Integration of Single-cell and Bulk RNA Sequencing to Identify a Distinct Tumor Stem Cells and Construct a Novel Prognostic Signature for Evaluating Prognosis and Immunotherapy in LUAD

Integration of Single-cell and Bulk RNA Sequencing to Identify a Distinct Tumor Stem Cells and Construct a Novel Prognostic Signature for Evaluating Prognosis and Immunotherapy in LUAD | 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 Integration of Single-cell and Bulk RNA Sequencing to Identify a Distinct Tumor Stem Cells and Construct a Novel Prognostic Signature for Evaluating Prognosis and Immunotherapy in LUAD Fengyun Zhao, Zhaowei Ding, Tianjiao Wu, Mingfang Ji, Fugui Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4752786/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cancer stem cells (CSCs) play a crucial role in the progression of lung adenocarcinoma (LUAD).This study aimed to explore the gene signatures of tumor stem cells in LUAD through Single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) data, and establish a tumor stem cell marker signature(TSCMS)prognostic risk model. Methods The LUAD scRNA-seq data and bulk RNA-seq data from the GEO and TCGA databases were collected. CytoTRACE software was used to quantify the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) was performed to identify potential biological functions in different clusters. The TSCMS prognostic risk model was constructed using Lasso-Cox regression analysis, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. The Cibersortx algorithm was used to evaluate immune infiltration, and drug response prediction was conducted using the pRRophetic package. Functional investigations of TAF10 in LUAD cells were performed using bioinformatics analysis, qRT-PCR, Western blot, Immunohistochemistry, cell proliferation and clone formation assay. Results Seven distinct cell clusters were identified by CytoTRACE (Epi C1 to C7), with Epi C1 demonstrating the highest stemness potential. The TSCMS prognostic risk model incorporated 49 tumor stemness-related genes, and high-risk patients exhibited reduced immune scores, lower ESTIMATE scores, and increased tumor purity. Furthermore, significant differences in immune landscapes and chemotherapy sensitivity were observed between high and low risk groups. TAF10 was found to be positively correlated with the RNA expression-based stemness score (RNAss) in various tumors, including LUAD. And we demonstrated that TAF10 was over-expressed in LUAD cell lines and tumor tissues of clinical patients, and high TAF10 expression was correlated with poor prognosis in LUAD patients. Silencing TAF10 inhibited LUAD cell proliferation and clone formation. Conclusions Our investigation highlights the prognostic utility of the TSCMS model for evaluating the clinical outcomes of LUAD patients, uncovering critical insights into immune cell infiltration and therapeutic response, and positions TAF10 as a novel therapeutic target for LUAD. single-cell RNA gene signature prognostic model LUAD tumor stem cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung adenocarcinoma (LUAD), a prevalent and challenging malignancy of the lungs, has exhibited a steady rise in its incidence in recent years [ 1 – 3 ]. Single-cell RNA sequencing (scRNA-seq) has emerged as a potent tool for delving deeper into the intricate landscape of this disease [ 4 , 5 ]. Characterized by rapid technological advancements, single-cell technologies have garnered substantial attention and application across diverse solid and hematologic malignancies, such as utilizing scRNA-seq to unveil the landscape of infiltrating T cells in liver cancer [ 6 ], and investigating the clonal evolution of circulating tumor cells within peripheral blood [ 7 ]. In LUAD, by furnishing high-resolution gene expression profiles, single-cell sequencing has endowed researchers with an unprecedented ability to decipher the intricate heterogeneity and underlying molecular mechanisms driving the pathogenesis [ 8 – 10 ]. Leveraging the capabilities of single-cell sequencing, researchers are navigating complex gene expression signatures, functional attributes, and intricate cellular interactions within distinct subpopulations in LUAD [ 10 ]. Of particular significance within the landscape of LUAD research is the burgeoning interest in tumor stem cells, which are recognized for their pivotal contribution to tumor initiation, progression, therapeutic resistance, and prognosis [ 11 , 12 ]. This systematic exploration has unveiled the existence of tumor stem cells within the intricate tapestry of cancer and has underscored their potential role in orchestrating the course of the disease [ 13 , 14 ]. In pursuit of refining prognostic, numerous studies have embarked on the development of prognostic model [ 15 – 17 ]. The publicly accessible databases, including TCGA and GEO repositories, provide abundant LUAD samples and associated clinical data, thereby facilitating the construction and rigorous validation of these prognostic models [ 18 , 19 ]. In this study, we harnessed the single-cell sequencing methodologies to unravel the intricate tapestry of tumor stem cells in LUAD, discern hallmark gene signatures, and establish refined prognostic risk models. First, a publicly available single-cell RNA sequencing (scRNA-seq) dataset (11 normal and 11 LUAD) was annotated, obtaining 88,144 cells and 16 clusters. Next, we assessed the stemness of 7,252 tumor-derived epithelial cells using CytoTRACE software, delineated 7 distinct cell clusters, and identified Epi C1 exhibited the highest stemness potential. Subsequently, we screened 49 cancer stem cell-related genes which were most relevant to the prognosis of LUAD patients to construct the tumor stem cell marker signature (TSCMS) prognostic risk model. This model had the potential to predict prognosis, efficacy of immune checkpoint blockade, and responsiveness to immunotherapy in LUAD patients. Moreover, we demonstrated that TAF10, the top one gene in terms of the stemness score and correlation with patient prognosis, played an oncogenic role in LUAD. These findings propose the TSCMS model exhibits an excellent predictive capacity for the prognosis of LUAD patients, and provide potential targets for LUAD treatment. Materials and methods Data Collection Single-cell sequencing data (scRNA-seq) and Bulk RNA sequencing data were obtained online from the GEO and TCGA databases. The single-cell data originated from GEO (GSE1311907), while the Bulk RNA data were downloaded from TCGA and GEO (TCGA-LUAD cohort, GSE26939 and GSE72094). The LUAD immunotherapy cohort ‘IMvigor210CoreBiologies’ was sourced from previously published research [ 20 ]. Data for drug IC50 predictions were acquired from a statistical study [ 21 ]. All these datasets were sourced from public databases or shared by others. Preprocessing of ScRNA-seq Data Employing the R package Seurat, we imported the unprocessed expression matrix [ 22 ]. Subsequently, we performed filtering to include single-cell data originating from both LUAD and normal tissues. Cells exhibiting mitochondrial gene content exceeding 30% and those manifesting expression of more than 10,000 genes were excluded from the analysis. For normalization, we applied the SCTransform function, which mitigates technical noise and ensures uniform scaling across cells. Subsequently, the RunPCA function was applied with the parameter npcs = 50, and the RunUMAP function used parameters reduction="pca" and dim = 1:30. The FindNeighbors function was employed with parameters reduction="pca" and dims = 1:30. Leveraging these neighborhood relationships, clustering was performed with the FindClusters function, wherein a resolution parameter of 0.1 was chosen to delineate 16 distinct cell clusters. Annotation of Cellular Subpopulations After obtaining the 16 clusters, we proceeded to annotate these clusters with cell types based on the expression of specific marker genes [ 23 ]. Immunological cells were identified using a spectrum of markers, including PTPRC, and various subclasses such as B cells (CD79A, and MS4A1), plasma cells (IGLC2, and IGHM), T cells (CD3D, and CD3E), monocytes (CD14, and S100A8), NK cells (NKG7, and GNLY), mast cells (CPA3, and KIT), and macrophages (CD68, and MARCO). Additionally, non-immune cell types were characterized, including epithelial cells (EPCAM, and KRT8), endothelial cells (PECAM1, and VWF), and fibroblasts (COL1A1, and DCN). This analysis ultimately yielded the identification of 10 major cell types within the dataset. Differential Gene Analysis The identification of highly expressed genes in scRNA-seq cells was performed using the Seurat package's FindAllMarkers function with parameters set as only.pos = T and logfc.threshold = 0.25, while keeping other parameters as default. Differential gene analysis for the epithelial cell cluster in scRNA-seq was presented in Supplementary Table S1 , and results were visualized using the R package EnhancedVolcano. For Bulk RNA-seq differential analysis, the DESeq2 package was utilized with default parameters. Differential analysis was conducted by grouping samples into high and low-risk categories based on the median, and the results of differentially expressed genes between the high-risk and low-risk groups are available in Supplementary Table S7. Prediction of Tumor Epithelial Cell Stemness CytoTRACE utilizes gene expression and an intrinsic stemness gene set to predict cell stemness at the single-cell level [ 24 ]. To identify the clusters of tumor epithelial cells with the highest stemness or lowest differentiation, we employed the CytoTRACE pipeline from the R package. The results of stemness-related genes (cor > 0.3) can be found in Supplementary Table S3. Gene Functional Enrichment Analysis The enrichment analysis of seven types of tumor tissue-derived epithelial cells in scRNA-seq was conducted using the R package GSVA [ 25 ]. Initially, 50 tumor Hallmark gene sets were obtained using the R package msigdbr. The GSVA function was applied with the parameter method="ssgsea" to perform enrichment analysis on the expression matrices of the seven epithelial cell types. The ssgsea enrichment scores can be found in Supplementary Table S2. For the GSEA enrichment analysis of the TSCMS model, the R package fgsea was used with default parameters. Differential genes between high and low-risk groups based on the TCGA training set were ranked according to their FoldChange. The enrichment results of KEGG pathways from GSEA can be found in Supplementary Table S10. Construction and Validation of the Prognostic Risk Model TSCMS Intersecting the stemness-related genes with the highly expressed genes within the tumor epithelial cell cluster Epi C1, we conducted a univariate Cox regression analysis to ascertain the prognostic significance of these overlapping genes in relation to overall survival among LUAD patients sourced from the TCGA dataset. Genes yielding a p-value of less than 0.05 were designated as prognostic candidates. Subsequently, we subjected the identified prognostic genes to a least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression, leveraging the "glmnet" package [ 26 ]. Employing ten-fold cross-validation, we curated a gene list featuring nonzero coefficients, culminating from an optimal model feature selection process (Supplementary Table S5). The resultant risk model was meticulously formulated by a linear summation of the products of genes and their corresponding risk coefficients. Patient stratification into low-risk or high-risk groups hinged upon a median threshold (Supplementary Table S6). To methodically validate the prognostic efficacy of the TSCMS model, we computed the area under the curve (AUC) utilizing the "timeROC" package [ 27 ]. Survival analysis, grounded in the Kaplan–Meier methodology, was adeptly undertaken. Further statistical assessment of differences was facilitated through the application of the log-rank test, seamlessly integrated within the R package "survminer" [ 28 ]. Notably, the predictive robustness of the model was subject to rigorous validation via survival analysis and AUC computation across two distinct GEO datasets. Immunocellular Infiltration Analysis Immune cell infiltration analysis was conducted by using R Packages CIBERSORT and ESTIMATE in TCGA-LUAD Patients [ 29 , 30 ]. The infiltration scores for 22 distinct immune cell types were computed using CIBERSORT (Supplementary Table S11). Based on the median risk score, patients were divided into two groups, and differences in immune cell infiltration across the 22 types were compared between these groups. Furthermore, the ESTIMATE package was utilized to calculate overall immune scores, stromal scores, ESTIMATE scores, and tumor purity (Supplementary Table S12). Following the division into two groups based on the median risk score, inter-group differences were assessed. Prediction of Immunotherapy Response The IMvigor210 cohort is an immunotherapy-focused dataset for bladder cancer (BLCA), encompassing gene expression matrices, patient clinical information [ 20 ], and records of immunotherapy responses. Patients were stratified into two groups based on the median cutoff of their risk scores. Comparative analysis was performed to assess differences in the expression of immune checkpoint markers between the two groups, as well as disparities in