Pan-Cancer Analysis of the Prognostic and Immunological Role of ECT2: A Promising Target for Survival and Immunotherapy

Pan-Cancer Analysis of the Prognostic and Immunological Role of ECT2: A Promising Target for Survival and Immunotherapy | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 3 March 2025 V1 Latest version Share on Pan-Cancer Analysis of the Prognostic and Immunological Role of ECT2: A Promising Target for Survival and Immunotherapy Authors : Lulu Wang 0009-0006-2325-0733 , Hua Jin , Xiaowei Liu [email protected] , and Hanzhi Zhang Authors Info & Affiliations https://doi.org/10.22541/au.174100364.44374556/v1 Published Cancer Informatics Version of record Peer review timeline 236 views 135 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The investigation of immune-related biomarkers for tumor immunotherapy represents a rapidly evolving and highly promising research frontier. While the ECT2 gene has been implicated in the progression of diverse malignancies, its pan-cancer implications and underlying molecular mechanisms remain insufficiently characterized. Building upon emerging evidence that suggests an association between ECT2 and tumor pathogenesis, our study endeavors to comprehensively elucidate the prognostic significance and immunological attributes of ECT2 in oncogenesis. Through integrative analysis of comprehensive genomic datasets derived from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) repositories, we systematically examined the oncogenic potential of ECT2 across human malignancies. Our findings demonstrate substantial upregulation of ECT2 expression in 31 distinct tumor types, with pan-cancer Cox regression analyses revealing statistically significant correlations between elevated ECT2 expression and adverse clinical outcomes, including reduced overall survival (OS), disease-specific survival (DSS), disease-free interval (DFS), and progression-free interval (PFI) across multiple cancer subtypes. To further validate the functional implications of ECT2 in tumor biology, we employed hepatocellular carcinoma (HCC) cell line HepG2 as an experimental model. Utilizing shRNA-mediated gene silencing, we observed marked reduction in cellular viability upon ECT2 knockdown. Immunofluorescence analyses corroborated these findings, demonstrating diminished expression of cell cycle regulatory proteins, particularly Cyclin D1, following ECT2 suppression. RNA sequencing analysis of ECT2-depleted HepG2 cells identified significant enrichment of differentially expressed genes in cell cycle regulation and proliferative signaling pathways, providing mechanistic insights into ECT2’s tumor-promoting effects. Our comprehensive pan-cancer analysis further revealed significant associations between ECT2 expression patterns and key immunological parameters, including immune cell infiltration, immune checkpoint molecule expression, tumor mutational burden (TMB), and microsatellite instability (MSI) status across various malignancies. Functional pathway analysis implicated ECT2 in critical mitotic cell cycle processes, offering novel perspectives on its role in cancer initiation and metastatic progression across diverse tumor types. These findings collectively contribute to our understanding of ECT2 as a potentially critical regulator in oncogenesis, with implications for both tumor biology and therapeutic development. The multifaceted role of ECT2 in cancer progression, as revealed through our integrative analysis, underscores its potential as both a prognostic biomarker and therapeutic target in cancer immunotherapy. Pan-Cancer Analysis of the Prognostic and Immunological Role of ECT2: A Promising Target for Survival and Immunotherapy Lulu Wang 1,2,3 , Hua Jin 1,2,3 , Xiaowei Liu * 4 , Hanzhi Zhang * 1,2,3 1 Department of General Practice, Yangpu Hospital, Tongji University School of Medicine, Shanghai, 200000. Dr. China. 2 Shanghai General Practice and Community Health Development Research Center, Shanghai, 200000. Dr. China. 3 Research Center for General Practice，School of Medicine, Tongji University, Shanghai, 200000. Dr. China. 4 Department of Spinal Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092. Dr. China. Co-first authors： Lulu Wang（ [email protected] ），Hua Jin（ [email protected] ） Correspondence to: *Dr Xiaowei Liu, Department of Spinal Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Jimo Road, Shanghai, 200092. Dr. China. E‑mail: [email protected] . *Dr Hanzhi Zhang, Department of General Practice, Yangpu Hospital, Tongji University School of Medicine, Tengyue Road, Shanghai, 200000. Dr. China. E‑mail: [email protected] . Abbreviated running title: Pan-Cancer Analysis of ECT2. Abstract The investigation of immune-related biomarkers for tumor immunotherapy represents a rapidly evolving and highly promising research frontier. While the ECT2 gene has been implicated in the progression of diverse malignancies, its pan-cancer implications and underlying molecular mechanisms remain insufficiently characterized. Building upon emerging evidence that suggests an association between ECT2 and tumor pathogenesis, our study endeavors to comprehensively elucidate the prognostic significance and immunological attributes of ECT2 in oncogenesis. Through integrative analysis of comprehensive genomic datasets derived from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) repositories, we systematically examined the oncogenic potential of ECT2 across human malignancies. Our findings demonstrate substantial upregulation of ECT2 expression in 31 distinct tumor types, with pan-cancer Cox regression analyses revealing statistically significant correlations between elevated ECT2 expression and adverse clinical outcomes, including reduced overall survival (OS), disease-specific survival (DSS), disease-free interval (DFS), and progression-free interval (PFI) across multiple cancer subtypes. To further validate the functional implications of ECT2 in tumor biology, we employed hepatocellular carcinoma (HCC) cell line HepG2 as an experimental model. Utilizing shRNA-mediated gene silencing, we observed marked reduction in cellular viability upon ECT2 knockdown. Immunofluorescence analyses corroborated these findings, demonstrating diminished expression of cell cycle regulatory proteins, particularly Cyclin D1, following ECT2 suppression. RNA sequencing analysis of ECT2-depleted HepG2 cells identified significant enrichment of differentially expressed genes in cell cycle regulation and proliferative signaling pathways, providing mechanistic insights into ECT2’s tumor-promoting effects. Our comprehensive pan-cancer analysis further revealed significant associations between ECT2 expression patterns and key immunological parameters, including immune cell infiltration, immune checkpoint molecule expression, tumor mutational burden (TMB), and microsatellite instability (MSI) status across various malignancies. Functional pathway analysis implicated ECT2 in critical mitotic cell cycle processes, offering novel perspectives on its role in cancer initiation and metastatic progression across diverse tumor types. These findings collectively contribute to our understanding of ECT2 as a potentially critical regulator in oncogenesis, with implications for both tumor biology and therapeutic development. The multifaceted role of ECT2 in cancer progression, as revealed through our integrative analysis, underscores its potential as both a prognostic biomarker and therapeutic target in cancer immunotherapy. Key Words ECT2, pan-cancer, prognostic biomarker, methylation, immune checkpoint, immune infiltration, cell cycle 1.Introduction Human carcinogenesis is widely recognized as a complex, multistep process involving the dysregulation of critical cellular mechanisms. This process is primarily mediated through the functional aberrations of proto-oncogenes, tumor suppressor genes, and other crucial regulatory genes that orchestrate essential cellular processes, including proliferation, differentiation, and maintenance of genomic integrity (1-3). Emerging evidence increasingly suggests a bidirectional interplay between genetic and epigenetic alterations in carcinogenesis. Genetic modifications have been demonstrated to disrupt various epigenetic patterns, while conversely, epigenetic modifications can drive genomic instability and promote mutational events. This reciprocal