviation (MAD) / median of the VAF[21].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSomatic copy number alteration calling \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFACETS[22] (version 0.6.2) was utilized to identify somatic copy number variant (CNV), as well as to determine tumor purity and ploidy. The total fraction of genome altered (FGA) was calculated as the percentage of a tumor genome showing a copy number different from the whole genome based on the CNV segment file for each tumor. Genomic Identification of Significant Targets in Cancer, version 2.0 (GISTIC2, version 2.0.23)[23] was employed to analyze focal genomic regions that exhibited significant amplification or deletion across all or Recurrence/Recurrence-free subgroup tumors. The aneuploidy score was also calculated based on the total number of altered arms for each tumor as previously suggested[24]. For the gene-wise result from GISTIC2, spearman correlation coefficients were computed to assess the correlation between gene level CNV and mRNA abundances, with an FDR threshold of less than 0.05. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eChromosomal number instability (CNI) and microsatellite instability (MSI) score calculation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe utilized CNVkit (version 0.9.2)[25] to detect copy number variations (CNVs) in tumor samples from each patient. The \\u0026quot;--reference\\u0026quot; parameter was employed to specify a copy number baseline derived from the Genecast normal database, serving as a negative control. After correction for GC content and length of target region using proprietary algorithms for each region, the read counts were transformed into log2 ratios and converted into Z-score based on Gaussian transformations versus a normal control group. The target regions that satisfied the Z-score greater than the 95\\u003csup\\u003eth\\u003c/sup\\u003e percentile plus twice-times absolute standard deviation of the normal control group were retained, and the Z-score was summed as the CNI score[26]. MSIsensor2 (version v0.1) (https://github.com/niu-lab/msisensor2) was used with default parameters to detect microsatellite instability (MSI) score. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHLA genotyping and HLA-LOH analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eReads from regions of HLA genes were extracted from normal BAM files using SAMtools (version 1.3)[27] and subsequently analyzed by HLA-HD software (version 1.2.0.1)[28] to identify Human Leukocyte Antigen class I (HLA-I) genotypes, employing the following parameters: minimum tag size set to 50 and cutting rate set to 0.95. Loss of heterozygosity (LOH) in HLA genes was determined using LOHHLA software (version 1.1.6)[29].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNeoantigen prediction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNeoantigens were predicted using the netMHC-4.0 (version 4.0a) algorithm, incorporating somatic SNVs, InDels, and HLA genotypes[30]. Predicted results meeting the criteria of a binding affinity (Aff) \\u0026lt;= 500 for the mutant (mut) and an Aff(mut)/Aff(wild) ratio \\u0026lt; 1 were identified as neoantigens. The neoantigen burden (TNB) for each tumor sample was determined by summing the number of predicted binder mutations per Mb (whole covered exome region was same to TMB). \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePathway alteration analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe somatic mutation genes were categorized into 10 canonical oncogenic signaling (COS) pathways and 8 DNA damage repair (DDR) pathways, based on previous research conducted separately[31]. The COS pathway comprised 335 genes and included pathways such as cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGF signaling, p53, and \\u0026beta;-catenin/Wnt. The DDR pathway encompassed mismatch repair (MMR), base excision repair (BER), checkpoint factors (CPF), Fanconi anemia (FA), homologous recombination repair (HRR), nucleotide excision repair (NER), nonhomologous end-joining (NHEJ), and DNA translesion synthesis (TLS). It involved a total of 233 genes. If a mutated gene was found in a specific pathway, it was inferred that the patient had a mutation in that pathway. The mutation frequency within a cohort was calculated as the number of patients with mutations in that pathway divided by the total number of patients. Comparison of the mutation frequency of each pathway across different cohorts or groups was performed using Fisher\\u0026rsquo;s exact test.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMutational signatures analysis and comparison\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe performed mutational signature analysis for the HG cohort using the DeconstructSigs R package (v1.8.0)[32]. Thirty COSMIC cancer signatures (https://cancer.sanger.ac.uk/signatures/signatures_v2/) were considered, and the contributions (weights) of these signatures in each tumor were normalized to a range between 0 and 1. The weight values of signatures between the Re and Rf groups were compared using the Wilcoxon rank-sum test. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRNA sequencing (RNA-seq) and data processing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe quantity of extracted RNA was measured using a Qubit 3.0 Fluorometer, while the quality was assessed using the Agilent 2100 Bioanalyzer system assay. After the rRNA removal from the total RNA, the cDNA library was constructed using the SMARTer Stranded Total RNA-Seq Kit v2 (634412, Takara). Following PCR enrichment and purification of adapter-ligated fragments, the libraries were paired-end sequenced (PE150) using the Illumina NovaSeq 6000 Sequencing System. The average sequencing depth was 56 million reads for both tumor and normal tissue samples.\\u003c/p\\u003e\\n\\u003cp\\u003eCutadapt (version 4.4) (https://doi.org/10.14806/ej.17.1.200) was utilized to remove the last 3 bases from each read. Trimmomatic (version 0.36)[16] was utilized to eliminate reads that contained adaptors, poly-N sequences, and low-quality reads using default parameters. The resulting trimmed reads were then aligned to the human hg19 reference transcriptome using hisat2 (version 2.1.0)[33]. The alignment data in BAM format was sorted and indexed using SAMtools (version 1.3)[27]. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDifferential gene expression and pathway enrichment analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo estimate the expression level of each gene, featureCounts (version 1.6.5)[34] were applied. Transcripts per million (TPM) values were calculated by normalizing the read counts, dividing them by the gene length and the total number of reads mapped to protein-coding genes. In total, 18,071 genes were initially profiled. DEGs (Differential Gene Expression) between different groups were identified using the DESseq2 package (version 1.38.3)[35] in the R software, with the criteria of |log2 (Fold Change)| \\u0026gt; 1 and a Bonferroni-adjusted p-value \\u0026lt; 0.05. Gene Set Enrichment Analysis (GSEA, version 4.3.3) was employed to perform pathway enrichment analysis among different groups[36]. Molecular Signatures Database (MSigDB, version 7.1) of hallmark gene sets (H), curated gene sets (C2), ontology gene sets (C5), oncogenic signature gene sets (C6), and immunologic signature gene sets (C7) were used in GSEA analysis. Differential gene enrichment analysis between groups was performed using the clusterProfiler R package using Reactome databases. The analysis included all differentially expressed genes, as well as those that were upregulated and downregulated. A significance threshold of p-value \\u0026lt; 0.05 was applied.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmune cell infiltration and gene expression signatures analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo evaluate the tumor microenvironment, the ESTIMATE R package (v1.0.13)[37] was employed with default settings to derive the ImmuneScore and StromalScore from gene expression data. A higher ImmuneScore indicates a greater immune cell infiltration within the tumor. To predict responses to immune checkpoint blockade (ICB) therapy in our HG cohort, we utilized an 18-gene T cell-inflamed gene expression profile (GEP)[38]. The GEP score for each sample was calculated as a weighted sum of these 18 genes, normalized against 11 housekeeping genes. Signature enrichment scores for 28 immune cell subsets within the tumor microenvironment[39] and 14 functional states derived from the CancerSEA database[40] were calculated using the gene set variation analysis (GSVA) R package (version 1.42.0)[41]. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWhole methylome sequencing (WMS) and data processing\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter the DNA extraction, WMS (Whole-methylome sequencing) libraries were generated using the NEBNext Enzymatic Methyl-seq Kit from New England Biolabs, according to the manufacturer\\u0026apos;s instructions. The quantification of the libraries was carried out using the Qubit dsDNA HS Assay Kit from Thermo Fisher Scientific. The libraries were then subjected to paired-end sequencing with a read length of 100 base pairs on the NovaSeq 6000 platform from Illumina. The average sequencing depth was 9-fold for both tumor and normal tissue samples.\\u003c/p\\u003e\\n\\u003cp\\u003eThe raw methylation sequencing reads were processed using Trimmomatic (version 0.36)[16] to remove adaptors and eliminate low-quality reads. The clean reads were then aligned to the human reference genome (hg19) and deduplicated using BisMark (version 0.23.0)[42]. SAMtools (version 1.3)[27] and BamUtil (https://github.com/statgen/bamUtil) were used for sorting and overlap-clipping of mapped reads. Reads with mapping quality below 20 were filtered out by SAMtools. The methylation status of each CpG site was extracted from a sorted bam file using the bismark methylation extractor function from BisMark. The beta value for each CpG site was calculated as the ratio of methylated CpGs to the sum of methylated and unmethylated CpGs in each sample. To examine genome-wide methylation patterns, the genome was divided into 1,846 non-overlapping 1-Mb segments after excluding regions that overlapped with Duke blacklisted regions or the hg19 gap track38[43]. Regions in 1-Mb segment and CpG island (https://genome.ucsc.edu/cgi-bin/hgTables) were then used to calculate the mean methylation. Tumor fraction was also extracted from the whole-methylome sequencing data using ichorCNA (version 0.2.0)[44], with normal copy number variation (CNV) files as a reference. Furthermore, an updated plasma aneuploidy score (PAscore) was calculated to summarize chromosome arm-level copy number alternation by adapting a previously described approach, using 30 PBMC profiles as reference baseline[45]. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDifferentially methylation region \\u003c/strong\\u003e(DMRs)\\u003cstrong\\u003e analysis \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify differentially methylated regions (DMRs) between recurrence and non-recurrence tumors, we utilized the DSS package (version 2.47.1)[46], with the results obtained from bismark methylation extractor as input. The following parameters were configured for DSS analysis: smoothing set to TRUE, smoothing span set to 500, minimum number of CpGs (minCG) set to 3, minimal length (minlen) set to 50, delta set to 0.1, distance threshold for merging (dis.merge) set to 100, percentage of significant probes (pct sig) set to 0.5, and p value \\u0026lt;0.05 was considered as statistical significant. DMRs were categorized as hyperDMRs or hypoDMRs based on the direction of methylation change. The average methylation levels in both directions were calculated using the smoothened CpG methylation levels. ChIPseeker (version 1.32.0)[47] was used for DMR annotation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAssociation analysis between gene expression and promoter methylation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate the relationship between gene expression and DNA methylation at the gene level, we categorized the gene expression levels into deciles based on the ascending distribution of log2-transformed TPM values[48]. The methylation level of a gene was determined by calculating the average methylation level of the CpGs within the nearest gene interval to the transcription start site (TSS). The criteria for selecting the interval were as follows: 1) If more than three CpGs were found within a 200bp range of the TSS, we assigned the average DNA methylation value of the adjacent CpGs in the TSS200 region to the corresponding gene; 2) If there were fewer than three CpGs within the TSS200 region, we calculated the average of the CpGs located in both TSS200 and the first exon of the gene; 3) If there were fewer than three CpGs in TSS200 or first exon, we