patients' immunotherapy responses. Drug Response Prediction We conducted drug response prediction using the pRRophetic package [ 21 ]. The gene expression profiles of high and low-risk groups were employed to estimate the IC50 values for various commonly used clinical or preclinical anti-tumor drugs. By leveraging statistical methods, we identified drugs with significantly distinct IC50 values between these risk groups (Supplementary Table S8 and S9). Gene Expression and Bioinformatics Analysis of TAF10 from Public Database The expression and RNA expression-based stemness score (RNAss) data for TAF10 in various tumor types in TCGA database were obtained from the SangerBox database ( http://SangerBox.com/Tool ) [ 31 , 32 ]. Cell Culture Human LUAD cell lines (A549, PC9, H1975) and human normal bronchial epithelioid cells (16HBE) were purchased and authenticated from the ATCC. All cell lines were maintained in either RPMI-1640 medium or DMEM (Thermo Fisher Scientific, MA, USA) medium supplemented with 10% fetal bovine serum. Cells were cultured at 37°C in a humidified atmosphere with 5% CO2. Cell Proliferation and Clone Formation Assay Cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay according to the manufacturer's instructions. For the clone formation assay, cells were seeded in 6-well plates at a density of 500 cells/well and incubated at 37°C. The medium was refreshed every 3 days until colonies formed. After cell fixation with paraformaldehyde, crystal violet was used to stain the cells. Colonies with more than 50 cells per colony were counted under a microscope. Quantitative Rea-time PCR (qRT‒PCR) First-strand cDNA was synthesized using the GenStar A212-05 kit according to the standard protocol. qPCR was performed using the SYBR Green Supermix and CFX96 real-time PCR detection system. The mRNA expression of genes was analyzed using the 2 −ΔΔCt method. The primers for TAF10 were 5′-ATTGATGCCATACTCGCTGAG-3′ and 5′- GAAGTGAAGCCCGTAGTGTCC-3′, and the primers for β-actin were 5′-TCGTGCGTGACATTAAGGAG-3′ and 5′-ATGCCAGGGTACATGGTGGT-3′. Western Blot Cells were lysed with RIPA buffer supplemented with protease inhibitor and boiled at 95°C for 5 min. Equal amounts of protein were added to sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane. The membrane was blocked with 5% nonfat dry milk for 1 h at room temperature and then incubated with primary antibodies overnight at 4°C. The next day, the membranes were incubated for 1 h at room temperature with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies. Immunoreactive proteins were visualized using SuperSignal West Dura Chemiluminescent. Antibodies for TAF10 (NBP1-80706) were purchased from NOVUS Biologicals, and GAPDH antibody (H11459) was purchased from Sigma as a loading control. Immunohistochemistry All paraffin-embedded tissues of patients in this study were obtained with informed patient consent. For immunohistochemistry staining, deparaffinized and rehydrated sections were boiled in Na-citrate buffer (10 mM, pH 6.0) for 30 min for antigen retrieval. The sections were incubated with primary antibodies and developed using the Ultra Vision Detection System. Images were captured using an Olympus IX51 microscope and processed using cellSens Dimension software. Plasmids and Cell Transfections Short hairpin RNA (shRNA) sequences targeting TAF10 were cloned into psiF-copGFP vectors (System Biosciences, Mountain View, CA). The shRNA sequences for TAF10 were 5′-CCAGAAATTCATCTCAGATAT-3′, and the sequence for the negative control (shCtl) was 5′-GGTGTGCAGTTGGAATGTA-3′. To establish stably transfected cell lines, HEK-293T cells were transfected with lentivirus produced using the second-generation packaging system pMD2.G and psPAX2, and the virus was harvested 48 h after transfection. The LUAD cell lines were transduced with the virus in the presence of 8 µg/mL polybrene and screened with puromycin for 7 days. Statistical Analysis In appropriate scenarios, we employed either the Student’s t-test or the Wilcoxon rank-sum test to assess the significance of differences between groups. The selection of the test depended on the distribution of the data and the assumption of normality. For survival analysis, the Log-Rank test was utilized to determine the significance of survival differences between different groups or conditions. P value less than 0.05 was considered statistically significant. Statistical significance levels were denoted as follows: * for p < 