interaction highlights the complex crosstalk between these two fundamental mechanisms during malignant transformation (4-6). Consequently, there is an urgent and compelling need to identify novel therapeutic targets and discover highly sensitive tumor biomarkers to enhance the precision of cancer diagnosis and optimize therapeutic strategies (7, 8). The human ECT2 gene product is an 883-amino acid polypeptide, with a theoretical molecular weight estimated at approximately 104 kilodaltons (kDa) (9). The functional regulation of Ect2 is mediated by a multifaceted network of molecular mechanisms, encompassing post-translational modifications (particularly phosphorylation), dynamic subcellular localization, and complex intramolecular as well as intermolecular interactions (10, 11). The Ect2 gene (Ect2) is located on chromosome 3q26, a region frequently chromosomally altered in human tumors(12, 13). Ect2 overexpression in human malignancies is primarily attributed to tumor-specific gene amplification. This molecular aberration has been observed across a spectrum of human cancers, with notably elevated Ect2 expression documented in tumors originating from diverse anatomical sites, including the brain, lung, bladder, esophagus, liver, colorectum, breast, pancreas, and ovaries (14-20). Thus, emerging evidence suggests that ECT2 plays a pivotal role not only in mediating cell proliferation but also in the pathogenesis of diverse human brain malignancies. However, critical knowledge gaps remain regarding the comprehensive tumorigenic mechanisms of ECT2 across various cancer types. Furthermore, the potential molecular mechanisms underlying ECT2-mediated immunomodulation in the tumor microenvironment and its influence on therapeutic responsiveness remain poorly characterized. Consequently, elucidating these aspects through systematic pan-cancer investigations represents a scientifically justified and clinically relevant research direction. Our study represents the first comprehensive pan-cancer analysis of ECT2, leveraging multi-omics data from both the TCGA (The Cancer Genome Atlas) project and GEO (Gene Expression Omnibus) database. Through an integrative analytical approach, we systematically investigated multiple molecular dimensions, including but not limited to: transcriptional regulation, prognostic associations, epigenetic modifications (DNA methylation), genomic alterations, post-translational modifications (protein phosphorylation), tumor immune microenvironment characteristics, and associated signaling pathways. This multi-faceted investigation aims to elucidate the potential mechanistic roles of ECT2 in tumorigenesis and its clinical implications across various malignancies. 2.Materials and methods 2.1 ECT2 Expression Pattern in Human Pan-Cancer PROTTER (version 1.0; accessible at https://wlab.ethz.ch/protter/start/) is an advanced, interactive web-based platform that facilitates comprehensive visualization and analysis of protein sequence annotations. This innovative tool enables researchers to integrate and map diverse protein features, including sequence annotations, predicted functional domains, and empirically identified proteomic data encompassing peptide sequences and post-translational modifications (PTMs), onto the topological representation of transmembrane protein structures. The platform offers customizable visualization parameters, allowing for detailed exploration of protein features within their spatial and topological context (21). Genecard database (https://www.genecards.org/) is a searchable comprehensive database that provides comprehensive, user-friendly information on all annotated and predicted human genes(22). HPA (The Human Protein Atlas) database (https://www.proteinatlas.org/) is an integration of various omics technologies, including antibody-based imaging, mass spec-based proteomics, transcriptomics, and systems biology, to map all humans in cells, tissues, and organs protein (23, 24). We downloaded the unified and standardized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database: TCGA TARGET GTEx (PANCAN, N=19131, G=60499), and further we extracted ENSG00000114346 (ECT2) gene expression data in each sample, we further screened the sample sources: Solid Tissue Normal, Primary Solid Tumor, Primary Tumor, Normal Tissue, Primary Blood Derived Cancer-Bone Marrow, Primary Blood Derived Cancer-Peripheral Blood samples, and further log2(x+0.001) transformation was performed on each expression value. Finally, we also eliminated the cancer species with less than 3 samples in a single cancer species, and finally obtained the expression data of 34 cancer species. We used R software (Version 3.6.4) to calculate the expression differences between normal and tumor samples in each tumor, and analyzed the significance of the differences using unpaired Wilcoxon Rank Sum and Signed Rank Tests. 2.2 Prognostic Analysis We downloaded the unified and standardized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database: TCGA TARGET GTEx (PANCAN, N=19131, G=60499), and further we extracted ENSG00000114346 (ECT2) gene expression data in each sample, we further screened the sample sources: Primary Blood Derived Cancer-Peripheral Blood, Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood Derived Cancer-Bone Marrow, Primary Samples from Solid Tumor, Recurrent Blood Derived Cancer-Bone Marrow, and we also draw from a TCGA prognosis study previously published in Cell obtained a high-quality TCGA prognostic data set(25), obtained TARGET follow-up data from UCSC’s Cancer Browser (https://xenabrowser.net/datapages/) as a supplement and the follow-up time was shorter than For the 30-day sample, we further performed log2(x+0.001) transformation on each expression value. Finally, we also eliminated the cancer types with less than 10 samples in a single cancer type. Finally, 44 cancer types and overall survival rates, 38 cancer types and disease-specific survival data, disease-free interval data for 32 cancer types, and progression-free time interval for 38 cancer types were obtained. We used the coxph function of the R package survival (version 3.2-7) to establish a Cox proportional hazards regression model to analyze the relationship between gene expression and prognosis in each tumor, and used the Logrank test to perform statistical tests to obtain prognostic significance. 2.3 Clinical stages We downloaded the unified and standardized pan-cancer dataset from the UCSC (https://xenabrowser.net/) database: TCGA Pan-Cancer (PANCAN, N=10535, G=60499), and further we extracted ENSG00000114346 (ECT2) gene expression data in each sample, we further screened the samples from Primary Blood Derived Cancer-Peripheral Blood and Primary Tumor, and further performed log2(x+0.001) transformation for each expression value, Finally, we also excluded cancer types with less than 3 samples in a single cancer type, and finally obtained the expression data of 37 cancer types. We used R software (version 3.6.4) to calculate the gene expression data of each tumor in different clinical settings. For the expression differences in staged samples, use unpaired Student’s t-Test for significant difference analysis between pairs, and use analysis of variance to test for differences in multiple groups of samples. 2.4 Pan-Cancer Analysis of the Correlation of the ECT2 Expression with Tumor Cell Infiltration and Immune Modulator Genes We downloaded the unified and standardized pan-cancer dataset from the UCSC database, and further we extracted ENSG00000114346 (ECT2) genes and 60 marker genes of two types of immune checkpoint pathway genes and 150 marker genes of five types of immune pathways in each sample. We further screened the sample sources as follows: Primary Solid Tumor, Primary Tumor, Primary Blood Derived Cancer-Bone Marrow, Primary Blood Derived Cancer-Peripheral Blood samples, we also filtered all normal samples, and further performed log2(x+0.001) transformation on each expression value, and then we calculated ENSG00000114346 (ECT2) and pearson correlation of marker genes of five immune pathways. 