calculated the average of the CpGs located in the upstream 1500bp region extending towards the first exon of the gene. Genes with fewer than three CpGs in the interval from TSS1500 to the first exon were excluded from the analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsensus Clustering for RNA datasets\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePrior to clustering analysis, we selected the top 500, 1,000, 1,500, 2,000, 2,500, 3,000, and 3,500 most variable coding genes based on their median absolute deviation (MAD) from the differentially expressed genes (DEGs) between tumor and normal samples, using the CancerSubtypes R package (version 1.20.0)[49]. Consensus clustering was then performed on these gene subsets using the ConsensusClusterPlus R package (version 1.62.0)[50]. The partitioning around medoids (PAM) algorithm with Spearman distance was employed for clustering. Due to the limited number of samples (n=82), the maximum number of clusters was set to six. To identify the most representative samples within each cluster, silhouette scores were computed for all samples using the CancerSubtypes R package. A two-cluster solution based on the top 3,500 most variably expressed genes (MAD-ranked) was selected due to its relatively higher average silhouette value, distinct separation between clusters in the consistent heatmap, and significant association with patient recurrence-free survival (RFS). Tumors exhibiting a silhouette value less than 0, as well as those from patients lacking recurrence-free survival (RFS) data (n=2), were excluded from both the survival analysis and subsequent single-sample transcriptomic classifier analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConstruction and validation of the single-sample transcriptomic classifier \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBuilding on previously published studies[51], we developed a Spearman nearest-centroid classifier specifically tailored for lung adenocarcinoma (LUAD) patients, utilizing RNA sequencing-derived gene expression data. The construction of the classifier involved first calculating the mean expression levels of samples within each predefined subtype (as described in the aforementioned section). Subsequently, for each individual sample, we determined the Spearman correlation between its gene expression profile and the centroid expression profiles of the subtypes. Each sample was then assigned to the subtype with the highest Spearman correlation coefficient. Samples exhibiting a maximal correlation value below 0.2 were deemed to have a weak association with any subtype and were excluded from the subsequent survival analysis.\\u003c/p\\u003e\\n\\u003cp\\u003eTo validate the robustness of this RNA-based classification method, we applied it to transcriptomic data from the TCGA cohort. The transcripts per million (TPM) values were calculated from raw read counts using the same method as described for the HG cohort.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsensus Clustering for DNA methylome datasets\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo profile the DNA methylation patterns across 98 tumor samples, we first calculated the average DNA methylation value for CpG sites located within a region of +/- 2000 base pairs of the transcription start site (TSS). This approach allows us to capture the DNA methylation status in the promoter regions of genes, which often play a critical role in regulating gene expression[52]. The subtyping method was similar to that used for RNA datasets. For this analysis, the distance metric applied was \\u0026apos;euclidean,\\u0026apos; and the clustering algorithm employed was \\u0026apos;k-means.\\u0026apos; A two-cluster solution based on the top 1,000 most variably expressed genes (ranked by median absolute deviation) was selected. Tumors with a silhouette value less than 0, as well as those from patients without recurrence-free survival (RFS) data, were excluded from the survival analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIntegrate analysis of RNA and WMS subtyping\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe utilized the NetworkD3 package (version 0.4) in R to generate a Sankey diagram that illustrates the relationships among RNA subtypes, WMS subtypes, and patient recurrence status. Our analysis revealed that the WMS subgroup effectively partitioned the RNA R1 subgroup into two distinct clusters. In contrast, nearly all samples from the RNA R2 subgroup remained within the same category in the WMS clustering analysis and exhibited fewer recurrences. To integrate the clustering results from both RNA and WMS omics data, we reclassified the samples as follows: samples belonging to both the RNA R1 and WMS M2 subgroups were combined into subtype C1; those belonging to the RNA R1 and WMS M1 subgroups were designated as subtype C2; and samples within the RNA R2 subgroup were retained as subtype C3.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIdentification of stably expressed differentially expressed genes (DEGs) across three integrative subtypes and their prognostic evaluation\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDifferentially expressed gene (DEG) lists were obtained for each pairwise comparison of the three subtypes (FDR \\u0026lt; 0.05 and |log2FC| \\u0026gt; 1). For each subtype, candidate gene lists for both high expression and low expression were generated by taking the intersection of relevant comparisons. For example, in the C1 subtype, genes highly expressed in C1 were identified by intersecting C1vsC2_up and C1vsC3_up, while genes with low expression in C1 were identified by intersecting C1vsC2_down and C1vsC3_down. To obtain a list of stably expressed DEGs in each subtype, gene filtering was performed. Initially, genes were filtered based on their Transcripts Per Million (TPM) values, retaining those with TPM \\u0026gt; 1 in more than 50% of samples within the relevant subtype. Subsequently, genes were further filtered based on their coefficient of variation (CV). The CV of TPM values was calculated within each specific subtype, and genes were ranked in ascending order according to their CV values. The top 20 genes with the lowest CV, indicating stable expression within the subtype, were selected. If fewer than 20 genes met this criterion, all available genes were included. To assess the prognostic impact of highly and stably expressed genes in the C1 subtype, we calculated p-values and hazard ratios (HRs) for each gene across percentiles from 0.1 to 0.9 in both the HG and TCGA cohorts. Bubble plots were generated to visualize the HRs and p-values for recurrence-free survival (RFS) in the HG cohort and disease-free survival (DFS) in the TCGA cohort. Additionally, we utilized the surv_cutpoint function from the survminer R package to estimate the optimal cutoff for each gene.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCell Culture\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSPCA-1 was purchased from Chinese Academy of Sciences Cell Bank (Shanghai, China) and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (HyClone, South Logan, UT, USA), penicillin (100 U/mL), and streptomycin (100 \\u0026mu;g/mL). HEK293T was purchased from the American Type Culture Collection (ATCC) (Manassas, VA) and cultured in DMEM supplemented with 10% fetal bovine serum (HyClone, South Logan, UT, USA), penicillin (100 U/mL), and streptomycin (100 \\u0026mu;g/mL). All cells were maintained at 37℃ in a humidified cell incubator with 5% CO\\u003csub\\u003e2\\u003c/sub\\u003e. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLentivirus infections\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eshRNA oligos targeting GINS1 or CPT1C and a non-targeting oligo control were engineered into pSIH-puro plasmid. The target sequences for short hairpin RNA were as followed: shGINS1 #1: 5\\u0026rsquo;-CAAGTTCTGGAGGAGATGAAA-3\\u0026rsquo;; shGINS1 #2: 5\\u0026rsquo;- CTTGCCAAATGCATTACGATT-3\\u0026rsquo;; shCPT1C #1: 5\\u0026rsquo;-CTCACGTTTCTGGAATG ACTT-3\\u0026rsquo;; shCPT1C #2: 5\\u0026rsquo;- CCTGCTGATGACCATGGTTAT-3\\u0026rsquo;. For pSIH-puro lentivirus production, the packaging plasmids vSVG, pLP1 and pLP2 were used. The indicated packaging plasmids and lentiviral vectors were co-transfected into HEK293T cells. After 48h transfection, the supernatant containing lentivirus particles was collected and stored in aliquots at -80 \\u0026deg;C. For lentivirus infection, cells were first treated with polybrene (5 \\u0026micro;g/mL) (TR-1003, Sigma), then infected with the indicated lentivirus. Stable cell populations were established by selecting with puromycin (2 \\u0026mu;g/mL) (540222, Sigma).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRNA extraction and qRT-PCR\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTotal RNA was extracted with TRIzol reagent (Thermo Fisher Scientific). The cDNAs were obtained using Quantscript RT kit (Tiangen, Beijing, China) according to the manufacture\\u0026rsquo;s protocol. Real-time RT-PCR was performed by using SYBR Premix Ex TaqTM II (TaKaRa, Japan) on Step-one plus real-time PCR system (Applied Biosystems, Foster City, CA, USA), according to the manufacturer\\u0026rsquo;s instructions. The sequences of primers for qRT-PCR were as followed: GINS1 Forward primer-ACGAGGATGGACTCAGACAAG; GINS1 Reverse primer-TGCAGCGTCGATTT CTTAACA; CPT1C Forward primer-GGATGGCACTGAAGAGGA AA; CPT1C Reverse primer- TCCTGGAAAAGGCATCTCTC; GAPDH Forward primer-CCGGGAAACTGTGGCGTGATGG; GAPDH Reverse primer-AGGTGGAGGAG TGGGTGTCGCTGTT.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAntibodies and reagents\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAntibodies used in this study were as follows: Anti-GINS1 (PA562341, Invitrogen), Anti-CPT1C (66072, Proteintech), Anti-\\u0026beta;-actin (A5316, Sigma). Secondary antibodies included HRP Goat Anti-Mouse (926-80010, LI-COR) and HRP Goat Anti-Rabbit (926-80011, LI-COR). \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTranswell assay\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSPCA-1 cells (8\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e per insert) were suspended in FBS-free RPMI1640 and seeded into the upper chambers with or without pre-coated matrigel (BD, Franklin Lakes, NJ, USA). The bottom chambers were added with RPMI 1640 medium supplemented with 10% FBS. After 24 hr incubation, the migratory or invasive cells were methanol-fixed and stained with crystal violet. Cells in three randomly selected fields were photographed and statistically analyzed.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCell viability and colony formation assays\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCell viability was quantified by CCK-8 assays. SPCA-1 cells with indicated treatment were seeded into 96-well plates (4\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e cells/mL; 100 \\u0026micro;L/well). Cell Proliferation Reagent CCK-8 (#CK04, Dojindo Molecular Technologies, Japan) was added to the cell culture medium at a ratio of 1:10. After 1 hr of incubation at 37℃, absorbance at 450 nm was measured using a microplate reader (BioTek). For colony formation assays, SPCA-1 cells with indicated treatment were seeded into 6-well plates (4\\u0026times;10\\u003csup\\u003e3\\u003c/sup\\u003e cells/well), followed by incubation at 37\\u0026deg;C for 9-12 days until the development of visible colonies. Colonies were stained with crystal violet staining solution and counted. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eXenograft tumor model \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e5-week-old, female BALB/c nude mice were purchased from Vital River (Beijing, China). 2\\u0026times;10\\u003csup\\u003e6\\u003c/sup\\u003e SPCA-1 cells with stable GINS1/CPT1C depletion or control cells were subcutaneously injected into the flanks of mice in separate groups. Each group was composed of 5 mice, randomly chosen. Following 3 weeks, the mice were sacrificed. Tumors were weighed and analyzed by a two-tailed, unpaired Student\\u0026rsquo;s t test. The Institutional Animal Care and Use Committee of Peking University Cancer Hospital and Institute approved all animal experiments. \\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eQuantification and statistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data statistics and figure generation were conducted using R version 4.1.2. Various statistical tests were employed based on the type of variables being compared. Continuous variables were compared using either the two-sided Wilcoxon rank-sum test or the Kruskal-Wallis rank-sum test. Categorical variables were compared using either the chi-square test or the two-sided Fisher\\u0026apos;s exact test. Survival analyses were performed using the Kaplan\\u0026ndash;Meier method and two-sided log-rank tests were used to compare survival curves (R packages survival and survminer). Univariate and multivariate Cox regression analyses were conducted using the survival and survminer packages. To evaluate the predictive ability of each Cox module at specific time points (first year, third year, and fifth year), recurrence prediction analyses were performed using the survival and timeROC packages. Chi-square proportion calculated using R package rms was used to assess the relative contribution of each variable to survival risk. Statistical significance was defined as p\\u003cem\\u003e \\u003c/em\\u003e\\u0026lt; 0.05.