0.05, ** for p < 0.01, *** for p < 0.001, and **** for p < 0.0001. Results Workflow and Cell Population Landscape in LUAD To explore the potential functions of LUAD tumor stem cells, we collected bulk RNA-seq data from TCGA-LUAD and GEO datasets (GSE26939 and GSE72094), as well as single-cell RNA-seq data from the GEO dataset (GSE131907). Using scRNA-seq, we predicted the stemness score of tumor epithelial cells. Then, we constructed a LUAD prognostic model based on tumor stemness genes and further validated its predictive ability (Fig. 1 a). First, we conducted quality control on all cells, applying filters with a minimum cell count of 3, a minimum feature count of 200, and mitochondrial gene content of less than 30 (Supplementary Figure S1 ). Next, we annotated a total of 22 samples (11 normal and 11 LUAD) from the single-cell dataset, comprising 88,144 cells and 16 clusters (Fig. 1 b, c, and Supplementary Figure S2). Based on the expression of cell markers within clusters, we identified ten major cell populations (Fig. 1 b). Compared to normal lung tissue, LUAD exhibited reduced infiltration of NK cells and macrophages. However, tumor patients demonstrated heterogeneity, with different LUAD samples showing varying proportions of epithelial cells (Fig. 1 d). We used common cell markers such as EPCAM for epithelial cells, PECAM1 for endothelial cells, PTPRC for immune cells, and COL1A1 for fibroblasts to define each cell type (Fig. 1 e). Therefore, LUAD exhibits substantial tumor heterogeneity, with varying compositions of tumors and their microenvironments among different patients. Prediction of Tumor Epithelial Stem Cells Further exploration of tumor stem cells involved the selection of 7252 tumor-derived epithelial cells for calculating stemness scores using the CytoTRACE software (Fig. 2 a). After applying dimensionality reduction and clustering techniques, 7 distinct cell clusters were identified (Fig. 2 b). Comparing the CytoTRACE-predicted stemness scores across these 7 tumor epithelial cell clusters revealed that Epi C1 exhibited the highest stemness potential (Fig. 2 c). Subsequent differential gene analysis of Epi C1 highlighted elevated expression of genes such as CDKN2A, TMSB10, SOO2A, PTGS2, and SNCG (Fig. 2 d and Supplementary Table S1 ). Additionally, the Hallmark GSVA enrichment analysis demonstrated that Epi C1 displayed higher enrichment scores in pathways associated with hypoxia, EMT, Kras signaling, MYC signaling, as well as E2F targets and G2M checkpoint, which are closely linked to cell cycle regulation, compared to other epithelial cell clusters (Fig. 2 e and Table S2). Thus, the Epi C1 cluster is likely to represent a subpopulation of stem-like epithelial cells within LUAD tumors. Construction and Validation of the Prognostic Model TSCMS To investigate the impact of stem-like tumor epithelial cells on LUAD patients, we intersected 1068 highly expressed genes in Epi C1 with 2509 CytoTRACE-computed genes showing correlation (cor > 0.3), resulting in 964 genes (Fig. 3 a and Supplementary Table S1 , 3). These genes were utilized for univariate Cox regression analysis to predict their association with survival in LUAD patients. We used the LUAD mRNA count expression matrix and corresponding clinical information from the TCGA database as the training set. Out of the 964 genes, 92 genes with p value < 0.05 (Supplementary Figure S3) were further subjected to Lasso regression and multiple-factor Cox regression with tenfold cross-validation, ultimately selecting 49 genes with non-zero coefficients as features to construct tumor stem cell marker signature (TSCMS) prognostic risk model (Fig. 3 b and Supplementary Table S4, 5). The risk score was calculated based on the cumulative expression values of the genes multiplied by their corresponding coefficients, and the TCGA training set samples were divided into high and low-risk groups using the median risk score (Fig. 3 c and Supplementary Table S6). In the training set, the model significantly stratified patients' survival (p < 0.0001), with area under the curve (AUC) values of 0.818, 0.851, and 0.871 for 1-year, 3-year, and 5-year survival, respectively (Fig. 3 d, g). Furthermore, we validated TSCMS using two independent external LUAD datasets, GSE26939 and GSE72094. The model demonstrated robust prognostic stratification ability in GSE26939 (p = 0.012) and GSE72094 (p = 0.00015) (Fig. 3 e, f), with corresponding AUC values of 0.707, 0.637, and 