2.5 Pan-Cancer Analysis of the Relationship between the ECT2 Gene Expression and TMB, MSI, tumor stemness scores We downloaded the unified and standardized pan-cancer dataset from the UCSC database, and further we extracted ENSG00000114346 (ECT2) gene expression data in each sample, we further screened the samples from Primary Blood Derived Cancer Peripheral Blood and Primary Tumor. In addition, we also obtained data from GDC (https://portal.gdc.cancer.gov/) downloaded the Simple Nucleotide Variation dataset of level 4 of all TCGA samples processed by MuTect2 software(26), and we calculated the TMB of each tumor using the TMB function of the R package maftools (version 2.8.05). We obtained the MSI (Microsatellite instability) score of each tumor from previous studies(27). We integrated the TMB, MSI and gene expression data of the samples, and further performed log2(x+0.001) transformation on each expression value, and finally we also eliminated the number of samples in a single cancer species less than 3 of cancer types, and finally obtained the expression data of 37 cancer types. We calculated tumor stemness scores from methylation signatures for each tumor obtained from previous studies(28), we integrated the stemness index and gene expression data of the samples, and further performed log2(x+0.001 for each expression value) ) transformation, and finally we also eliminated the cancer species with less than 3 samples in a single cancer species, and finally obtained the expression data of 37 cancer species. 2.6 RNA modification gene analysis We downloaded the unified and standardized pan-cancer dataset from the UCSC database, and further we extracted ENSG00000114346 (ECT2) gene and three types of RNA modified m1A, m5C, m6A gene marker gene expression data in each sample, we further screened the sample sources: Primary Solid Tumor, Primary Tumor, Primary Blood Derived Cancer-Bone Marrow, Primary Blood Derived Cancer-Peripheral Blood samples, we also filtered all normal samples, and further performed log2(x+0.001) transformation on each expression value, then we calculated ENSG00000114346 (ECT2) and RNA modifier genes pearson correlation. 2.7 Differences in Tumor Microenvironment We downloaded the unified and standardized pan-cancer dataset from the UCSC database, and further we extracted ENSG00000114346 (ECT2) gene expression data in each sample, we further screened the sample sources: Primary Blood Derived Cancer Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood Derived Cancer-Bone Marrow, Primary Solid Tumor, Recurrent Blood Derived Cancer-Bone Marrow samples, further log2(x+0.001) transformation was performed on each expression value, in addition, we also extracted the gene expression profile of each tumor respectively, and mapped the expression profile. On the Gene Symbol, the xCell method(29), the quantiseq method(30) and the Timer method(31) of the R software package IOBR were further used to quantify the immune cell infiltration score of pan-cancer patients, and calculate the correlation between the degree of immune cells and the expression of ECT2. 2.8 Tumor-Immune System Interaction Database (TISIDB) and Tumor Immune Single Cell Hub Database (TISCH) TISIDB is an online database of tumor-immune system interactions(32). In the present study, we used TISIDB to determine the expression of ECT2 in 29 tumors was significantly different in immune type C1 (wound healing), C3 (inflammatory), C2 (IFN-gamma dominant), C4 (lymphocyte depleted), and C6 (TGF-beta dominant). The Tumor Immune Single-Cell Center (TISCH, http://tisch.comp-genomics.org) is an online database focusing on the tumor microenvironment (TME) (33). 2.9 Functional enrichment and PPI network analysis We used the GPS-Prot algorithm(http://gpsprot.org/index.php) to conduct the PPI network analysis of ECT2. Linkedomics database (http://www.linkedomics.org/admin.php) is publicly available portal that includes multi-omics data from all 32 TCGA Cancer types and 10 Clinical Proteomics Tumor Analysis Consortium (CPTAC) cancer cohorts. The General Repository of Biological Interaction Datasets (BioGRID) (https://thebiogrid.org) is a public database for archiving and disseminating genetic and protein interaction data in model organisms and humans(34, 35). The Protein Interaction Network Analysis (PINA) (https://omics.bjcancer.org/pina/.) platform is a comprehensive platform for protein interaction network construction, filtering, analysis, visualization and management (36). GRNdb is a freely accessible and user-friendly database for easy exploration and visualization of predicted regulatory networks formed by transcription factors (TF) (37, 38). GSCA Lite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) is an integrated genomic, and immunogenomic web-based platform for gene set cancer research(39). 2.10 Genetic alterations muTarget database (https://www.mutarget.com/) is publicly available portal that links somatic mutations and gene expression to identify biomarkers and potential therapeutic targets in different types of solid tumors(40). The UALCAN database (http://ualcan.path.uab.edu/analysis.html) was used to investigate the methylation level and phosphorylation of ECT2 between different cancers and corresponding adjacent tissues. In addition, we used the cBioPortal for Cancer Genomics (http:// www.cbioportal.org/) to assess gene mutation co-occurrence patterns between ECT2 signatures and other proteins across patients from TCGA Pan-Cancer Atlas Studies(41). 2.11. Cell Culture and Lentivirus-Mediated ECT2 Gene Silencing The human hepatocellular carcinoma cell line HepG2 was obtained from the Chinese Academy of Sciences and maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gemini Bio Products). Cells were cultured in a humidified incubator at 37°C with 5% CO 2 atmosphere. For ECT2 gene silencing, lentiviral particles containing complementary oligonucleotide sequences targeting the specific sequence (5′-GCCTCAGATTGAAACAAGA-3′) were synthesized by Genomeditech. Following viral transduction, cells were selected using 2 μg/ml puromycin (cat. no. ST551; Beyotime Biotechnology) for 72 hours to establish stable knockdown cell lines. 2.12 Cell Viability Assay Cell viability was assessed using the Cell Counting Kit-8 (CCK-8; Yeasen Biotechnology) according to the manufacturer’s protocol. Briefly, cells were seeded in 96-well plates at a density of 5×10^3 cells per well and incubated for 4 days to allow cell attachment. Following the incubation period, CCK-8 reagent was added to each well and further incubated according to the manufacturer’s instructions. The optical density (OD) of each well was measured using a microplate reader at dual wavelengths of 450 nm (measurement) and 630 nm (reference). 2.13 Immunofluorescence Microscopy Following a 48-hour incubation period, cells were fixed with 4% paraformaldehyde for 15 minutes at 4°C. Subsequently, cells were permeabilized with Triton X-100 and blocked with 5% goat serum (P0096, C0265; Beyotime Biotechnology) to prevent non-specific binding. Primary antibody incubation was performed overnight at 4°C. Following thorough washing, samples were incubated with fluorescence-conjugated secondary antibodies: either FITC-labeled goat anti-rabbit IgG (AS011; ABclonal) or Cy3-conjugated goat anti-rabbit IgG (AS007; ABclonal) for 1 hour at room temperature in light-protected conditions. Nuclei were counterstained with DAPI (C1006; Beyotime Biotechnology) for 5 minutes under dark conditions. Fluorescent images were acquired using a Leica DMI 6000B fluorescence microscope (Leica Microsystems, Germany) with consistent acquisition parameters. Quantitative image analysis was performed using ImageJ software (NIH, USA). 2.14 Quantitative Real-Time PCR Analysis Total RNA extraction was performed using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following the manufacturer’s protocol. Subsequently, RNA was reverse transcribed into cDNA using the PrimeScript RT reagent kit (cat. no. RR036A; Takara Bio, Inc.) according to the manufacturer’s instructions. Quantitative real-time PCR (qPCR) was conducted using SYBR Green Master Mix (cat. no. 11198ES03; Yeasen Biotechnology) on the QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). Relative mRNA expression levels were normalized to GAPDH and calculated using the comparative 2 −ΔΔCq method. 