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eMutational landscape of early-stage poorly differentiated LUAD\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTotally, 101 patients were enrolled in this study (the HG cohort). The median age of the cohort was 60 years (range: 34-80 years). Sixty-three patients (62.4%) were male, and thirty-eight patients were female (37.6%). At the end of the follow-up period (median follow-up: 43.2\\u0026plusmn;20.1 months), 33 (32.7%) patients experienced recurrence. Detailed individual patient information is listed in \\u003cstrong\\u003eTable S1\\u003c/strong\\u003e. Totally, WES, WMS and RNA-seq was successfully conducted on matched tumor-normal tissue pairs from 97, 98 and 82 patients in this cohort, respectively (\\u003cstrong\\u003eFigure 1A\\u003c/strong\\u003e). Among them, 78 patients had WES, RNA-seq and WMS data, simultaneously (\\u003cstrong\\u003eFigure 1B\\u003c/strong\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe first investigated the mutational landscape of the HG cohort (\\u003cstrong\\u003eTable S2\\u003c/strong\\u003e), and the top 30 mutated genes are shown in \\u003cstrong\\u003eFigure 1C\\u003c/strong\\u003e. The most frequently mutated genes were \\u003cem\\u003eEGFR\\u003c/em\\u003e (51%), \\u003cem\\u003eTP53\\u003c/em\\u003e (45%), \\u003cem\\u003eMUC16\\u003c/em\\u003e (28%), \\u003cem\\u003eRYR2\\u003c/em\\u003e (28%), \\u003cem\\u003eTTN\\u003c/em\\u003e (26%) and \\u003cem\\u003eKRAS\\u003c/em\\u003e (19%). These findings differed from a recent proteogenomic study of unselected Chinese LUAD patients[53]. In that cohort, the mutational frequencies of \\u003cem\\u003eMUC16\\u003c/em\\u003e, \\u003cem\\u003eRYR2\\u003c/em\\u003e and \\u003cem\\u003eTTN\\u003c/em\\u003e were lower, and were not among the top 10 mutated genes. Somatic mutational signatures were also investigated in the HG cohort (\\u003cstrong\\u003eTable S3\\u003c/strong\\u003e), and our analysis identified Signatures 1, 3, 4, 6, 7, 13, and 24 as significant contributors (\\u003cstrong\\u003eFigure 1D\\u003c/strong\\u003e). Some of these signatures have been reported to be associated with lung cancer. For instance, Signature 4 is associated with tobacco smoking and is likely due to DNA damage caused by tobacco smoke-derived mutagens[54]. The co-occurrence and mutual exclusivity patterns of the most mutated genes were further investigated (\\u003cstrong\\u003eFigure 1E\\u003c/strong\\u003e). Notably, \\u003cem\\u003eEGFR\\u003c/em\\u003e mutations were mutually exclusive with mutations in many other genes, including \\u003cem\\u003eTTN\\u003c/em\\u003e, \\u003cem\\u003eKRAS\\u003c/em\\u003e, \\u003cem\\u003eRYR1\\u003c/em\\u003e and \\u003cem\\u003eMUC16\\u003c/em\\u003e, which was consistent with previous studies[55, 56]. As revealed by FACETS, significant CNVs were observed in the HG cohort (\\u003cstrong\\u003eFigure S1\\u003c/strong\\u003e). Using GISTIC 2.0, remarkable focal level copy number gains were observed at 1q21.1, 7p11.2, 10q11.21, 14q13.3, 16q11.2, 19q12 and 20q13.33 (\\u003cstrong\\u003eFigure 1F\\u003c/strong\\u003e;\\u003cstrong\\u003e\\u0026nbsp;Table S4\\u003c/strong\\u003e). Additionally, losses were detected at 1p11.2, 2q21.2, 9p21.3, 16q11.2, and 16p13.2 (\\u003cstrong\\u003eFigure 1G\\u003c/strong\\u003e;\\u003cstrong\\u003e\\u0026nbsp;Table S4\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRecurrence-associated molecular events at genomic, epigenomic and transcriptomic levels \\u0026nbsp;\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThrough multi-dimensional omics data, we explored the interactions between the genome, epigenome, and transcriptome (\\u003cstrong\\u003eTables S5-7\\u003c/strong\\u003e). We initially assessed the impact of CNVs on mRNA expression levels. As illustrated in \\u003cstrong\\u003eFigure 2A\\u003c/strong\\u003e, CNVs have the potential to influence gene expression either positively or negatively, in both cis and trans modes. A comprehensive analysis of 16,455 CNV-mRNA pairs revealed significant correlations: 588 pairs exhibited significant cis effects, whereas 6,569 CNVs influenced the expression of 4,075 mRNAs in trans (Spearman correlation analysis, FDR \\u0026lt; 0.05). We estimated tumor purity in 97 patients using the FACETS algorithm for WES data and the ichorCNA R package for DNA methylation data (\\u003cstrong\\u003eTable S8\\u003c/strong\\u003e). As depicted in \\u003cstrong\\u003eFigure 2B\\u003c/strong\\u003e, the tumor purity estimates derived from these two methods exhibited a strong correlation (Pearson\\u0026rsquo;s correlation coefficient = 0.69, p \\u0026lt; 0.001), indicating the robustness and reliability of these computational approaches for assessing tumor purity. We subsequently investigated the correlation between the epigenome and transcriptome using whole methylome sequencing (WMS) and RNA-seq data. As illustrated in \\u003cstrong\\u003eFigure 2C\\u003c/strong\\u003e, DNA methylation exhibited an inverse relationship with gene expression in the first three to four deciles, after which it remained low across higher expression deciles within the promoters and first exons of both tumor (Spearman correlation; R = -0.51, p \\u0026lt; 0.001) and normal tissues (Spearman correlation; R = -0.46, p \\u0026lt; 0.001).\\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo identify the determinants of recurrence in this specific disease entity, we analyzed the clinicopathological and molecular differences between recurrent and non-recurrent cases in the HG cohort. Among the clinicopathological variables examined, visceral pleural invasion (VPI, p = 0.001) and pathological T stage (pT stage, p = 0.022) were significantly correlated with recurrence. Other factors such as age, smoking status, lymphovascular invasion (LVI), tumor size, receipt of adjuvant therapy, tumor mutational burden (TMB), and mutant-allele tumor heterogeneity (MATH) were not significantly associated with recurrence (\\u003cstrong\\u003eFigure 2D\\u003c/strong\\u003e;\\u003cstrong\\u003e\\u0026nbsp;Figure S2A\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S8\\u003c/strong\\u003e). We also compared the frequently mutated genes between recurrent and non-recurrent cases and found mutations of some frequently mutated genes, including \\u003cem\\u003eEGFR\\u003c/em\\u003e, \\u003cem\\u003eTP53\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;RYR2\\u003c/em\\u003e and \\u003cem\\u003eMUC16\\u003c/em\\u003e, were not correlated with recurrence (\\u003cstrong\\u003eFigure S2B\\u003c/strong\\u003e). Conversely, mutations in \\u003cem\\u003eSTK11\\u003c/em\\u003e, \\u003cem\\u003eXIRP2\\u003c/em\\u003e, \\u003cem\\u003eMXRA5\\u003c/em\\u003e, and \\u003cem\\u003eZNF536\\u003c/em\\u003e were more frequent in the recurrent cases. Previous studies have also indicated that mutations in \\u003cem\\u003eEGFR\\u003c/em\\u003e could not determine the prognosis of LUAD[57], whereas mutations in \\u003cem\\u003eSTK11\\u003c/em\\u003e and \\u003cem\\u003eMXRA5\\u003c/em\\u003e have been associated with poor prognosis[58, 59]. Additionally, analysis of the 10 canonical oncogene signaling pathways and DNA damage response (DDR) pathways in recurrent and non-recurrent cases revealed no significant differences between the two groups (\\u003cstrong\\u003eFigure S2C\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S9\\u003c/strong\\u003e). Next, we investigated the potential association between mutational signatures and recurrence, and found no significant differences in mutational signatures between recurrent and non-recurrent cases (\\u003cstrong\\u003eFigure 2E\\u003c/strong\\u003e). Next, we investigated the differences in copy number variations (CNVs) and observed that recurrent tumors exhibited a higher frequency of CNVs, including both amplifications and deletions (\\u003cstrong\\u003eFigure 2F; Table S10\\u003c/strong\\u003e).\\u0026nbsp;According to our analyses, several molecular features related to chromosomal instability, such as abnormal ploidy, FGA and aneuploidy, were enriched in recurrent tumors, suggesting that chromosomal instability indicates a worse prognosis.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition to the intrinsic properties of tumor cells, immune cell populations are crucial prognostic determinants of LUAD. Using single-sample Gene Set Enrichment Analysis (ssGSEA), we compared the enrichment scores of 28 immune cell subsets between recurrent and non-recurrent cases (\\u003cstrong\\u003eTable S11\\u003c/strong\\u003e). As shown in \\u003cstrong\\u003eFigure 2G\\u003c/strong\\u003e, recurrent tumors showed significantly lower enrichment scores for central memory CD4+ T cells and immature dendritic cells (p \\u0026lt; 0.05). Previous studies have demonstrated that activated CD8+ T cells, effector memory CD8+ T cells and macrophages are associated with LUAD prognosis, which was not observed in the present study. Based on the MSigDB database(C2), gene set enrichment analysis (GSEA) of recurrent and non-recurrent cases demonstrated that recurrent cases were significantly enriched in pathways, including the adenylate cyclase activating pathway, tumorigenesis by ret c634r, aml methylation cluster 7 dn, DNA methylation, peptide hormone biosynthesis, and inflammatory response tgfb1 (nominal p-value \\u0026lt; 0.05, \\u003cstrong\\u003eFigure S2D\\u003c/strong\\u003e;\\u003cstrong\\u003e\\u0026nbsp;Table S12\\u003c/strong\\u003e). We investigated methylation differences between recurrent and non-recurrent cases using WMS data. Our analysis revealed a global pattern of hypomethylation in 1M-bp regions in recurrent cases compared to that in non-recurrent cases. Specifically, among the 10,569 differentially methylated regions (DMRs) identified, 95.7% exhibited global hypomethylation, and 77.8% of the CpG island (CGI) regions displayed similar trends (\\u003cstrong\\u003eFigure 2H\\u003c/strong\\u003e; \\u003cstrong\\u003eTables S13\\u003c/strong\\u003e, \\u003cstrong\\u003e14\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePatient subtyping based on multiple molecular dimensions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNext, we explored the molecular subtyping of early-stage poorly differentiated LUADs using different omics datasets. Initially, bulk RNA-seq data from 82 patients were analyzed to explore the transcriptomic subtypes of early-stage poorly differentiated LUADs. A total of 3,725 genes were differentially expressed between tumor and normal tissue (|(log2FC)| \\u0026gt; 1, FDR \\u0026lt; 0.05). Of these, the expression of 2,197 genes was upregulated and the expression of 1,528 genes was downregulated in tumor tissues (\\u003cstrong\\u003eFigure 3A\\u003c/strong\\u003e;\\u003cstrong\\u003e\\u0026nbsp;Table S15\\u003c/strong\\u003e). Through unsupervised consensus clustering of the top 3,500 differentially expressed genes (DEGs), we identified two distinct transcriptomic groups in the HG cohort: R1 (n = 38) and R2 (n = 44) (\\u003cstrong\\u003eFigure 3B\\u003c/strong\\u003e). To enable the application of these transcriptomic classes in future research and clinical settings, we developed a single-sample classifier based on the clustering results. This classifier was constructed by using an approach adopted in a recent study of non-muscle invasive bladder cancer[51], wherein a group label was assigned to the transcriptomic profile of a tumor based on its correlation with group-specific mean expression profiles\\u0026nbsp;(detailed in the Methods section). We applied this classifier to both the HG (n = 82) and TCGA cohorts (n = 394), and reclassified the tumor samples into R1 and R2 subtypes (\\u003cstrong\\u003eTable S8\\u003c/strong\\u003e,\\u003cstrong\\u003e\\u0026nbsp;16\\u003c/strong\\u003e).\\u0026nbsp;The reclassification resulted in R1 (n = 56) and R2 (n = 26) in the HG cohort. The R1 subtype exhibited significantly poorer recurrence-free survival (RFS) in the HG cohort (p = 0.018; log-rank test, \\u003cstrong\\u003eFigure 3C\\u003c/strong\\u003e) and worse disease-free survival (DFS) in TCGA cohort (p = 0.005; log-rank test, \\u003cstrong\\u003eFigure 3I\\u003c/strong\\u003e) than the R2 subtype.