2.15. Statistical Analysis Statistical analysis was performed using the Sangerbox tools, a free online platform for data analysis (http://www.sangerbox.com/tool)(42) The differential expression levels of ECT2 between cancerous and adjacent normal tissues were statistically evaluated using independent two-sample t-tests. To assess the prognostic significance of ECT2, hazard ratios (HRs) and corresponding p-values were derived from univariate Cox proportional hazards regression models. Patients were stratified into distinct cohorts based on high and low ECT2 expression levels, and their survival outcomes were analyzed using the Kaplan-Meier method with log-rank testing. A significance threshold of p<0.05 was uniformly applied across all statistical analyses to determine statistical relevance. 3. Results 3.1. ECT2 variants, localization, single-cell variations, and expression profiles under physiological conditions To investigate the subcellular localization of ECT2, we employed indirect immunofluorescence staining to analyze its distribution within the endoplasmic reticulum (ER) and microtubule networks in three distinct cancer cell lines: A549 lung carcinoma cells, U-251 malignant glioblastoma (MG) cells, and U-2 osteosarcoma (OS) cells, which were obtained from the Human Protein Atlas (HPA) database. Our immunofluorescence analysis revealed that ECT2 exhibited co-localization with both nuclear and cytoplasmic markers in all three cell lines (A549, U-251 MG, and U-2 OS), demonstrating its dual localization in the nucleus and cytoplasm. Notably, we observed no significant co-localization of ECT2 with ER or microtubule markers in any of the analyzed cell lines (Figure 1A) . Complementary to the localization studies, our transcriptomic analysis demonstrated widespread expression of ECT2 messenger RNA across diverse normal human tissues, encompassing immune, internal, nervous system, secretory, muscular, and reproductive systems (Figure 1B) . Structural analysis of the ECT2 protein revealed that naturally occurring missense variants at Ser15 and Thr833 are localized within the cell membrane (Figure 1C) . To further explore the functional implications of ECT2 expression, we analyzed single-cell RNA sequencing data from fluorescent ubiquitin-based cell-cycle indicator (FUCCI) U-2 OS cells. These results demonstrated cell cycle-dependent regulation of ECT2 RNA expression, with protein expression patterns correlating closely with the progression through G1, S, and G2 phases (Figure 1D) . Additionally, gene-disease network interaction analysis identified multiple functional partners of ECT2, suggesting its involvement in complex cellular pathways (Figure 1E) . 3.2. ECT2 is aberrantly overexpressed and is associated with tumor stages, metastases, and poor cancer prognoses We used R software to calculate the difference in expression between normal and tumor samples in each tumor. Using unpaired Wilcoxon Rank Sum and Signed Rank Tests for significance analysis, we observed ECT2 in 31 tumors Significantly up-regulated such as GBM, GBMLGG, LGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, KIRP, KIPAN, COAD, READ, PRAD, STAD, HNSC, KIRC, LUSC, LIHC, WT, SKCM, BLCA, READ, OV, PAAD, TGCT, UCS, ALL, LAML, ACC, CHOL (Figure 2A). We used the coxph function of the R package survival to build a Cox proportional hazards regression model to analyze the relationship between gene expression and prognosis in each tumor, using Logrank test was used to perform statistical test to obtain prognostic significance, and finally observed in 14 tumor types GBMLGG, LGG, LAML, LUAD, KIRP, KIPAN, PRAD, LIHC, MESO, PAAD, LAML, PCPG, ACC, KICH with high ECT2 expression has poor overall survival, and in 1 tumor type OV, low expression has poor prognosis (Figure 2B). In 14 tumor types GBMLGG, LGG, LUAD, ESCA, KIRP, KIPAN, PRAD, KIRC, LIHC, MESO, PAAD, PCPG, ACC, KICH with high ECT2 expression has poor disease-specific survival (Supplementary Figure 1A), and in 1 tumor type OV, low expression has poor prognosis. In 4 tumor types CESC, LUAD, LIHC, PAAD medium and high ECT2 expression has poor disease-free interval (Supplementary Figure 1B). In 14 tumor types GBMLGG, LGG, LUAD, ESCA, KIRP, KIPAN, PRAD, LIHC, SKCM-P, MESO, PAAD, PCPG, ACC, KICH medium and high ECT2 expression has poor progression-free interval (Supplementary Figure 1C). The CPTAC database results showed that the expression levels of the total ECT2 protein were escalated in primary tissues of breast cancer, colon cancer, uterine corpus endometrial carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma, head and neck squamous carcinoma, glioblastoma multiforme, hepatocellular carcinoma, compared with normal tissues (Figure 2C). Supplementary Figure 2A showed that the expression of ECT2 in PRAD, PAAD and UCS patients was significantly different in pathological T1/T2/T3/T4stage. Supplementary Figure 2B indicated that ECT2 expression in GBMLGG, LGG, PRAD, THCA and MESO was significantly different in the pathological M0/M1 stage. Supplementary Figure 2C showed that the expression of ECT2 in HNSC, UCS and CHOL patients was significantly different in pathological N0/N1/N2/N3 stage. Supplementary Figure 2D indicated that ECT2 expression in PRAD, THCA and UCS was significantly different in the pathological I/ II/ III/ IV stage. Supplementary Figure 2E indicated that ECT2 expression in BRCA and ESCA was significantly different in the pathological G1/G2/G3 stage. 3.Genetic alteration analysis data. Subsequently, we conducted a comprehensive genomic analysis of ECT2 across multiple malignant tumor types. In the TCGA pan-cancer cohort, gene amplification emerged as the predominant DNA alteration pattern. As illustrated in Figure 3A, the amplification events were predominantly observed in Lung Squamous Cell Carcinoma (LUSC), followed by Ovarian Serous Cystadenocarcinoma (OV) and Esophageal Adenocarcinoma (ESCA). Notably, ECT2 mutations were specifically identified in Uterine Corpus Endometrial Carcinoma (UCEC), Skin Cutaneous Melanoma (SKCM), and Colorectal Adenocarcinoma (COAD). Deep deletion of ECT2 was primarily detected in Stomach Adenocarcinoma (STAD). Figure 3B comprehensively demonstrates the spectrum of genetic alterations in ECT2, detailing mutation types, specific sites, and corresponding case numbers. Missense mutations constituted the predominant variant class, with the D320A/N/Y alteration being the most recurrent mutation, identified in three UCEC cases. The structural implications of the D320A/N/Y mutation site were further visualized in the three-dimensional protein structure of ECT2 (Figure 3C). Our subsequent correlation analysis revealed a significant association between specific genetic alterations and ECT2 expression levels. Both gene amplification and mutation status demonstrated substantial correlations with RNA expression profiles (Figure 3D). Statistical analyses confirmed that both Mutation Type and Copy Number alterations were significantly dependent on ECT2 expression levels (Figure 3E, F), suggesting that genetic variations may underlie the observed elevated expression of ECT2 in malignant tissues. Clinically, patients with ECT2 genetic alterations exhibited significantly poorer outcomes across multiple survival parameters compared to those without alterations. Specifically, we observed worse overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) in the ECT2 alteration group (Figure 3G-J). It is noteworthy that genetic mutations can exert pleiotropic effects beyond single protein alterations, potentially modulating the transcriptional landscape of entire signaling pathways. Through integrative analysis of somatic mutations and gene expression profiles, our findings may facilitate the identification of novel biomarkers and potential therapeutic targets across various solid tumor types (40, 43, 44). More than 50% of human tumors carry mutations in the TP53 gene, the most frequently mutated gene in human cancers(45). Through comprehensive genomic analysis, we identified significant associations between ECT2 gene expression alterations and key tumor suppressor gene mutations. Specifically, the expression profile of ECT2 demonstrated a robust correlation with TP53 somatic mutations (Supplementary Figure 3A). Furthermore, our investigation revealed statistically significant associations between ECT2 expression levels and mutations in two other critical tumor suppressor genes: PTEN and RB1 (Supplementary Figure 3B, C). These findings suggest potential functional interactions between ECT2 and these fundamental cellular regulatory pathways in the context of tumorigenesis. 4. Pan-Cancer Analysis of the DNA Methylation, RNA Modification and the Phosphorylation of ECT2 DNA methylation, a reversible modification, is a major driver of tumorigenesis because both normal DNA methylation patterns and global changes in methylation levels in regulatory regions are disrupted during the early stages of tumorigenesis(46-48). In this context, promoter methylation profiling has been repeatedly proposed as a biomarker for diagnosis and development of cancer treatment strategies(49). Through comprehensive analysis of the UALCAN database, we investigated the promoter methylation status of ECT2 across various cancer types. Our findings revealed a distinct pattern of hypomethylation in testicular germ cell tumors (TGCT), sarcoma (SARC), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), and colorectal adenocarcinoma (COAD) (Figure 4A). Conversely, hypermethylation was prominently observed in prostate adenocarcinoma (PRAD), hepatocellular carcinoma (LIHC), and kidney renal papillary cell carcinoma (KIRP) (Figure 4B). These differential methylation patterns strongly suggest that DNA methylation may serve as a critical epigenetic mechanism regulating the abnormal expression of ECT2 in tumorigenesis. Emerging evidence from numerous studies has highlighted the pivotal role of m6A RNA methylation regulators in the pathogenesis of various human diseases. Notably, these epigenetic regulators have been implicated in diverse pathological conditions, including nonalcoholic fatty liver disease (NAFLD), azoospermia, heart failure, and most significantly, various human malignancies. The expanding understanding of m6A methylation in cancer biology underscores its potential as a therapeutic target across multiple disease states (50). Detailed genomic analysis revealed significant positive correlations between ECT2 expression and multiple RNA modification-related genes across various modification types, including m1A, m5C, and m6A methylation pathways. Notably, key RNA methyltransferase components such as METTL3, METTL14, WTAP, and the m6A reader protein YTHDF1 demonstrated strong positive associations with ECT2 expression levels (Figure 4C). These collective findings suggest that ECT2 expression may be predominantly regulated through mechanisms of RNA post-transcriptional modification. Furthermore, post-translational modification (PTM) has been identified as a crucial molecular mechanism for ECT2 activation. Previous research by Qi Zhang et al. elucidated a complex regulatory network involving ECT2, demonstrating that ECT2 and the deubiquitinating enzyme USP7 form a reciprocal positive feedback loop. This regulatory mechanism facilitates ECT2 activation through promoting intermolecular self-association, thereby enhancing its oncogenic functions (51). To investigate the role of ECT2 phosphorylation in tumorigenesis, we conducted a comparative analysis of ECT2 phosphorylation levels between primary tumor tissues and adjacent normal tissues by utilizing the CPTAC (Clinical Proteomic Tumor Analysis Consortium) database. Our analysis encompassed six major cancer types: breast invasive carcinoma (BRCA), clear cell renal cell carcinoma (KIRC), colorectal adenocarcinoma (COAD), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), and uterine corpus endometrial carcinoma (UCEC).The in-depth analysis through the CPTAC database revealed significant alterations in ECT2 phosphorylation patterns across various malignancies. Notably, head and neck squamous cell carcinoma (HNSCC) tissues exhibited elevated phosphorylation levels at multiple sites (S367, T373, S442, S443, T444, T857, S858, S861) compared to normal tissues. Furthermore, we observed distinct phosphorylation profiles in specific cancer types: increased T373 phosphorylation in breast cancer, marked elevation of T359 phosphorylation in pancreatic adenocarcinoma, substantial upregulation of both T359 and S866 phosphorylation in hepatocellular carcinoma, and significant enhancement of S858 and T359 phosphorylation in lung adenocarcinoma (Supplementary Figure 4).These comprehensive findings strongly suggest that site-specific phosphorylation of ECT2 plays a pivotal role in tumor biology and may contribute to oncogenic processes across multiple cancer types. The observed phosphorylation patterns indicate potential molecular mechanisms underlying tumor development and progression, highlighting ECT2 as a potential therapeutic target for cancer treatment. 5. ECT2 affects tumor immune infiltration and microenvironment in pan-cancer With the bloom of tumor molecular biology and technology advances, so does the understanding of the complexity and diversity of the immune environment of the tumor microenvironment and its impact on treatment response(52-54).The tumor microenvironment is a complex system that includes not only malignant cells, but also various non-malignant cells (fibroblasts, inflammatory cells, vascular-related cells) and an acellular matrix scaffold composed of collagen, elastin, and glycoproteins(55). Herein, algorithms such as TIMER(56), CIBERSORT(57), QUANTISEQ(30), XCELL(29), MCPCOUNTER(58), IPS(59), EPIC(60) are used to explore the potential relationship between different levels of immune cell infiltration in various tumor types of TCGA and ECT2 gene expression. With the deconvo_xCell method, we observed that ECT2 expression was significantly associated with immune infiltration in 44 cancer types (Figure 5A). With the deconvo_quantiseq method, we finally obtained 11 types of immune cell infiltration scores, including B_cells, Macrophages_M1, Macrophages_M2, Monocytes, Neutrophils, NK_cells, T_cells_CD4, T_cells_CD8, Tregs, Dendritic_cells, Other. Pearson’s correlation coefficient of immune cell infiltration scores observed that ECT2 expression is significantly associated with immune infiltration in 43 cancer types (Figure 5B). With the Timer method, we finally obtained 6 types of immune cell infiltration scores, including B cell, T cell CD4, T cell CD8, Neutrophil, Macrophage, DC. Pearson’s correlation coefficient of immune cell infiltration scores observed that ECT2 expression is significantly associated with immune infiltration in 37 cancer types (Figure 5C). NK cells, Th1 cells, Th2 cells and CD4+ T cells were most positively associated with the ECT2 expression in these different cancers. In addition, we used the CIBERSORT, MCPCOUNTER, IPS, and EPIC methods to further indicates that ECT2 gene expression is significantly correlated with immune infiltration (Supplementary Figure 5). We used the scRNA-seq database to analyze the expression of ECT2 in TME-related cells. Among HNSC, KIRC, LIHC, NHL, NSCLC, ALL, and MM, we found that ECT2 was expressed in malignant cells. In addition, ECT2 expression was the highest in CD8 T cells, conventional CD4 T cells, exhausted CD8 T cells, monocytes or macrophages, proliferating T cell fibroblasts (Supplementary Figure 6). These results demonstrated that ECT2 was closely related to TME in cancer. 6. The effect of ECT2 on immunological status in pan-cancers Pan-cancer analyses aimed at depicting the immunological role of ECT2 are critical in determining the types of cancers that may benefit from anti-ECT2 immunotherapy. Our findings revealed that ECT2 was negatively correlated with the majority of immunomodulators in NB, THYM, TGCT, GBM and LUSC, and ECT2 was positively correlated with a majority of immunomodulators in other cancers (Figure 6A). In NB, THYM, TGCT, GBM and LUSC, a majority of MHC molecules were negatively correlated with ECT2, which indicated that the capacity of antigen presentation and processing was downregulated in the high-ECT2 expression cancers. Chemokines, including CCL2, CCL3, CCL4, CCL5, CCL19, CCL20, CCL21, CXCL11, CXCL13, and paired receptors including CCR1, CCR2, CCR5, CCR6, and CXCR3, were positively correlated with ECT2. These chemokines and receptors promote the recruitment of effector TIICs such as CD8+ T cells, TH17 cells, and antigen-presenting cells. Given the complex and manifold functions of the chemokine system, the relationship between ECT2 and individual chemokines was not sufficient to clarify the overall immunological effect of ECT2 in TME. The activities of the cancer immunity cycle are a direct comprehensive performance of the functions of the chemokine system and other immunomodulators(61). Immune surveillance affects the prognosis of tumor patients, and tumors use PD-1, PD-L1, CTLA-4 and other immune checkpoints to evade immune responses(62). Consistently, in this study, ECT2 was found to be positively correlated with a majority of immune checkpoint inhibitors and stimulators, including HMGB1, LAG-3, VEGFA, IDO1, and CD80, and ECT2 was negatively correlated with the majority of immune checkpoint inhibitors and stimulators in NB and TGCT (Figure 6B). Thorsson et al. performed an immunogenomic analysis of more than 10,000 tumors, identified 6 immune subtypes that included multiple cancer types, and hypothesized that these subtypes could define patterns of immune responses that influence prognosis. These analyses provide resources for exploring the immunogenicity of cancer types(63). Supplementary Figure 7 showed that the expression of ECT2 in 29 tumors was significantly different in immune type C1 (wound healing), C3 (inflammatory), C2 (IFN-gamma dominant), C4 (lymphocyte depleted), and C6 (TGF-beta dominant). Based on the above results, we conclude that ECT2 is expressed differently in immune and molecular subtypes of various human cancer types. In summary, the overexpression pattern of ECT2 is TME specific, which demonstrates the potential of ECT2 as a target for normalized cancer immunotherapy. 