\\u0026nbsp;The same trend was also observed in another independent validation cohort comprising more than 200 early-stage LUAD cases (\\u003cstrong\\u003eFigure S3A\\u003c/strong\\u003e). We subsequently analyzed the WMS data from 98 patients to explore the methylation subtyping of early-stage poorly differentiated LUAD. \\u003cstrong\\u003eFigure 3D\\u003c/strong\\u003e shows methylation levels of all gene promoter regions (TSS +/-2kb) between tumor and normal tissues (\\u003cstrong\\u003eTable S17\\u003c/strong\\u003e). Unsupervised consensus clustering based on the top 1000 gene promoter regions was used to identify methylation subtypes, and two subtypes were identified: M1 (n = 67) and M2 (n = 31) (\\u003cstrong\\u003eFigure 3E\\u003c/strong\\u003e). The M2 subtype exhibited a significantly inferior RFS compared to the M1 subtype (p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e= 0.029; log-rank test; \\u003cstrong\\u003eFigure 3F\\u003c/strong\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe emergence of cancer necessitates molecular alterations in both the transcriptome and epigenome. We further leveraged data from 78 patients who underwent both RNA-seq and WMS to develop integrative subtyping. As shown in \\u003cstrong\\u003eFigure 3G\\u003c/strong\\u003e, tumors classified under the R1 subtype were further divided into two subsets based on methylation profiling, termed C1 (26/53, R1\\u0026amp;M2) and C2 (27/53, R1\\u0026amp;M1) in integrative subtyping. Most tumors predicted to be of the R2 subtype were enriched in the M1 subtype (23/25, 92.00%), with only four (16%) recurring after surgery; thus, the R2 subtype was designated as the C3 subtype in integrative subtyping. Consequently, the 78 patients included in integrative subtyping were classified into three groups: C1 (n = 26), C2 (n = 27), and C3 (n = 25). Survival analysis revealed significant differences in prognosis among the three subtypes, with C1 subtype patients exhibiting the shortest recurrence-free survival (RFS) and C3 subtype patients demonstrating the longest RFS (p = 0.024; log-rank test; \\u003cstrong\\u003eFigure 3H\\u003c/strong\\u003e, \\u003cstrong\\u003eTable S8\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; Univariate Cox regression analysis of the selected clinical features, as shown in \\u003cstrong\\u003eFigure S3B\\u003c/strong\\u003e, identified age, tumor size, STAS, VPI, and pT stage as significant prognostic factors (all p \\u0026lt; 0.05). In multivariate Cox regression analysis, integrative subtyping remained an independent prognostic indicator for recurrence-free survival (RFS) (p \\u0026lt; 0.05, \\u003cstrong\\u003eFigure S3C\\u003c/strong\\u003e). We then performed a time-dependent receiver operating characteristic (ROC) analysis to assess the predictive accuracy for recurrence at 1, 3 and 5 years using a Cox model that incorporated integrative subtyping along with other prognostic clinical features. The predictive accuracy, indicated by the area under the curve (AUC), was approximately 0.7 when using integrative subtyping alone or in combination with another clinical feature (\\u003cstrong\\u003eFigure S3D\\u003c/strong\\u003e). Further analysis combining integrative subtyping with the five aforementioned clinical factors yielded AUC values of 0.74, 0.80, and 0.74 for the first, third, and fifth years, respectively (\\u003cstrong\\u003eFigure S3E\\u003c/strong\\u003e). These results suggest that the integrative model can more effectively predict the progression of early-stage poorly differentiated lung adenocarcinomas (LUADs), particularly at the 3-year mark. Notably, integrative subtyping emerged as the most significant predictor of RFS, accounting for 38.9% of the variance based on ANOVA of the Cox proportional hazards model (\\u003cstrong\\u003eFigure S3F\\u003c/strong\\u003e), which represents the largest Chi-square value relative to the total Chi-square values of all variables included in the model.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinicopathological and molecular characteristics of the three integrative subtypes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe three integrative subtypes identified using RNA-seq and WMS data exhibited different prognoses, suggesting that they are different disease subsets. Therefore, we further investigated their characteristics in depth. We initially compared the clinicopathological characteristics of the three integrative subtypes. As shown in \\u003cstrong\\u003eFigure 4A\\u003c/strong\\u003e, the C3 subtype included more males (p = 0.005, Fisher\\u0026rsquo;s exact test) and smokers (p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e= 0.014, Fisher\\u0026rsquo;s exact test) than the C2 subtype, but there was no significant difference between subtypes C1 and C2 and subtypes C1 and C3. In terms of pT stage, the C1 subtype enriched more patients with T2 or T3 stage disease than the C3 subtype (p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e= 0.013, Fisher\\u0026rsquo;s exact test). Additionally, tumor size decreased in subtypes C1, C2, and C3, but the difference was not statistically significant. Other clinical characteristics, including age, STAS, VPI, LVI, pTNM stage, and adjuvant therapy, showed no significant differences among the three subtypes (\\u003cstrong\\u003eFigure 4A\\u003c/strong\\u003e,\\u003cstrong\\u003e\\u0026nbsp;Figure S4A\\u003c/strong\\u003e). We further explored whether the three integrative subtypes have significantly different molecular characteristics. As shown in \\u003cstrong\\u003eFigure 4B\\u003c/strong\\u003e, we observed increased TMB and MATH in the C1 subtype (all p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, Wilcoxon test), indicating that patients exhibiting the C1 subtype could harbor more mutations and intra-tumor heterogeneity (ITH). Parameters related to chromosome stability, such as aneuploidy, ploidy, FGA, MSI, CNI, and PA score, were also higher in the C1 subtype (all p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, Wilcoxon test; \\u003cstrong\\u003eFigure 4B\\u003c/strong\\u003e and \\u003cstrong\\u003eFigure S4B\\u003c/strong\\u003e). Additionally, we observed that the C1 subtype was significantly more likely to exhibit HLA loss of heterozygosity (HLA-LOH) and a higher tumor neoantigen burden (TNB) than subtypes C2 and C3 (all p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, Fisher\\u0026rsquo;s exact test). Increased HLA-LOH levels are usually accompanied by tumor immune evasion. Together, these results indicate that the C1 subtype has significant genomic instability, mutation burden, and ITH.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo further investigate molecular heterogeneity among the three integrative subtypes, we compared the SNV and CNV profiles, focusing on the top 50 most frequently mutated genes and CNVs (\\u003cstrong\\u003eTable S2\\u003c/strong\\u003e, \\u003cstrong\\u003eTable S6\\u003c/strong\\u003e). While frequently mutated genes, such as \\u003cem\\u003eEGFR\\u003c/em\\u003e, \\u003cem\\u003eTP53\\u003c/em\\u003e, \\u003cem\\u003eKRAS\\u003c/em\\u003e, \\u003cem\\u003eRYR2,\\u003c/em\\u003e and \\u003cem\\u003eTTN\\u003c/em\\u003e, showed similar mutation frequencies across subtypes, several other genes showed significant differences (\\u003cstrong\\u003eFigure 4C\\u003c/strong\\u003e). For example, \\u003cem\\u003eSEC16B\\u003c/em\\u003e and \\u003cem\\u003eFSIP2\\u003c/em\\u003e mutations were more common in the C1 subtype, whereas \\u003cem\\u003eAHNAK2\\u003c/em\\u003e mutations were enriched in the C3 subtype (p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, Fisher\\u0026rsquo;s exact test). Additionally, \\u003cem\\u003eSTK11\\u0026nbsp;\\u003c/em\\u003emutations were more common in the C1 subtype than in the C3 subtype (p\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, Fisher\\u0026rsquo;s exact test). Next, we compared the CNV among these three subtypes and found that deletions of many pivotal cancer-associated genes were more common in the C1 subtype, including \\u003cem\\u003eCDKN2A\\u003c/em\\u003e, \\u003cem\\u003eMLLT3\\u003c/em\\u003e, \\u003cem\\u003eCD274\\u003c/em\\u003e, \\u003cem\\u003eJAK2\\u003c/em\\u003e, \\u003cem\\u003eNOTCH1\\u003c/em\\u003e and \\u003cem\\u003eNFIB\\u003c/em\\u003e (\\u003cstrong\\u003eFigure 4D\\u003c/strong\\u003e). We further compared 10 canonical oncogene signaling pathways and the DDR pathway between these three subtypes (\\u003cstrong\\u003eTable S18\\u003c/strong\\u003e). We found that the C1 subtype showed a higher mutational rate for the Notch and BER pathways than the C2 subtype (p \\u0026lt; 0.05), and the C2 subtype showed a higher mutational rate for the PI3K pathway than the C3 (p \\u0026lt; 0.05, \\u003cstrong\\u003eFigure S4C\\u003c/strong\\u003e). \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the RNA-seq data, analysis of the enrichment scores for 28 distinct immune cell subsets among the three integrative subtypes (\\u003cstrong\\u003eTable S11\\u003c/strong\\u003e) revealed significant disparities in the majority of immune cell types between these subtypes (\\u003cstrong\\u003eFigure 4E\\u003c/strong\\u003e). Notably, there was a progressive increase in the proportions of nearly all these cell types from subtype C1 to C2 and further to C3. For instance, the C3 subtype tumors exhibited the highest abundance of various immune effector cells, including activated CD8+ T cells, natural killer cells, and mast cells (all p \\u0026lt; 0.05, Wilcoxon test), compared to the C1 and C2 subtype tumors. Furthermore, our analysis revealed that the C3 subtype demonstrated elevated levels of CD274 (PD-L1) expression and a higher Gene Expression Profiling (GEP) score (\\u003cstrong\\u003eFigure 4F\\u003c/strong\\u003e,\\u003cstrong\\u003e\\u0026nbsp;G\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S19\\u003c/strong\\u003e). Notably, both the immune and stromal scores, as estimated by the ESTIMATE algorithm, were significantly higher in subtype C3 than subtypes C1 and C2 (\\u003cstrong\\u003eFigure 4H\\u003c/strong\\u003e, \\u003cstrong\\u003eI\\u003c/strong\\u003e). These findings suggest that the C3 subtype is characterized by an immunologically active tumor microenvironment (TME). Additionally, an analysis of functional states across 25 cancer types revealed significant enrichment of cell cycle and DNA repair functions in subtype C1, whereas subtype C3 was significantly enriched for quiescence and stemness-related functions (\\u003cstrong\\u003eFigure S4D\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S20\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eThe exploration of representative differentially expressed genes in the three integrative subtypes\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNext, we identified DEGs that were stably expressed across the three integrative subtypes (\\u003cstrong\\u003eFigure S5A\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S21\\u003c/strong\\u003e). To evaluate the prognostic significance of highly and stably expressed genes in the C1 subtype, we calculated the p-values and hazard ratios (HRs) for each gene across percentiles from 0.1 to 0.9 in both the HG and TCGA cohorts (\\u003cstrong\\u003eFigure S5B\\u003c/strong\\u003e, \\u003cstrong\\u003eC\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S22\\u003c/strong\\u003e). Among these genes,\\u0026nbsp;\\u003cem\\u003eGINS\\u003c/em\\u003e complex subunit 1 (\\u003cem\\u003eGINS1)\\u003c/em\\u003e and Carnitine palmitoyltransferase 1C (\\u003cem\\u003eCPT1C)\\u003c/em\\u003e were of particular interest, because their high expression was significantly associated with poor prognosis in both cohorts (\\u003cstrong\\u003eFigure 5A\\u003c/strong\\u003e, \\u003cstrong\\u003eB\\u003c/strong\\u003e). Previous studies have highlighted their pivotal roles in promoting tumor progression[60, 61]. We utilized the surv_cutpoint function from the survminer R package to determine the optimal cutoff for each gene and found that higher expression levels of both genes were associated with shorter recurrence-free survival (RFS) in the HG cohort (GINS1: HR = 2.23, p = 0.050; CPT1C: HR = 4.68, p = 0.002, \\u003cstrong\\u003eFigure 5C\\u003c/strong\\u003e, \\u003cstrong\\u003eD\\u003c/strong\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo further explore the biological functions of \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPT1C\\u003c/em\\u003e in LUAD progression, we knocked down the expression of endogenous \\u003cem\\u003eGINS1\\u0026nbsp;\\u003c/em\\u003eand \\u003cem\\u003eCPT1C\\u003c/em\\u003e in SPCA-1 cells using lentiviruses encoding shRNAs. We found that the downregulation of both \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPT1C\\u003c/em\\u003e markedly attenuated cell migration and invasion\\u0026nbsp;\\u003cem\\u003ein vitro\\u003c/em\\u003e (\\u003cstrong\\u003eFigure 5E-H\\u003c/strong\\u003e and\\u0026nbsp;\\u003cstrong\\u003eFigure S5D, E\\u003c/strong\\u003e). In addition to inhibiting pro-metastatic function, the knockdown of \\u003cem\\u003eGINS1\\u003c/em\\u003e and\\u003cem\\u003e\\u0026nbsp;CPT1C\\u003c/em\\u003e strikingly restrained cell proliferation, as demonstrated by the reduced proliferative ability in CCK-8 and colony formation assays (\\u003cstrong\\u003eFigure 5I-L\\u003c/strong\\u003e and \\u003cstrong\\u003eFigure S5F\\u003c/strong\\u003e,\\u003cstrong\\u003e\\u0026nbsp;G\\u003c/strong\\u003e). We further performed the xenograft tumor model to consolidate the biological functions of \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPT1C\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;in vivo\\u003c/em\\u003e. As shown in\\u0026nbsp;\\u003cstrong\\u003eFigure 5M\\u003c/strong\\u003e and\\u0026nbsp;\\u003cstrong\\u003eN\\u003c/strong\\u003e, compared to the control group, the downregulation of \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPT1C\\u003c/em\\u003e markedly attenuated tumor progression\\u0026nbsp;\\u003cem\\u003ein vivo,\\u0026nbsp;\\u003c/em\\u003eaccompanied by a marked reduction in tumor size and weight.