7. Pan-Cancer Analysis of the Correlation between the ECT2 Expression and TMB, MSI TMB (Tumor mutation burden) and MSI (Microsatellite Instability) are two promising biomarkers associated with immunotherapy response. TMB is highly correlated with the efficacy of PD-1/PD-L1 inhibitors, which allows some tumor patients to use TMB markers to also respond to immunotherapy(64-66). The expression level of ECT2 remarkably correlated with TMB in 13 tumors, including GBM, GBMLGG, LUAD, COAD, COADREAD, STES, KIPAN, STAD, PRAD, KIRC, PCPG, ACC, and KICH (Figure 7A). The correlation of the ECT2 expression with MSI was also investigated in 13 types of cancer, and GBM, COAD, COADREAD, STES, SARC, STAD, KIRC, LUSC, LIHC, and READ exhibited positive correlations, and GBMLGG, PRAD, and DLBC exhibited negative correlations (Figure 7B). This result deserves more in-depth research. Stemness, defined as the potential for self-renewal and differentiation from primitive cells, was originally thought to be a normal stem cell with the ability to give rise to all types of cells in an adult organism(67, 68). We obtained 2 tumor stemness indices calculated from mRNA expression and methylation signature from previous studies(28). We calculated the pearson correlation of ECT2 gene expression and DNAss tumor stemness score in each tumor and we observed a significant correlation in 9 tumors, of which 8 tumors were significantly positive and 1 tumor was significantly negative related (Figure 7C). The correlation of the ECT2 expression with DMPss was also investigated in 8 types of cancer, and GBM, GBMLGG, LGG, LUAD, STES, STAD, and LUSC exhibited positive correlations, and THYM exhibited negative correlations (Figure 7D). 8. Enrichment analysis of ECT2-related partners In order to further study the molecular mechanism of ECT2 gene in tumorigenesis, we tried to screen out the target ECT2-binding proteins and genes related to ECT2 expression for a series of pathway enrichment analysis. Based on BioGRID database and PINA database, we obtained a total of 136 ECT2-binding proteins (Figure 8A). Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that Metabolic pathways, RNA transport, Hippo signaling pathway and cell cycle were enriched (Figure 8B, C). Gene ontology (GO) analysis results revealed that ECT2-binding proteins were mainly enriched in the cell cycle process (Figure 8D). We further analyzed the correlation between 14 functional states and ECT2 in different cancers. In various tumors, ECT2 is mainly involved in the process of cell cycle and cell proliferation, and also plays an important role in the process of DNA damage repair (Figure 8E). In the LinkedOmics database, we analyzed the genes that are positively and negatively related to ECT2 in Adrenocortical carcinoma (ACC), and we found that these genes are mainly enriched in the cell cycle and DNA replication pathways (Figure 8F, Supplementary Figure 8A, B)(69). Gene regulatory networks (GRNs) composed of transcription factors and their downstream target genes play a crucial role in the regulation of gene expression. Due to the importance of ECT2 in cancer, we explored the TFs that regulate ECT2. We obtained 8 TFs which regulate ECT2 in the GRNdb database, which are mainly involved in cell cycle, apoptosis regulation, DNA damage repair and other pathways (Supplementary Figure 8C, D). These results indicated that ECT2 plays an essential role in cancer cell cycle progression. 9. ECT2 Promotes Cell Cycle Transition and Proliferation in Hepatocellular Carcinoma Cells To further elucidate the role of ECT2 in hepatocellular carcinoma (HCC), we established a lentiviral-mediated ECT2 knockdown (shECT2) model in the HCC cell line HepG2. Initial validation by qPCR confirmed the successful knockdown of ECT2 in HepG2 cells (Supplementary Figure 9A). Subsequent immunofluorescence analysis demonstrated a significant reduction in ECT2 protein expression, accompanied by decreased levels of cell cycle regulators Cyclin D1 and CDK1, indicating that ECT2 depletion disrupts cell cycle progression (Supplementary Figure 9B, C). Consistently, qPCR analysis revealed reduced mRNA expression of Cyclin D1 and CDK1 following ECT2 knockdown (Supplementary Figure 9D). Functional assays using CCK-8 revealed that ECT2 knockdown significantly inhibited HepG2 cell proliferation (Supplementary Figure 9E). Immunofluorescence further demonstrated that ECT2 depletion led to a marked reduction in the expression of epithelial-mesenchymal transition (EMT) markers Slug and Snail, suggesting that ECT2 may regulate EMT initiation and progression in HepG2 cells (Supplementary Figure 9F, G). To explore the molecular mechanisms underlying ECT2 function, we performed RNA sequencing (RNA-seq) on HepG2 cells with ECT2 knockdown. Differential gene expression analysis identified 1403 significantly altered genes, including 1103 upregulated and 300 downregulated genes (Supplementary Figure 10A, B). Gene Ontology (GO) enrichment analysis revealed that these differentially expressed genes were primarily involved in processes such as mitotic cell cycle checkpoint, I-kappaB kinase/NF-kappaB signaling, DNA-binding transcription factor activity, and Rho GDP-dissociation inhibitor binding (Supplementary Figure 10C). KEGG pathway analysis further highlighted key signaling pathways, including the Jak-STAT and AMPK signaling pathways (Supplementary Figure 10D). Notably, ECT2 knockdown significantly altered the expression of transcription factor families, with the ZF-C2H2 family exhibiting the most pronounced changes (Supplementary Figure 10E). In summary, our findings demonstrate that ECT2 promotes cell cycle transition and proliferation in HCC cells, thereby contributing to the progression of hepatocellular carcinoma. These results underscore the potential of ECT2 as a therapeutic target in HCC treatment. Discussion Immunotherapy targeting immune checkpoints is currently the exceedingly eye-catching therapeutic approach in the field of oncology, improving the prognosis of cancer patients(70). With accelerating globalization and advancements in immunotherapy and targeted therapy, the prognosis of cancer patients has improved(71, 72). However, OS in cancer patients remains poor ascribed to the heterogeneity of various patients. Early discovery and efficacious treatment are imperative prerequisites for enhancing the prognosis of cancer patients. Therefore, finding new therapeutic targets related to immunotherapy has received mounting attention of researchers. In recent years, escalating studies have focused on genome-wide pan-cancer analysis, revealing gene mutations, RNA changes and cancer driver genes associated to the manifestation and development of cancer, which is paramount for the early diagnosis of cancer and the identification of underlying biomarkers(73, 74). Increasing evidence showed that ECT2 is involved in the occurrence and development of all kinds of cancers. Studies also report functional links between ECT2 and clinical disease, especially tumors(20, 51, 75, 76). However, the pathogenesis of ECT2 in diverse tumors is unclear and requires further