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePotential prognostic and predictive significance of the integrative subtyping\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn current clinical practice, adjuvant chemotherapy remains a cornerstone in the treatment of LUAD; however, its use in early-stage LUAD remains controversial. In the HG cohort, 37 patients received adjuvant chemotherapy after surgery. These patients tended to exhibit a worse prognosis than those who did not receive postoperative treatment, although the difference showed only a marginal statistical significance (p = 0.059, \\u003cstrong\\u003eFigure 6A\\u003c/strong\\u003e). We explored the potential of integrative subtyping as a possible indicator for assessing adjuvant chemotherapy efficacy. As shown in \\u003cstrong\\u003eFigure 6B-D\\u003c/strong\\u003e, the survival analysis suggested limited benefit from adjuvant chemotherapy in both C1 and C3 subtypes, with the C3 subtype exhibiting a particularly unfavorable outcome (p = 0.038). In contrast, a trend toward improved prognosis was observed in patients classified as the C2 subtype who received adjuvant chemotherapy, although this difference did not reach statistical significance (p = 0.301). These preliminary findings indicated that integrative subtyping might offer some clinical utility in evaluating heterogeneous responses to adjuvant chemotherapy in early-stage poorly differentiated LUAD, warranting further investigation to assess its potential value in therapeutic decision-making.\\u003c/p\\u003e\\n\\u003cp\\u003eIn summary, each integrative subtype exhibits unique clinical features, molecular characteristics, and TME, as shown in\\u0026nbsp;\\u003cstrong\\u003eFigure 6E\\u003c/strong\\u003e. The C1 subtype is characterized by high genomic instability, low immune infiltration and high tumor heterogeneity. The C2 subtype displays low tumor heterogeneity and HLA-LOH, fewer neoantigens and moderate immune activation, and it may benefit from adjuvant chemotherapy. The C3 subtype exhibits substantial immune infiltration, high GEP score, high TNB, and low frequency of HLA-LOH, suggesting a potential benefit from immunotherapy.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eEarly-stage poorly differentiated LUADs exhibit a poor prognosis, with approximately 30% of them experiencing recurrence[3]. Precise management of this population is a challenge in clinical practice. Adding molecular dimensional features and stratification to the new grading system could render the prognostic evaluation more precise. In this study, we performed integrative multi-omics analysis, including genomics, epigenomics and transcriptomics, in an early-stage poorly differentiated lung adenocarcinoma cohort (n=101). Our analysis of the multi-platform dataset revealed comprehensive molecular characteristics of this special disease entity. Furthermore, three molecular subtypes were identified based on the transcriptomic and methylation data. These integrative subtypes demonstrated distinct clinicopathological and molecular features with prognostic significance, enabling further precise stratification of early-stage poorly differentiated LUADs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; Consistent with previous genomic studies in the Chinese population[53, 62], \\u003cem\\u003eEGFR\\u003c/em\\u003e (51%) and \\u003cem\\u003eTP53\\u003c/em\\u003e (45%) were also the predominant mutated genes in early-stage poorly differentiated LUAD. However, the mutational frequencies of \\u003cem\\u003eMUC\\u003c/em\\u003e16 (28%), \\u003cem\\u003eRYR2\\u003c/em\\u003e (28%) and \\u003cem\\u003eTTN\\u003c/em\\u003e (26%) were significantly higher in early-stage poorly differentiated LUAD than in the unselected Chinese LUAD patients. \\u003cem\\u003eMUC16\\u0026nbsp;\\u003c/em\\u003emutations have been found in several solid tumors, including melanoma and breast cancer[63-65]. Several recent studies have revealed that \\u003cem\\u003eMUC16\\u003c/em\\u003e mutations are associated with TMB in solid tumors[66]. A recent study reported that \\u003cem\\u003eRYR2\\u003c/em\\u003e was one of the most mutational genes which were almost always shared by primary lung cancers and brain metastasis lesions[67]. Previous studies have revealed that the mutation of\\u003cem\\u003e\\u0026nbsp;RYR2\\u0026nbsp;\\u003c/em\\u003ewas a significant biomarker associated with high TMB in\\u0026nbsp;LUAD[68]. As another gene with high-frequency mutations,\\u0026nbsp;\\u003cem\\u003eTTN\\u0026nbsp;\\u003c/em\\u003emutations were associated with the therapeutic efficacy of immune checkpoint blockade in advanced non-small cell lung cancer[69, 70]. Moreover, \\u003cem\\u003eTTN\\u003c/em\\u003e mutations may function as crucial intra-tumoral drivers for micropapillary/solid components, as shown in another LUAD cohort[71].\\u0026nbsp;In addition to oncogenic gene mutations, remarkable CNVs were also found in early-stage poorly differentiated LUADs.\\u0026nbsp;These results indicated that poorly differentiated LUADs had relatively special mutational landscape and chromosome structure variations, which may lead to the transition of growth patterns during the development of LUAD.\\u003c/p\\u003e\\n\\u003cp\\u003eAccurate risk classification plays a pivotal role in guiding treatment strategies and enhancing patient outcomes. While the current grading system categorizes poorly differentiated LUADs as a homogeneous group, our study revealed three distinct molecular subtypes through transcriptomic and methylation profiling. These subtypes exhibit unique molecular signatures that correlate with specific clinical, pathological, and prognostic characteristics. The C1 subtype displayed the highest levels of TMB, MATH, aneuploidy and HLA-LOH levels, coupled with relatively reduced immune cell infiltration. These factors likely contribute to increased genomic instability and impaired anti-tumor immune responses and lead to its poor prognosis. Multiple studies have demonstrated that high TMB is associated with a poor prognosis of LUADs[72]. Aneuploidy is a hall mark of cancer, and patients with high aneuploidy often show a poor prognosis[73]. Lower PD-L1 expression further implies that these tumors might rely less on the PD-L1/PD-1 pathway for immune escape, instead of utilizing alternative mechanisms such as HLA-LOH. In contrast to the C1 subtype, the C3 subtype exhibited lower genomic instability, as evidenced by reduced TMB, MATH and FGA. Notably, this subtype also demonstrated the highest level of immune cell infiltration, which may contribute to its more favorable prognosis. Furthermore, the elevated immune infiltration and higher GEP scores suggest that this subtype may be particularly responsive to immunotherapy, making it a promising candidate for such treatment strategies. For the C2 subtype, its molecular characteristics was intermediate and featured with low heterogeneity, low LOH and less neo antigens. In this study, we found the C1 and C3 subtypes did not benefit from adjuvant chemotherapy, especially for the later one. However, patients with C2 subtype receiving adjuvant chemotherapy tended to exhibit better prognosis, implying adjuvant chemotherapy was a potential treatment modality for the C2 subtype. However, these are preliminary findings that require further validation in an additional large-scale cohort. We will continue to accumulate more data on this aspect and validate the results through both retrospective and prospective cohort studies. Currently, several molecular classification systems exist for unselected LUAD patients, yet they remain largely disconnected from pathological diagnostics.\\u0026nbsp;The integration of classical morphological information and molecular data remains a severe challenge for clinical utility. The integrative subtypes identified in this study offer a practicable approach to further enhance prognostic evaluation in poorly differentiated LUAD, building upon the current pathological diagnostic framework. To identify early-stage LUAD with a high recurrence risk, we recommend first using the histological grading system to select poorly differentiated cases, then performing molecular subtyping exclusively on these tumors.\\u0026nbsp;This stratified approach will facilitate more precise prognostic assessment and guide adjuvant therapy decisions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u0026nbsp;Given the pressing demand for innovative treatment methods, the DEGs identified among the three integrative subtypes were selected for further studies to illustrate their potential as new therapeutic targets. Our study found that \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPTIC\\u003c/em\\u003e exhibited high expression in the C1 subtype and were associated with a higher hazard ratio of recurrence in both the HG and TCGA cohorts, acting as potential therapeutic targets. \\u003cem\\u003eGINS1\\u003c/em\\u003e, part of helicase at DNA replication forks, is involved in DNA replication initiation and elongation[74, 75]. Previous studies have shown that\\u0026nbsp;\\u003cem\\u003eGINS1\\u003c/em\\u003e plays essential roles in tumorigenesis and progression, including NSCLC[60]. In addition to DNA replication, \\u003cem\\u003eGINS1\\u003c/em\\u003e is associated with tumor metastasis, sorafenib resistance, doxorubicin resistance, and B-cell proliferation, suggesting essential role of \\u003cem\\u003eGINS1\\u003c/em\\u003e in tumor progression, drug resistance and immune microenvironment[75, 76]. \\u003cem\\u003eCPT1C\\u003c/em\\u003e, a rate-limiting enzyme in fatty acid oxidation, fuels tumor growth under metabolic stress and acts as a prognostic marker in many tumors[77, 78]. Several studies have reported that \\u003cem\\u003eCPT1C\\u003c/em\\u003e, a regulator of lipid metabolic reprogramming, is pivotal for the proliferation and metastasis of various tumors[61, 79]. Moreover, dysregulation of \\u003cem\\u003eCPT1C\\u003c/em\\u003e can lead to plasma membrane remodeling and anthracycline resistance in breast cancer[80].\\u0026nbsp;Collectively, all the evidence showed that both \\u003cem\\u003eGINS1\\u003c/em\\u003e and \\u003cem\\u003eCPT1C\\u003c/em\\u003e are pivotal for tumor progression and are associated with drug resistance, which is consistent with the pro-proliferative\\u0026nbsp;and pro-metastatic functions of \\u003cem\\u003eGINS1\\u003c/em\\u003e/\\u003cem\\u003eCPT1C\\u003c/em\\u003e in our studies and indicates potential drug targets to enhance adjuvant chemotherapy in LUADs.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eMoreover, our studies found that downregulated genes in both GINS1-high expression and C1 subtype samples were significantly enriched in pathways including \\u0026quot;Neutrophil degranulation\\u0026quot;, \\u0026quot;Signaling by Interleukins\\u0026quot;, \\u0026quot;Arachidonic acid metabolism\\u0026quot; and \\u0026quot;PD-1 signaling\\u0026quot; (\\u003cstrong\\u003eFigure S6A, B\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S23\\u003c/strong\\u003e). These findings suggest that GINS1 may promote tumor progression by suppressing T cell and neutrophil functions, thereby inducing an immunosuppressive microenvironment and contributing to the aggressive phenotype of the C1 subtype[81-83]. Notably, arachidonic acid metabolism, a critical metabolic pathway in tumor progression, has been shown to enhance antitumor immune responses by activating CD8+ T cells in colorectal cancer[84]. Therefore, subsequent studies on elucidating how GINS1 modulates arachidonic acid metabolism to influence C1 subtype transformation, may provide a stronger theoretical foundation for developing therapeutic strategies targeting arachidonic acid metabolism inhibitors. Additionally, we observed that downregulated genes in both CPT1C-high and C1 subtype samples were significantly enriched in pathways such as \\u0026quot;Chemokine receptors bind chemokines\\u0026quot;, \\u0026quot;Neutrophil degranulation\\u0026quot;, \\u0026quot;Phosphorylation of CD3 and TCR zeta chains\\u0026quot;, \\u0026quot;PD-1 signaling\\u0026quot; and \\u0026quot;Biosynthesis of specialized proresolving mediators (SPMs)\\u0026quot; (\\u003cstrong\\u003eFigure S6A, C\\u003c/strong\\u003e; \\u003cstrong\\u003eTable S23\\u003c/strong\\u003e). These results suggest that CPT1C may similarly promote tumor progression by creating an immunosuppressive microenvironment that contributes to the aggressive phenotype of the C1 subtype[82, 83, 85, 86]. Of particular interest, SPMs, a class of bioactive lipid mediators derived from polyunsaturated fatty acids (PUFAs), play crucial roles in actively resolving inflammation and inducing surrounding immune-infiltrating cells such as tumor-associated macrophages, representing a promising direction for anticancer therapy[87, 88]. While CPT1C is known to participate in fatty acid oxidation[77], its specific impact on SPMs and the function of SPMs in early-stage poorly differentiated lung adenocarcinoma remain unclear. Therefore, future studies may focus on elucidating the mechanism by which CPT1C regulates SPMs, with the goal of developing novel SPM-based therapeutic strategies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; While this study provides some valuable insights, several limitations should be acknowledged. First, we actually made substantial efforts to screen early-stage LUAD from our institution between 2012 and 2017, and screened out 101 poorly differentiated cases. Although this is the largest multi-omics cohort of early-stage poorly differentiated LUAD with long-term follow-up to date, the sample size remains limited.\\u0026nbsp;This constrained statistical power may explain why certain clinically apparent trends\\u0026mdash;particularly regarding differential responses to adjuvant chemotherapy across the three subtypes\\u0026mdash;failed to reach statistical significance. Consequently, these preliminary findings warrant validation through larger-scale prospective studies.\\u0026nbsp;Second, owing to the scarcity of public multi-omics data encompassing transcriptomic and methylation profiles for poorly differentiated LUAD, we could not externally validate our integrative molecular subtyping system or its prognostic value. Future multi-center studies focusing on early-stage poorly differentiated LUAD cohorts will be essential to verify the clinical relevance and generalizability of our classification. Third, we performed comprehensive and in-depth analyses of the molecular characteristics of poorly differentiated LUAD. However, we did not profile the proteome or metabolome, which could reflect the biological characteristics of this special disease entity more directly. Therefore, we will perform proteomic and metabolomic analyses on poorly differentiated LUAD in the future to establish a more comprehensive and reliable subtyping.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp; In summary, through integrated analyses, we delineate a genomic, epigenomic and transcriptomic landscape and uncover three molecular subtypes with distinct prognoses of early-stage poorly differentiated LUAD. Our study not only provides important insight into the relatively specific biology of this subset of LUAD but also reveals its molecular heterogeneity, which can potentially facilitate their precise treatment and postoperative monitoring.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank all the participants and family members for participating in this study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBL, WT, XTZ, LDX, SY and NW contributed to the conception and design of the study. BL, WT, XTZ, YRL, LDX, XY, ELZ and YSH acquired and analyzed the data. MH, YGZ, XRC, YQW and TYG provided patient materials and scientific discussion. BL, WT, XTZ and NW drafted the initial version of the Stage 1 protocol. All authors contributed to revisions of the Stage 1 protocol, preparation of the Stage 2 manuscript, and approved the submitted version.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by\\u0026nbsp;the Capital\\u0026rsquo;s funds for health improvement and research (2024-1-1023),\\u0026nbsp;the National Natural Science Foundation of China (No.\\u0026nbsp;82303583,\\u0026nbsp;82373082),\\u0026nbsp;the Science Foundation of Peking University Cancer Hospital (2022-11), the\\u0026nbsp;National Key R\\u0026amp;D Program of China (No.\\u0026nbsp;2022YFC2406804), the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0501702)\\u0026nbsp;and\\u0026nbsp;the National Ten-thousand Talent Program.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe raw data can be obtained from the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number PRJCA026561 (https://ngdc.cncb.ac.cn/gsa). To protect patient privacy, data access can be obtained through a request to the data access committee. Access to the data will be restricted to noncommercial entities.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInstitutional review board statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Ethics Committee of Peking University Cancer Hospital \\u0026amp; Institute (Institutional Review Board No.\\u0026nbsp;2024KT65).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no conflict of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eMoreira AL, Ocampo PSS, Xia Y, Zhong H, Russell PA, Minami Y, Cooper WA, Yoshida A, Bubendorf L, Papotti M, et al: \\u003cstrong\\u003eA Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2020, \\u003cstrong\\u003e15:\\u003c/strong\\u003e1599-1610.\\u003c/li\\u003e\\n\\u003cli\\u003eRuan Y, Cao W, Han J, Yang A, Xu J, Zhang T: \\u003cstrong\\u003ePrognostic impact of the newly revised IASLC proposed grading system for invasive lung adenocarcinoma: a systematic review and meta-analysis.\\u003c/strong\\u003e \\u003cem\\u003eWorld J Surg Oncol \\u003c/em\\u003e2024, \\u003cstrong\\u003e22:\\u003c/strong\\u003e302.\\u003c/li\\u003e\\n\\u003cli\\u003eXu L, Su H, Hou L, Wang F, Xie H, She Y, Gao J, Zhao S, Dai C, Xie D, et al: \\u003cstrong\\u003eThe IASLC Proposed Grading System Accurately Predicts Prognosis and Mediastinal Nodal Metastasis in Patients With Clinical Stage I Lung Adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eAm J Surg Pathol \\u003c/em\\u003e2022, \\u003cstrong\\u003e46:\\u003c/strong\\u003e1633-1641.\\u003c/li\\u003e\\n\\u003cli\\u003e\\u003cstrong\\u003eComprehensive molecular profiling of lung adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eNature \\u003c/em\\u003e2014, \\u003cstrong\\u003e511:\\u003c/strong\\u003e543-550.\\u003c/li\\u003e\\n\\u003cli\\u003eChen J, Yang H, Teo ASM, Amer LB, Sherbaf FG, Tan CQ, Alvarez JJS, Lu B, Lim JQ, Takano A, et al: \\u003cstrong\\u003eGenomic landscape of lung adenocarcinoma in East Asians.\\u003c/strong\\u003e \\u003cem\\u003eNat Genet \\u003c/em\\u003e2020, \\u003cstrong\\u003e52:\\u003c/strong\\u003e177-186.\\u003c/li\\u003e\\n\\u003cli\\u003eFukui T, Shaykhiev R, Agosto-Perez F, Mezey JG, Downey RJ, Travis WD, Crystal RG: \\u003cstrong\\u003eLung adenocarcinoma subtypes based on expression of human airway basal cell genes.\\u003c/strong\\u003e \\u003cem\\u003eEur Respir J \\u003c/em\\u003e2013, \\u003cstrong\\u003e42:\\u003c/strong\\u003e1332-1344.\\u003c/li\\u003e\\n\\u003cli\\u003eBurdett NL, Willis MO, Alsop K, Hunt AL, Pandey A, Hamilton PT, Abulez T, Liu X, Hoang T, Craig S, et al: \\u003cstrong\\u003eMultiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer.\\u003c/strong\\u003e \\u003cem\\u003eNat Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e55:\\u003c/strong\\u003e437-450.\\u003c/li\\u003e\\n\\u003cli\\u003eDavidson NR, Barnard ME, Hippen AA, Campbell A, Johnson CE, Way GP, Dalley BK, Berchuck A, Salas LA, Peres LC, et al: \\u003cstrong\\u003eMolecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms.\\u003c/strong\\u003e \\u003cem\\u003ebioRxiv \\u003c/em\\u003e2023.\\u003c/li\\u003e\\n\\u003cli\\u003eLinazi G, Maimaiti A, Abulaiti Z, Shi H, Zhou Z, Aisa MY, Kang Y, Abulimiti A, Dilimulati X, Zhang T, et al: \\u003cstrong\\u003ePrognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment.\\u003c/strong\\u003e \\u003cem\\u003eHeliyon \\u003c/em\\u003e2024, \\u003cstrong\\u003e10:\\u003c/strong\\u003ee36989.\\u003c/li\\u003e\\n\\u003cli\\u003eDeng C, Zheng Q, Zhang Y, Jin Y, Shen X, Nie X, Fu F, Ma X, Ma Z, Wen Z, et al: \\u003cstrong\\u003eValidation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association With Common Driver Mutations.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2021, \\u003cstrong\\u003e16:\\u003c/strong\\u003e1684-1693.\\u003c/li\\u003e\\n\\u003cli\\u003eCaso R, Sanchez-Vega F, Tan KS, Mastrogiacomo B, Zhou J, Jones GD, Nguyen B, Schultz N, Connolly JG, Brandt WS, et al: \\u003cstrong\\u003eThe Underlying Tumor Genomics of Predominant Histologic Subtypes in Lung Adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2020, \\u003cstrong\\u003e15:\\u003c/strong\\u003e1844-1856.\\u003c/li\\u003e\\n\\u003cli\\u003eDong ZY, Zhang C, Li YF, Su J, Xie Z, Liu SY, Yan LX, Chen ZH, Yang XN, Lin JT, et al: \\u003cstrong\\u003eGenetic and Immune Profiles of Solid Predominant Lung Adenocarcinoma Reveal Potential Immunotherapeutic Strategies.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2018, \\u003cstrong\\u003e13:\\u003c/strong\\u003e85-96.\\u003c/li\\u003e\\n\\u003cli\\u003eDetterbeck FC, Boffa DJ, Kim AW, Tanoue LT: \\u003cstrong\\u003eThe Eighth Edition Lung Cancer Stage Classification.\\u003c/strong\\u003e \\u003cem\\u003eChest \\u003c/em\\u003e2017, \\u003cstrong\\u003e151:\\u003c/strong\\u003e193-203.\\u003c/li\\u003e\\n\\u003cli\\u003eTravis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, et al: \\u003cstrong\\u003eThe 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2015, \\u003cstrong\\u003e10:\\u003c/strong\\u003e1243-1260.\\u003c/li\\u003e\\n\\u003cli\\u003eOkayama H, Kohno T, Ishii Y, Shimada Y, Shiraishi K, Iwakawa R, Furuta K, Tsuta K, Shibata T, Yamamoto S, et al: \\u003cstrong\\u003eIdentification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas.\\u003c/strong\\u003e \\u003cem\\u003eCancer Res \\u003c/em\\u003e2012, \\u003cstrong\\u003e72:\\u003c/strong\\u003e100-111.\\u003c/li\\u003e\\n\\u003cli\\u003eBolger AM, Lohse M, Usadel B: \\u003cstrong\\u003eTrimmomatic: a flexible trimmer for Illumina sequence data.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2014, \\u003cstrong\\u003e30:\\u003c/strong\\u003e2114-2120.\\u003c/li\\u003e\\n\\u003cli\\u003eAldana R, Freed D: \\u003cstrong\\u003eData Processing and Germline Variant Calling with the Sentieon Pipeline.\\u003c/strong\\u003e \\u003cem\\u003eMethods Mol Biol \\u003c/em\\u003e2022, \\u003cstrong\\u003e2493:\\u003c/strong\\u003e1-19.\\u003c/li\\u003e\\n\\u003cli\\u003eCibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G: \\u003cstrong\\u003eSensitive detection of somatic point mutations in impure and heterogeneous cancer samples.\\u003c/strong\\u003e \\u003cem\\u003eNat Biotechnol \\u003c/em\\u003e2013, \\u003cstrong\\u003e31:\\u003c/strong\\u003e213-219.\\u003c/li\\u003e\\n\\u003cli\\u003eWang K, Li M, Hakonarson H: \\u003cstrong\\u003eANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2010, \\u003cstrong\\u003e38:\\u003c/strong\\u003ee164.\\u003c/li\\u003e\\n\\u003cli\\u003eMayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP: \\u003cstrong\\u003eMaftools: efficient and comprehensive analysis of somatic variants in cancer.\\u003c/strong\\u003e \\u003cem\\u003eGenome Res \\u003c/em\\u003e2018, \\u003cstrong\\u003e28:\\u003c/strong\\u003e1747-1756.\\u003c/li\\u003e\\n\\u003cli\\u003eMroz EA, Tward AD, Hammon RJ, Ren Y, Rocco JW: \\u003cstrong\\u003eIntra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas.\\u003c/strong\\u003e \\u003cem\\u003ePLoS Med \\u003c/em\\u003e2015, \\u003cstrong\\u003e12:\\u003c/strong\\u003ee1001786.\\u003c/li\\u003e\\n\\u003cli\\u003eArora A, Shen R, Seshan VE: \\u003cstrong\\u003eFACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing.\\u003c/strong\\u003e \\u003cem\\u003eMethods Mol Biol \\u003c/em\\u003e2022, \\u003cstrong\\u003e2493:\\u003c/strong\\u003e89-105.\\u003c/li\\u003e\\n\\u003cli\\u003eMermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G: \\u003cstrong\\u003eGISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.\\u003c/strong\\u003e \\u003cem\\u003eGenome Biol \\u003c/em\\u003e2011, \\u003cstrong\\u003e12:\\u003c/strong\\u003eR41.\\u003c/li\\u003e\\n\\u003cli\\u003eTaylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC, Schumacher SE, Wang C, Hu H, Liu J, et al: \\u003cstrong\\u003eGenomic and Functional Approaches to Understanding Cancer Aneuploidy.\\u003c/strong\\u003e \\u003cem\\u003eCancer Cell \\u003c/em\\u003e2018, \\u003cstrong\\u003e33:\\u003c/strong\\u003e676-689.e673.\\u003c/li\\u003e\\n\\u003cli\\u003eTalevich E, Shain AH, Botton T, Bastian BC: \\u003cstrong\\u003eCNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing.\\u003c/strong\\u003e \\u003cem\\u003ePLoS Comput Biol \\u003c/em\\u003e2016, \\u003cstrong\\u003e12:\\u003c/strong\\u003ee1004873.\\u003c/li\\u003e\\n\\u003cli\\u003eWeiss GJ, Beck J, Braun DP, Bornemann-Kolatzki K, Barilla H, Cubello R, Quan W, Jr., Sangal A, Khemka V, Waypa J, et al: \\u003cstrong\\u003eTumor Cell-Free DNA Copy Number Instability Predicts Therapeutic Response to Immunotherapy.