study. After an extensive literature search, we did not find any literature on pan-cancer analysis of ECT2. This study used the TCGA database to examine the expression level of ECT2 in distinct tumors and visualize its prognosis in pan-cancer, we observed ECT2 in 31 tumors remarkably up-regulated. Furthermore, combined with previous literature and our survival analysis of OS, DSS, DFI and PFI in ECT2-overexpressing tumors, we found that ECT2 expression was correlated with GBMLGG(75), PAAD, LUAD(76), BRCA(51), ESCA(77), LIHC(17, 78) and COADREAD(18) linked with poor prognosis. In contrast, in OV, low ECT2 expression was associated with poorer DSS and warrants further study. Epigenetics is the orderliness that regulates the expression of heritable genes without altering the DNA sequence. Omnifarious types of epigenetic modifications such as DNA methylation, histone modification, chromatin remodeling, and non-coding RNA regulation have been documented, among which DNA and RNA methylation modifications are extremely essential (79). In addition, changes in DNA methylation also regulate many aspects of advanced cancer pathophysiology, such as response to therapy and metastasis(80, 81). The CbioPortal tool was used to explore the mutation patterns and amplification frequencies of ECT2 in different tumors. We found that the most common DNA change in ECT2 in TCGA pan-cancer was amplification and the main genetic change found in the ECT2 gene was a missense mutation. Most importantly, overall survival, disease-specific survival, disease-free interval, and progression-free interval were lower in the ECT2 gene-altered group than in the unaltered group. Analysis of promoter methylation status revealed that ECT2 in TGCT, SARC, LUSC, LUAD, ESCA and COAD was hypomethylated. Meanwhile, ECT2 is hypermethylated in various types of cancers including PRAD, LIHC, and KIRP, suggesting that DNA methylation may be responsible for abnormal ECT2 expression. Immune cells in the tumor microenvironment (TME) exert a dramatic influence on tumorigenesis and development (82). Our study further elucidates the broader tumor applicability of ECT2 and confirms that ECT2 expression is tightly related to the biological processes of immune cells and immune-related molecules in nearly all cancers. In addition, our study revealed that ECT2 is co-expressed with genes encoding MHC, immune activation, immune suppression, chemokines, and chemokine receptor proteins. In addition, we founded that ECT2 is expressed differently in immune and molecular subtypes of numerous human cancer types. These results suggest that the expression of ECT2 is closely correlated with the immune infiltration of tumor cells, affects the prognosis of patients, and proffers a new target for the development of immunosuppressants. TMB is used as an auspicious pan-cancer predictive biomarker that can guide immunotherapy in the era of precision medicine, and TMB can also predict the prognosis of pan-cancer patients after immunotherapy(83, 84). MSI is additionally an essential biomarker in immune checkpoint inhibitors (ICIs)(85). Our study showed that ECT2 expression was associated with TMB in 13 cancer types and MSI in 13 cancer types. This may additionally propose that the expression degree of ECT2 influences TMB and MSI in cancer, thereby affecting the patient’s response to immune checkpoint inhibition therapy. This will supply a new reference for the prognosis of immunotherapy (86). In addition, information on ECT2-binding components and ECT2 expression-related genes from all tumors was integrated. Significant enrichment of a range of identified biological terms characterizing processes associated with ”mitotic cell cycle processes”, ”cell cycle” and ”DNA replication”. In a variety of tumors, ECT2 is broadly speaking involved in the cell cycle and cell proliferation, and additionally performs an important role in DNA damage repair. These findings propose that ECT2 may also make contributions to cancer cell proliferation with the aid of promoting cell cycle progression. This study investigated the role of ECT2 in hepatocellular carcinoma (HCC) by establishing a lentiviral-mediated ECT2 knockdown (shECT2) model in the HepG2 HCC cell line. Knockdown efficiency was confirmed via qPCR, and immunofluorescence revealed reduced ECT2 protein expression, accompanied by decreased levels of cell cycle regulators Cyclin D1 and CDK1, indicating disrupted cell cycle progression. Functional assays demonstrated that ECT2 knockdown significantly inhibited HepG2 cell proliferation. Additionally, immunofluorescence revealed reduced expression of epithelial-mesenchymal transition (EMT) markers Slug and Snail, suggesting ECT2’s role in EMT regulation. These findings collectively indicate that ECT2 promotes cell cycle transition, proliferation, and EMT in HCC cells, contributing to tumor progression. The study underscores ECT2 as a potential therapeutic target in HCC treatment. Identifying biomarkers and novel therapeutic ambitions is a fundamental aim of precision oncology, which will make contributions to the choicest treatment of human cancers. Our study demonstrates that computational biology can discover the molecular biological mechanisms by means of which ECT2 impacts tumor progression. This find out about found that ECT2 performs a prognostic function in pan-cancer and tumor microenvironment, which gives clues to apprehend the prognostic and immune results of ECT2 in distinctive tumors. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All the datasets were open access datasets. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by the national key clinical specialty project (The construction of multidisciplinary cooperative diagnosis and treatment system for children’s cancer guided by improving clinical service capacity). Author Contributions LW、HJ、XL conceived the study, drafted the manuscript and performed the analysis. HZ revised the manuscript and finally approved the version to be published. All authors read and approved the final manuscript. Acknowledgements Not applicable. Abbreviations ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma COADREAD Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma FPPP FFPE Pilot Phase II GBM Glioblastoma multiforme GBMLGG Glioma HNSC Head and Neck squamous cell carcinoma KICH Kidney Chromophobe KIPAN Pan-kidney cohort (KICH+KIRC+KIRP) KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Brain Lower Grade Glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma STAD Stomach adenocarcinoma SKCM Skin Cutaneous Melanoma STES Stomach and Esophageal carcinoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma OS Osteosarcoma ALL Acute Lymphoblastic Leukemia NB Neuroblastoma WT High-Risk Wilms Tumor Reference 1. 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Trends Cell Biol. 2017;27(11):863-75. Figures Fig. 1. ECT2 variant, localization, single-cell variations, functional partners, and expression profile under physiological conditions. (A) Immunofluorescence staining of the subcellular distribution of ECT2within the nucleus, endoplasmic reticulum (ER), and microtubules of A549 lung carcinoma cells, U-251 glioblastoma cells, and U-2 osteosarcoma cells as adopted from the HPA database. (B) Bar plot of ECT2 mRNA expressions in various normal human tissues from the GTEx database. (C) ECT2 protein topology showing membrane localization with a natural missense variant of Ser15 and Thr833. (D) Plots of single-cell RNA-sequencing data from the FUCCI U-2 osteosarcoma cell line, showing the correlation between ECT2 mRNA expression and cell cycle progression. (E) Network of functional gene partners of ECT2. Fig. 2. Up-regulation of ECT2 mRNA expression in pan-cancer and the relationship between ECT2 expression and OS in tumor patients (A) ECT2 expression levels in tumor and normal tissues based on the consolidated data of GTEx and TCGA databases. (B) Cox analysis of ECT2 expression with OS in pan-cancer. (C) The ECT2 protein expression level in normal tissues and primary tissues of breast cancer, ovarian cancer, colon cancer, clear cell RCC, and UCEC, lung adenocarcinoma, pancreatic adenocarcinoma, head and neck squamous carcinoma, glioblastoma multiforme, hepatocellular carcinoma was examined using the CPTAC dataset. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Fig. 3. Mutation feature of ECT2 in different tumors of TCGA. (A) We analyzed the mutation features of ECT2 for TCGA tumors using the cBioPortal tool. The alteration frequency with mutation type. (B) The mutation site is displayed. (C) The mutation site with the highest alteration frequency (D320A/N/Y site) in the 3D structure of ECT2 is displayed. (D) Putative ECT2 copy-number alterations from GISTIC. (E, F) Mutation Type and Copy Number were statistically dependent of ECT2 expression. (G-J) The effect of ECT2 mutation status on overall, disease-specific, disease-free, and progression-free survival of cancer patients was investigated using the cBioPortal database. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Fig. 4. DNA methylation and mutation features of ECT2 in pan-cancer. (A, B) Promoter methylation level of ECT2 in pan-cancer. The results were obtained from the UALCAN database. (C) The correlation of ECT2 expression and RNA modification regulator expression in pan-cancer. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Fig. 5. The ECT2 expression correlated with immune infiltration (A) The ECT2 expression significantly correlated with the infiltration levels of various immune cells based on xCell. (B) The ECT2 expression significantly correlated with the infiltration levels of various immune cells in the QUANTISEQ database. (C) The ECT2 expression significantly correlated with the infiltration levels of various immune cells in the Timer database. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Fig. 6. The effect of ECT2 on immunological status in pan-cancers. (A) Correlation between ECT2 and immunomodulators (chemokines, receptors, MHC, and immunostimulators). (B) Correlation between ECT2 and immune checkpoints (Inhibitory, Stimulaotry) The color indicates the correlation coefficient. The asterisks indicate a statistically significant p-value calculated using spearman correlation analysis. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Fig. 7. Correlation of ECT2 gene expression with TMB and MSI in pan-cancer tissues (A) A stick chart showing the relationship between ECT2 gene expression and TMB in different tumors. (B) A stick chart showing the association between ECT2 gene expression and MSI in different tumors. (C)A stick chart shows the relationship between the ECT2 gene expression and DNAss in diverse tumors. (D)A stick chart shows the relationship between the ECT2 gene expression and DMPss in diverse tumors. Fig. 8. Enrichment analysis of ECT2-related genes (A) ECT2-binding proteins were obtained from the BioGRID database and the PINA database. (B-C) Analysis of ECT2-binding proteins by Kyoto Encyclopedia of Genes and Genomes pathway analysis. (D) Analysis of ECT2-binding proteins by Gene ontology (GO) analysis. (E) The correlation between 14 functional states and ECT2 in different cancers. (F) Genes positively and negatively associated with ECT2 in adrenocortical carcinoma (ACC) in the LinkedOmics database. Supplementary Fig. 1 The relationship between ECT2 expression and DSS, DFI and PFI in tumor patients (A) Cox analysis of ECT2 expression with disease-specific survival in pan-cancer. (B) Cox analysis of ECT2 expression with disease-free interval in pan-cancer. (C) Cox analysis of ECT2 expression with progression-free interval in pan-cancer. Supplementary Fig. 2 The relationship between ECT2 gene expression and different clinicopathological stages in pan-cancer (A)Violin plots showing differential ECT2 expression levels in pathological T1/T2/T3/T4 stage. (B)Violin plots showing differential ECT2 expression levels in the pathological M0/M1 stage. (C)Violin plots showing differential ECT2 expression levels in the pathological N0/N1/N2/N3 stage. (D)Violin plots showing differential ECT2 expression levels in the pathological I/ II/ III/ IV stage. (E)Violin plots showing differential ECT2 expression levels in the pathological G1/G2/G3 stage. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Fig. 3 Linking ECT2 expression changes to mutations (A) The TP53 mutation is strongly associated with ECT2 expression changes in seven cancers. (B-C) The PTEN and RBL1 mutations are associated with ECT2 expression changes. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Fig. 4 Phosphorylation of ECT2 in several selected cancers according to the CPTAC database The schematic diagram and phosphorylation sites of the ECT2 protein are shown. The phosphorylation of ECT2 at S367, T373, S442, S443, T444, T857, S858, S861, T373, T359, and S866 was analyzed in breast cancer, colon cancer, clear cell RCC, LUAD, ovarian cancer, pancreatic adenocarcinoma, head and neck squamous carcinoma, and UCEC. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Fig. 5 Correlations between ECT2 expression and tumor immune infiltration (A) The ECT2 expression significantly correlated with the infiltration levels of various immune cells based on CIBERSORT. (B) The ECT2 expression significantly correlated with the infiltration levels of various immune cells in the EPIC database. (C) The ECT2 expression significantly correlated with the infiltration levels of various immune cells in the IPS database. (D) The ECT2 expression significantly correlated with the infiltration levels of various immune cells in the MCP counter database. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Fig. 6 Correlation analysis between ECT2 expression and TME (tumor microenvironment). ECT2 was expressed in malignant cells and was the highest in CD8 T cells, conventional CD4 T cells, exhausted CD8 T cells, monocytes and macrophages, and proliferating T cell fibroblasts in HNSC, KIRC, LIHC, NHL, NSCLC, ALL and MM. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Fig. 7 Correlation between ECT2 expression and immune subtypes in TCGA tumors. Supplementary Fig. 8 Transcription factor (TF) regulatory networks (A, B) Genes positively and negatively associated with ECT2 in adrenocortical carcinoma (ACC). (C) TFs which regulate ECT2 in the GRNdb database. (D) Heat map presents the percentage of cancers in which a gene has an effect (FDR≤0.05) on the pathway among selected cancers types; the number in each cell indicates the percentage. (∗p<0.05, ∗∗p<0.01, and ∗∗∗p<0.001.) Supplementary Figure 9 Effect of ECT2 knockdown in HCC cell lines on cell proliferation (A) qPCR confirmed the efficiency of ECT2 knockdown. (B, C) Immunofluorescence analysis revealed a significant reduction in the expression of cell cycle proteins Cyclin D1 and CDK1 following ECT2 depletion. (D) qPCR further demonstrated decreased mRNA levels of Cyclin D1 and CDK1, along with altered expression of P16 and P21. (E) CCK-8 assays showed that ECT2 knockdown significantly inhibited HepG2 cell proliferation. (F, G) Immunofluorescence also indicated a marked reduction in the expression of epithelial-mesenchymal transition (EMT) markers Slug and Snail in shECT2 cells. (∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001, ∗∗∗∗p<0.0001). Supplementary Figure 10 RNA-seq after knockdown of ECT2 in HepG2 cells (A) Correlation analysis of samples; (B) Volcano plot of differentially expressed genes; (C) Gene Ontology (GO) enrichment analysis of differentially expressed genes; (D) KEGG pathway enrichment analysis of differentially expressed genes; (F) Transcription factor family enrichment analysis of differentially expressed genes. Information & Authors Information Version history V1 Version 1 03 March 2025 Peer review timeline Published Cancer Informatics Version of Record 29 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords pan-cancer prognostic biomarker Authors Affiliations Lulu Wang 0009-0006-2325-0733 Yangpu Hospital Affiliated to Tongji University School of Medicine View all articles by this author Hua Jin Yangpu Hospital Affiliated to Tongji University School of Medicine View all articles by this author Xiaowei Liu [email protected] Tongji University Dongfang Hospital View all articles by this author Hanzhi Zhang Yangpu Hospital Affiliated to Tongji University School of Medicine View all articles by this author Metrics & Citations Metrics Article Usage 236 views 135 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lulu Wang, Hua Jin, Xiaowei Liu, et al. Pan-Cancer Analysis of the Prognostic and Immunological Role of ECT2: A Promising Target for Survival and Immunotherapy. Authorea . 03 March 2025. 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