\\u003c/strong\\u003e \\u003cem\\u003eClin Cancer Res \\u003c/em\\u003e2017, \\u003cstrong\\u003e23:\\u003c/strong\\u003e5074-5081.\\u003c/li\\u003e\\n\\u003cli\\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: \\u003cstrong\\u003eThe Sequence Alignment/Map format and SAMtools.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2009, \\u003cstrong\\u003e25:\\u003c/strong\\u003e2078-2079.\\u003c/li\\u003e\\n\\u003cli\\u003eKawaguchi S, Higasa K, Shimizu M, Yamada R, Matsuda F: \\u003cstrong\\u003eHLA-HD: An accurate HLA typing algorithm for next-generation sequencing data.\\u003c/strong\\u003e \\u003cem\\u003eHum Mutat \\u003c/em\\u003e2017, \\u003cstrong\\u003e38:\\u003c/strong\\u003e788-797.\\u003c/li\\u003e\\n\\u003cli\\u003eMcGranahan N, Rosenthal R, Hiley CT, Rowan AJ, Watkins TBK, Wilson GA, Birkbak NJ, Veeriah S, Van Loo P, Herrero J, Swanton C: \\u003cstrong\\u003eAllele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution.\\u003c/strong\\u003e \\u003cem\\u003eCell \\u003c/em\\u003e2017, \\u003cstrong\\u003e171:\\u003c/strong\\u003e1259-1271.e1211.\\u003c/li\\u003e\\n\\u003cli\\u003eJurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M: \\u003cstrong\\u003eNetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.\\u003c/strong\\u003e \\u003cem\\u003eJ Immunol \\u003c/em\\u003e2017, \\u003cstrong\\u003e199:\\u003c/strong\\u003e3360-3368.\\u003c/li\\u003e\\n\\u003cli\\u003eChen K, Yang A, Carbone DP, Kanu N, Liu K, Wang R, Nie Y, Shen H, Bai J, Wu L, et al: \\u003cstrong\\u003eSpatiotemporal genomic analysis reveals distinct molecular features in recurrent stage I non-small cell lung cancers.\\u003c/strong\\u003e \\u003cem\\u003eCell Rep \\u003c/em\\u003e2022, \\u003cstrong\\u003e40:\\u003c/strong\\u003e111047.\\u003c/li\\u003e\\n\\u003cli\\u003eRosenthal R, McGranahan N, Herrero J, Taylor BS, Swanton C: \\u003cstrong\\u003eDeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution.\\u003c/strong\\u003e \\u003cem\\u003eGenome Biol \\u003c/em\\u003e2016, \\u003cstrong\\u003e17:\\u003c/strong\\u003e31.\\u003c/li\\u003e\\n\\u003cli\\u003eKim D, Paggi JM, Park C, Bennett C, Salzberg SL: \\u003cstrong\\u003eGraph-based genome alignment and genotyping with HISAT2 and HISAT-genotype.\\u003c/strong\\u003e \\u003cem\\u003eNat Biotechnol \\u003c/em\\u003e2019, \\u003cstrong\\u003e37:\\u003c/strong\\u003e907-915.\\u003c/li\\u003e\\n\\u003cli\\u003eLiao Y, Smyth GK, Shi W: \\u003cstrong\\u003efeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2014, \\u003cstrong\\u003e30:\\u003c/strong\\u003e923-930.\\u003c/li\\u003e\\n\\u003cli\\u003eLove MI, Huber W, Anders S: \\u003cstrong\\u003eModerated estimation of fold change and dispersion for RNA-seq data with DESeq2.\\u003c/strong\\u003e \\u003cem\\u003eGenome Biol \\u003c/em\\u003e2014, \\u003cstrong\\u003e15:\\u003c/strong\\u003e550.\\u003c/li\\u003e\\n\\u003cli\\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: \\u003cstrong\\u003eGene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.\\u003c/strong\\u003e \\u003cem\\u003eProc Natl Acad Sci U S A \\u003c/em\\u003e2005, \\u003cstrong\\u003e102:\\u003c/strong\\u003e15545-15550.\\u003c/li\\u003e\\n\\u003cli\\u003eScire J, Huisman JS, Grosu A, Angst DC, Lison A, Li J, Maathuis MH, Bonhoeffer S, Stadler T: \\u003cstrong\\u003eestimateR: an R package to estimate and monitor the effective reproductive number.\\u003c/strong\\u003e \\u003cem\\u003eBMC Bioinformatics \\u003c/em\\u003e2023, \\u003cstrong\\u003e24:\\u003c/strong\\u003e310.\\u003c/li\\u003e\\n\\u003cli\\u003eAyers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, et al: \\u003cstrong\\u003eIFN-\\u0026gamma;-related mRNA profile predicts clinical response to PD-1 blockade.\\u003c/strong\\u003e \\u003cem\\u003eJ Clin Invest \\u003c/em\\u003e2017, \\u003cstrong\\u003e127:\\u003c/strong\\u003e2930-2940.\\u003c/li\\u003e\\n\\u003cli\\u003eCharoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z: \\u003cstrong\\u003ePan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.\\u003c/strong\\u003e \\u003cem\\u003eCell Rep \\u003c/em\\u003e2017, \\u003cstrong\\u003e18:\\u003c/strong\\u003e248-262.\\u003c/li\\u003e\\n\\u003cli\\u003eYuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L, Luo T, Yan H, Long Z, et al: \\u003cstrong\\u003eCancerSEA: a cancer single-cell state atlas.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2019, \\u003cstrong\\u003e47:\\u003c/strong\\u003eD900-d908.\\u003c/li\\u003e\\n\\u003cli\\u003eH\\u0026auml;nzelmann S, Castelo R, Guinney J: \\u003cstrong\\u003eGSVA: gene set variation analysis for microarray and RNA-seq data.\\u003c/strong\\u003e \\u003cem\\u003eBMC Bioinformatics \\u003c/em\\u003e2013, \\u003cstrong\\u003e14:\\u003c/strong\\u003e7.\\u003c/li\\u003e\\n\\u003cli\\u003eKrueger F, Andrews SR: \\u003cstrong\\u003eBismark: a flexible aligner and methylation caller for Bisulfite-Seq applications.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2011, \\u003cstrong\\u003e27:\\u003c/strong\\u003e1571-1572.\\u003c/li\\u003e\\n\\u003cli\\u003eBie F, Wang Z, Li Y, Guo W, Hong Y, Han T, Lv F, Yang S, Li S, Li X, et al: \\u003cstrong\\u003eMultimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.\\u003c/strong\\u003e \\u003cem\\u003eNat Commun \\u003c/em\\u003e2023, \\u003cstrong\\u003e14:\\u003c/strong\\u003e6042.\\u003c/li\\u003e\\n\\u003cli\\u003eAdalsteinsson VA, Ha G, Freeman SS, Choudhury AD, Stover DG, Parsons HA, Gydush G, Reed SC, Rotem D, Rhoades J, et al: \\u003cstrong\\u003eScalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors.\\u003c/strong\\u003e \\u003cem\\u003eNat Commun \\u003c/em\\u003e2017, \\u003cstrong\\u003e8:\\u003c/strong\\u003e1324.\\u003c/li\\u003e\\n\\u003cli\\u003eLeary RJ, Sausen M, Kinde I, Papadopoulos N, Carpten JD, Craig D, O\\u0026apos;Shaughnessy J, Kinzler KW, Parmigiani G, Vogelstein B, et al: \\u003cstrong\\u003eDetection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing.\\u003c/strong\\u003e \\u003cem\\u003eSci Transl Med \\u003c/em\\u003e2012, \\u003cstrong\\u003e4:\\u003c/strong\\u003e162ra154.\\u003c/li\\u003e\\n\\u003cli\\u003eFeng H, Conneely KN, Wu H: \\u003cstrong\\u003eA Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2014, \\u003cstrong\\u003e42:\\u003c/strong\\u003ee69.\\u003c/li\\u003e\\n\\u003cli\\u003eYu G, Wang LG, He QY: \\u003cstrong\\u003eChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2015, \\u003cstrong\\u003e31:\\u003c/strong\\u003e2382-2383.\\u003c/li\\u003e\\n\\u003cli\\u003eAnastasiadi D, Esteve-Codina A, Piferrer F: \\u003cstrong\\u003eConsistent inverse correlation between DNA methylation of the first intron and gene expression across tissues and species.\\u003c/strong\\u003e \\u003cem\\u003eEpigenetics Chromatin \\u003c/em\\u003e2018, \\u003cstrong\\u003e11:\\u003c/strong\\u003e37.\\u003c/li\\u003e\\n\\u003cli\\u003eXu T, Le TD, Liu L, Su N, Wang R, Sun B, Colaprico A, Bontempi G, Li J: \\u003cstrong\\u003eCancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2017, \\u003cstrong\\u003e33:\\u003c/strong\\u003e3131-3133.\\u003c/li\\u003e\\n\\u003cli\\u003eWilkerson MD, Hayes DN: \\u003cstrong\\u003eConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.\\u003c/strong\\u003e \\u003cem\\u003eBioinformatics \\u003c/em\\u003e2010, \\u003cstrong\\u003e26:\\u003c/strong\\u003e1572-1573.\\u003c/li\\u003e\\n\\u003cli\\u003eLindskrog SV, Prip F, Lamy P, Taber A, Groeneveld CS, Birkenkamp-Demtr\\u0026ouml;der K, Jensen JB, Strandgaard T, Nordentoft I, Christensen E, et al: \\u003cstrong\\u003eAn integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer.\\u003c/strong\\u003e \\u003cem\\u003eNat Commun \\u003c/em\\u003e2021, \\u003cstrong\\u003e12:\\u003c/strong\\u003e2301.\\u003c/li\\u003e\\n\\u003cli\\u003eAndo M, Saito Y, Xu G, Bui NQ, Medetgul-Ernar K, Pu M, Fisch K, Ren S, Sakai A, Fukusumi T, et al: \\u003cstrong\\u003eChromatin dysregulation and DNA methylation at transcription start sites associated with transcriptional repression in cancers.\\u003c/strong\\u003e \\u003cem\\u003eNat Commun \\u003c/em\\u003e2019, \\u003cstrong\\u003e10:\\u003c/strong\\u003e2188.\\u003c/li\\u003e\\n\\u003cli\\u003eXu JY, Zhang C, Wang X, Zhai L, Ma Y, Mao Y, Qian K, Sun C, Liu Z, Jiang S, et al: \\u003cstrong\\u003eIntegrative Proteomic Characterization of Human Lung Adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eCell \\u003c/em\\u003e2020, \\u003cstrong\\u003e182:\\u003c/strong\\u003e245-261.e217.\\u003c/li\\u003e\\n\\u003cli\\u003eHudson KM, Klimczak LJ, Sterling JF, Burkholder AB, Kazanov MD, Saini N, Mieczkowski PA, Gordenin DA: \\u003cstrong\\u003eGlycidamide-induced hypermutation in yeast single-stranded DNA reveals a ubiquitous clock-like mutational motif in humans.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2023, \\u003cstrong\\u003e51:\\u003c/strong\\u003e9075-9100.\\u003c/li\\u003e\\n\\u003cli\\u003eKim Y, Lee B, Shim JH, Lee SH, Park WY, Choi YL, Sun JM, Ahn JS, Ahn MJ, Park K: \\u003cstrong\\u003eConcurrent Genetic Alterations Predict the Progression to Target Therapy in EGFR-Mutated Advanced NSCLC.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2019, \\u003cstrong\\u003e14:\\u003c/strong\\u003e193-202.\\u003c/li\\u003e\\n\\u003cli\\u003eDuan J, Xu J, Wang Z, Bai H, Cheng Y, An T, Gao H, Wang K, Zhou Q, Hu Y, et al: \\u003cstrong\\u003eRefined Stratification Based on Baseline Concomitant Mutations and Longitudinal Circulating Tumor DNA Monitoring in Advanced EGFR-Mutant Lung Adenocarcinoma Under Gefitinib Treatment.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Oncol \\u003c/em\\u003e2020, \\u003cstrong\\u003e15:\\u003c/strong\\u003e1857-1870.\\u003c/li\\u003e\\n\\u003cli\\u003eTakamochi K, Oh S, Matsunaga T, Suzuki K: \\u003cstrong\\u003ePrognostic impacts of EGFR mutation status and subtype in patients with surgically resected lung adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eJ Thorac Cardiovasc Surg \\u003c/em\\u003e2017, \\u003cstrong\\u003e154:\\u003c/strong\\u003e1768-1774.e1761.\\u003c/li\\u003e\\n\\u003cli\\u003eZheng J, Deng Y, Huang B, Chen X: \\u003cstrong\\u003ePrognostic implications of STK11 with different mutation status and its relationship with tumor-infiltrating immune cells in non-small cell lung cancer.\\u003c/strong\\u003e \\u003cem\\u003eFront Immunol \\u003c/em\\u003e2024, \\u003cstrong\\u003e15:\\u003c/strong\\u003e1387896.\\u003c/li\\u003e\\n\\u003cli\\u003eHe Y, Chen X, Liu H, Xiao H, Kwapong WR, Mei J: \\u003cstrong\\u003eMatrix-remodeling associated 5 as a novel tissue biomarker predicts poor prognosis in non-small cell lung cancers.\\u003c/strong\\u003e \\u003cem\\u003eCancer Biomark \\u003c/em\\u003e2015, \\u003cstrong\\u003e15:\\u003c/strong\\u003e645-651.\\u003c/li\\u003e\\n\\u003cli\\u003eLi M, Shi M, Hu C, Chen B, Li S: \\u003cstrong\\u003eMALAT1 modulated FOXP3 ubiquitination then affected GINS1 transcription and drived NSCLC proliferation.\\u003c/strong\\u003e \\u003cem\\u003eOncogene \\u003c/em\\u003e2021, \\u003cstrong\\u003e40:\\u003c/strong\\u003e3870-3884.\\u003c/li\\u003e\\n\\u003cli\\u003eChen Y, Zhou Y, Han F, Zhao Y, Tu M, Wang Y, Huang C, Fan S, Chen P, Yao X, et al: \\u003cstrong\\u003eA novel miR-1291-ERR\\u0026alpha;-CPT1C axis modulates tumor cell proliferation, metabolism and tumorigenesis.\\u003c/strong\\u003e \\u003cem\\u003eTheranostics \\u003c/em\\u003e2020, \\u003cstrong\\u003e10:\\u003c/strong\\u003e7193-7210.\\u003c/li\\u003e\\n\\u003cli\\u003eChen YJ, Roumeliotis TI, Chang YH, Chen CT, Han CL, Lin MH, Chen HW, Chang GC, Chang YL, Wu CT, et al: \\u003cstrong\\u003eProteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression.\\u003c/strong\\u003e \\u003cem\\u003eCell \\u003c/em\\u003e2020, \\u003cstrong\\u003e182:\\u003c/strong\\u003e226-244.e217.\\u003c/li\\u003e\\n\\u003cli\\u003eYu Y, Lin D, Li A, Chen Y, Ou Q, Hu H, Yao H: \\u003cstrong\\u003eAssociation of Immune Checkpoint Inhibitor Therapy With Survival in Patients With Cancers With MUC16 Variants.\\u003c/strong\\u003e \\u003cem\\u003eJAMA Netw Open \\u003c/em\\u003e2020, \\u003cstrong\\u003e3:\\u003c/strong\\u003ee205837.\\u003c/li\\u003e\\n\\u003cli\\u003eLi J, Liu B, Ye Q, Xiao X, Yan S, Guan W, He L, Wang C, Yu Z, Tai Z, et al: \\u003cstrong\\u003eComprehensive genomic analysis of primary malignant melanoma of the esophagus reveals similar genetic patterns compared with epithelium-associated melanomas.\\u003c/strong\\u003e \\u003cem\\u003eMod Pathol \\u003c/em\\u003e2022, \\u003cstrong\\u003e35:\\u003c/strong\\u003e1596-1608.\\u003c/li\\u003e\\n\\u003cli\\u003eYin G, Liu L, Yu T, Yu L, Feng M, Zhou C, Wang X, Teng G, Ma Z, Zhou W, et al: \\u003cstrong\\u003eGenomic and transcriptomic analysis of breast cancer identifies novel signatures associated with response to neoadjuvant chemotherapy.\\u003c/strong\\u003e \\u003cem\\u003eGenome Med \\u003c/em\\u003e2024, \\u003cstrong\\u003e16:\\u003c/strong\\u003e11.\\u003c/li\\u003e\\n\\u003cli\\u003eWang X, Yu X, Krauthammer M, Hugo W, Duan C, Kanetsky PA, Teer JK, Thompson ZJ, Kalos D, Tsai KY, et al: \\u003cstrong\\u003eThe Association of MUC16 Mutation with Tumor Mutation Burden and Its Prognostic Implications in Cutaneous Melanoma.\\u003c/strong\\u003e \\u003cem\\u003eCancer Epidemiol Biomarkers Prev \\u003c/em\\u003e2020, \\u003cstrong\\u003e29:\\u003c/strong\\u003e1792-1799.\\u003c/li\\u003e\\n\\u003cli\\u003eDuan H, Ren J, Wei S, Yang Z, Li C, Wang Z, Li M, Wei Z, Liu Y, Wang X, et al: \\u003cstrong\\u003eIntegrated analyses of multi-omic data derived from paired primary lung cancer and brain metastasis reveal the metabolic vulnerability as a novel therapeutic target.\\u003c/strong\\u003e \\u003cem\\u003eGenome Med \\u003c/em\\u003e2024, \\u003cstrong\\u003e16:\\u003c/strong\\u003e138.\\u003c/li\\u003e\\n\\u003cli\\u003eWang C, Liang H, Lin C, Li F, Xie G, Qiao S, Shi X, Deng J, Zhao X, Wu K, Zhang X: \\u003cstrong\\u003eMolecular Subtyping and Prognostic Assessment Based on Tumor Mutation Burden in Patients with Lung Adenocarcinomas.\\u003c/strong\\u003e \\u003cem\\u003eInt J Mol Sci \\u003c/em\\u003e2019, \\u003cstrong\\u003e20\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eSu C, Wang X, Zhou J, Zhao J, Zhou F, Zhao G, Xu X, Zou X, Zhu B, Jia Q: \\u003cstrong\\u003eTitin mutation in circulatory tumor DNA is associated with efficacy to immune checkpoint blockade in advanced non-small cell lung cancer.\\u003c/strong\\u003e \\u003cem\\u003eTransl Lung Cancer Res \\u003c/em\\u003e2021, \\u003cstrong\\u003e10:\\u003c/strong\\u003e1256-1265.\\u003c/li\\u003e\\n\\u003cli\\u003eJia Q, Wang J, He N, He J, Zhu B: \\u003cstrong\\u003eTitin mutation associated with responsiveness to checkpoint blockades in solid tumors.\\u003c/strong\\u003e \\u003cem\\u003eJCI Insight \\u003c/em\\u003e2019, \\u003cstrong\\u003e4\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eLi J, Xiong S, He P, Liang P, Li C, Zhong R, Cai X, Xie Z, Liu J, Cheng B, et al: \\u003cstrong\\u003eSpatial whole exome sequencing reveals the genetic features of highly-aggressive components in lung adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eNeoplasia \\u003c/em\\u003e2024, \\u003cstrong\\u003e54:\\u003c/strong\\u003e101013.\\u003c/li\\u003e\\n\\u003cli\\u003eLi L, Li J: \\u003cstrong\\u003eCorrelation of tumor mutational burden with prognosis and immune infiltration in lung adenocarcinoma.\\u003c/strong\\u003e \\u003cem\\u003eFront Oncol \\u003c/em\\u003e2023, \\u003cstrong\\u003e13:\\u003c/strong\\u003e1128785.\\u003c/li\\u003e\\n\\u003cli\\u003eLakhani AA, Thompson SL, Sheltzer JM: \\u003cstrong\\u003eAneuploidy in human cancer: new tools and perspectives.\\u003c/strong\\u003e \\u003cem\\u003eTrends Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e39:\\u003c/strong\\u003e968-980.\\u003c/li\\u003e\\n\\u003cli\\u003eIlves I, Petojevic T, Pesavento JJ, Botchan MR: \\u003cstrong\\u003eActivation of the MCM2-7 helicase by association with Cdc45 and GINS proteins.\\u003c/strong\\u003e \\u003cem\\u003eMol Cell \\u003c/em\\u003e2010, \\u003cstrong\\u003e37:\\u003c/strong\\u003e247-258.\\u003c/li\\u003e\\n\\u003cli\\u003eKingsley G, Skagia A, Passaretti P, Fernandez-Cuesta C, Reynolds-Winczura A, Koscielniak K, Gambus A: \\u003cstrong\\u003eDONSON facilitates Cdc45 and GINS chromatin association and is essential for DNA replication initiation.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2023, \\u003cstrong\\u003e51:\\u003c/strong\\u003e9748-9763.\\u003c/li\\u003e\\n\\u003cli\\u003eLiang J, Yao N, Deng B, Li J, Jiang Y, Liu T, Hu Y, Cao M, Hong J: \\u003cstrong\\u003eGINS1 promotes ZEB1-mediated epithelial-mesenchymal transition and tumor metastasis via \\u0026beta;-catenin signaling in hepatocellular carcinoma.\\u003c/strong\\u003e \\u003cem\\u003eJ Cell Physiol \\u003c/em\\u003e2024, \\u003cstrong\\u003e239:\\u003c/strong\\u003ee31237.\\u003c/li\\u003e\\n\\u003cli\\u003eFad\\u0026oacute; R, Zagmutt S, Herrero L, Muley H, Rodr\\u0026iacute;guez-Rodr\\u0026iacute;guez R, Bi H, Serra D, Casals N: \\u003cstrong\\u003eTo be or not to be a fat burner, that is the question for cpt1c in cancer cells.\\u003c/strong\\u003e \\u003cem\\u003eCell Death Dis \\u003c/em\\u003e2023, \\u003cstrong\\u003e14:\\u003c/strong\\u003e57.\\u003c/li\\u003e\\n\\u003cli\\u003eLi J, Zheng W, Wu J, Zhang J, Lv B, Li W, Liu J, Zhang X, Huang T, Luo Z: \\u003cstrong\\u003eCPT1C-mediated fatty acid oxidation facilitates colorectal cancer cell proliferation and metastasis.\\u003c/strong\\u003e \\u003cem\\u003eActa Biochim Biophys Sin (Shanghai) \\u003c/em\\u003e2023, \\u003cstrong\\u003e55:\\u003c/strong\\u003e1301-1309.\\u003c/li\\u003e\\n\\u003cli\\u003eZhao H, Cheng X, Yan L, Mi F, Wang W, Hu Y, Liu X, Fan Y, Min Q, Wang Y, et al: \\u003cstrong\\u003eAPC/C-regulated CPT1C promotes tumor progression by upregulating the energy supply and accelerating the G1/S transition.\\u003c/strong\\u003e \\u003cem\\u003eCell Commun Signal \\u003c/em\\u003e2024, \\u003cstrong\\u003e22:\\u003c/strong\\u003e283.\\u003c/li\\u003e\\n\\u003cli\\u003eMuley H, Valencia K, Casas J, Moreno B, Botella L, Lecanda F, Fad\\u0026oacute; R, Casals N: \\u003cstrong\\u003eCpt1c Downregulation Causes Plasma Membrane Remodelling and Anthracycline Resistance in Breast Cancer.\\u003c/strong\\u003e \\u003cem\\u003eInt J Mol Sci \\u003c/em\\u003e2023, \\u003cstrong\\u003e24\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eMollinedo F: \\u003cstrong\\u003eNeutrophil Degranulation, Plasticity, and Cancer Metastasis.\\u003c/strong\\u003e \\u003cem\\u003eTrends Immunol \\u003c/em\\u003e2019, \\u003cstrong\\u003e40:\\u003c/strong\\u003e228-242.\\u003c/li\\u003e\\n\\u003cli\\u003eShi RY, Zhou N, Xuan L, Jiang ZH, Xia J, Zhu JM, Chen KM, Zhou GL, Yu GP, Zhang J, et al: \\u003cstrong\\u003eTrafficking circuit of CD8(+) T cells between the intestine and bone marrow governs antitumour immunity.\\u003c/strong\\u003e \\u003cem\\u003eNat Cell Biol \\u003c/em\\u003e2024, \\u003cstrong\\u003e26:\\u003c/strong\\u003e1346-1358.\\u003c/li\\u003e\\n\\u003cli\\u003eLin X, Kang K, Chen P, Zeng Z, Li G, Xiong W, Yi M, Xiang B: \\u003cstrong\\u003eRegulatory mechanisms of PD-1/PD-L1 in cancers.\\u003c/strong\\u003e \\u003cem\\u003eMol Cancer \\u003c/em\\u003e2024, \\u003cstrong\\u003e23:\\u003c/strong\\u003e108.\\u003c/li\\u003e\\n\\u003cli\\u003eCui L, Liu R, Han S, Zhang C, Wang B, Ruan Y, Yu X, Li Y, Yao Y, Guan X, et al: \\u003cstrong\\u003eTargeting Arachidonic Acid Metabolism Enhances Immunotherapy Efficacy in ARID1A-Deficient Colorectal Cancer.\\u003c/strong\\u003e \\u003cem\\u003eCancer Res \\u003c/em\\u003e2025, \\u003cstrong\\u003e85:\\u003c/strong\\u003e925-941.\\u003c/li\\u003e\\n\\u003cli\\u003eWu W, Zhou Q, Masubuchi T, Shi X, Li H, Xu X, Huang M, Meng L, He X, Zhu H, et al: \\u003cstrong\\u003eMultiple Signaling Roles of CD3\\u0026epsilon; and Its Application in CAR-T Cell Therapy.\\u003c/strong\\u003e \\u003cem\\u003eCell \\u003c/em\\u003e2020, \\u003cstrong\\u003e182:\\u003c/strong\\u003e855-871.e823.\\u003c/li\\u003e\\n\\u003cli\\u003eVelasco C\\u0026aacute;rdenas RM, Brandl SM, Mel\\u0026eacute;ndez AV, Schlaak AE, Buschky A, Peters T, Beier F, Serrels B, Taromi S, Raute K, et al: \\u003cstrong\\u003eHarnessing CD3 diversity to optimize CAR T cells.\\u003c/strong\\u003e \\u003cem\\u003eNat Immunol \\u003c/em\\u003e2023, \\u003cstrong\\u003e24:\\u003c/strong\\u003e2135-2149.\\u003c/li\\u003e\\n\\u003cli\\u003eLavy M, Gauttier V, Poirier N, Barill\\u0026eacute;-Nion S, Blanquart C: \\u003cstrong\\u003eSpecialized Pro-Resolving Mediators Mitigate Cancer-Related Inflammation: Role of Tumor-Associated Macrophages and Therapeutic Opportunities.\\u003c/strong\\u003e \\u003cem\\u003eFront Immunol \\u003c/em\\u003e2021, \\u003cstrong\\u003e12:\\u003c/strong\\u003e702785.\\u003c/li\\u003e\\n\\u003cli\\u003eSerhan CN: \\u003cstrong\\u003ePro-resolving lipid mediators are leads for resolution physiology.\\u003c/strong\\u003e \\u003cem\\u003eNature \\u003c/em\\u003e2014, \\u003cstrong\\u003e510:\\u003c/strong\\u003e92-101.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"molecular-cancer\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"molc\",\"sideBox\":\"Learn more about [Molecular Cancer](http://gsejournal.biomedcentral.com/)\",\"snPcode\":\"12943\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12943/3\",\"title\":\"Molecular Cancer\",\"twitterHandle\":\"@SN_Oncology\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Multi-omics, poorly differentiated, lung adenocarcinoma\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6082622/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6082622/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntroduction: \\u003c/strong\\u003eEarly-stage poorly differentiated lung adenocarcinoma (LUAD) is plagued by a high risk of postoperative recurrence, and its prognostic heterogeneity complicates treatment and surveillance planning. We conducted this integrative multi-omics study to identify those patients with a truly high risk of adverse outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eWhole-exome, RNA and whole methylome sequencing were carried out on 101 treatment-naïve early-stage poorly differentiated LUADs. Integrated analyses were conducted to disclose molecular characteristics and explore molecular subtyping. Functional validation of key molecules was carried out through \\u003cem\\u003ein vitro\\u003c/em\\u003e and \\u003cem\\u003ein vivo\\u003c/em\\u003e experiments.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults: \\u003c/strong\\u003eRecurrent tumors exhibited significantly higher ploidy (p = 0.024), the fraction of the genome altered (FGA, p = 0.042), and aneuploidy (p = 0.022) compared to non-recurrent tumors, as well as a higher frequency of CNVs. Additionally, recurrent tumors showed hypomethylation at both the global level and in CpG island regions. Integrative transcriptomic and methylation analyses identified three molecular subtypes (C1, C2, and C3), with the C1 subtype presenting the worst prognosis (p = 0.024). Although frequently mutated genes showed similar mutation frequencies across the three subtypes, the C1 subtype exhibited the highest tumor mutation burden (TMB), mutant-allele tumor heterogeneity (MATH), aneuploidy, and HLA loss of heterozygosity (HLA-LOH), along with relatively lower immune cell infiltration. Furthermore,\\u003cem\\u003e GINS1\\u003c/em\\u003eand \\u003cem\\u003eCPT1C\\u003c/em\\u003e were found to promote LUAD progression, and their high expression correlated with a poor prognosis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions: \\u003c/strong\\u003eThis multi-omics study identified three integrative subtypes with distinct prognostic implications, paving the way for more precise management and postoperative monitoring of early-stage poorly differentiated LUAD.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Multi‑omics analysis identifies different molecular subtypes with unique outcomes in early-stage poorly differentiated lung adenocarcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-04-10 10:13:59\",\"doi\":\"10.21203/rs.3.rs-6082622/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Accepted\",\"date\":\"2025-04-12T23:49:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-04-11T18:05:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"61074628277517336359922393838119258891\",\"date\":\"2025-04-10T14:16:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"259963128812973292494820745730320625016\",\"date\":\"2025-04-08T03:54:59+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-04-08T03:52:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-04-07T09:18:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Molecular Cancer\",\"date\":\"2025-04-05T07:15:55+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"molecular-cancer\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"molc\",\"sideBox\":\"Learn more about [Molecular Cancer](http://gsejournal.biomedcentral.com/)\",\"snPcode\":\"12943\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12943/3\",\"title\":\"Molecular Cancer\",\"twitterHandle\":\"@SN_Oncology\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0b772b97-b3a4-49c7-bc97-c9bc50be8cfd\",\"owner\":[],\"postedDate\":\"April 10th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-05-05T16:04:39+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6082622\",\"link\":\"https://doi.org/10.1186/s12943-025-02333-7\",\"journal\":{\"identity\":\"molecular-cancer\",\"isVorOnly\":false,\"title\":\"Molecular Cancer\"},\"publishedOn\":\"2025-05-01 15:57:13\",\"publishedOnDateReadable\":\"May 1st, 2025\"},\"versionCreatedAt\":\"2025-04-10 10:13:59\",\"video\":\"\",\"vorDoi\":\"10.1186/s12943-025-02333-7\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12943-025-02333-7\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6082622\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6082622\",\"identity\":\"rs-6082622\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}