0.595 for 1-year, 3-year, and 5-year survival in GSE26939 (Fig. 3 h), and 0.702, 0.667, and 0.751 in GSE72094 (Fig. 3 i). In conclusion, the newly developed prognostic risk model based on stem-like tumor epithelial cells exhibits an excellent predictive capacity for the prognosis of LUAD patients. The Association between TSCMS and Immune Cell Infiltration in the TME As immune cells wield a pivotal role in tumor immunity and promotion, we delved into the relationship between TSCMS and immune cell infiltration within LUAD patients. Leveraging the cibersortx program, we investigated the infiltration of 22 distinct immune cell types. Notably, the high-risk group demonstrated diminished levels of B cell naive, CD4 + T cell memory resting, monocytes, and mast cells when compared with the low-risk group (Fig. 4 a). Conversely, macrophage M0 infiltration exhibited heightened levels within the high-risk group (Fig. 4 a). Moreover, employing the ESTIMATE program, we calculated infiltration scores for both high and low-risk cohort, the high-risk group revealed markedly reduced immune scores (Fig. 4 b), lower ESTIMATE scores (Fig. 4 d), and heightened tumor purity (Fig. 4 e). Remarkably, no pronounced disparities emerged in stromal scores (Fig. 4 c). After partitioning TCGA-LUAD samples into two distinct groups according to the median risk score, we conducted an analysis of differential gene expression (Supplementary Table S7), subsequently followed by GSEA enrichment analysis ranked by fold change. Specifically, the high-risk group exhibited enrichments in pivotal pathways such as cell cycle regulation, DNA repair, and P53 signaling, while the low-risk group showcased enrichments in chemokine signaling, chemokine receptor interactions, and T cell receptor signaling (Fig. 4 f). These results imply a positive correlation between the TSCMS risk score and tumor cell proliferation, coupled with a negative correlation with immune functionality. The diminished predictive prognosis of TSCMS might be attributed to its association with reduced immune infiltration capacity. TSCMS could Predict Immunotherapy Benefits in LUAD Patients Building upon the pivotal role of TSCMS in immune cell infiltration, we further explored its predictive influence on immune checkpoint blockade and immunotherapy response. Firstly, within the IMvigor210 cohort, we analyzed immune checkpoint expression including PD1, PD-L1, and CTLA4. Notably, there were no significant differences in PD1 and CTLA4 expression between high and low-risk groups (Fig. 5 a, d), while PD-L1 expression was higher in the high-risk group (Fig. 5 b). Evaluating the response to anti-PD-L1 therapy, the risk score was notably lower in the R (complete response/partial response; CR/PR) group compared to the NR (stable disease/progressive disease; SD/PD) group (Fig. 5 c). Moreover, the stratification of patient prognosis by TSCMS within this cohort exhibited statistically significant implications (Fig. 5 e). In terms of treatment response, the low-risk group showed a 9% higher population of CR/PR compared to the high-risk group (Fig. 5 f). In conclusion, these findings suggest that patients with a lower risk score may benefit more from anti-PD-L1 therapy, indicating TSCMS as a potentially helpful biomarker for anti-PD-L1 treatment. TSCMS-Based Prediction of Anti-Tumor Drug Efficacy In addition to immunotherapy, chemotherapy remains a pivotal approach in the battle against tumors. Thus, we computed the IC50 sensitivities of commonly used clinical or preclinical anti-tumor drugs between high and low-risk TSCMS groups. Among the findings, IC50 values for 61 drugs were observed to be lower in the high-risk group in comparison to the low-risk group (Supplementary Table S8). Furthermore, for 8 drugs, the IC50 values in the low-risk group were significantly lower than those in the high-risk group (Supplementary Table S9). Through prioritizing results based on significance, we revealed the top 6 drugs with better sensitivity in the high-risk group (Fig. 6 a) and the top 3 drugs with better sensitivity in the low-risk group (Fig. 6 b) in terms of IC50 outcomes. These results hold the potential to offer invaluable guidance for personalized treatment strategies in LUAD patient. TAF10 plays Oncogenic Role in LUAD To validate the role of the genes used to construct the TSCM prognostic risk model, we selected TAF10 for further investigation, as it was the most relevant to the prognosis of LUAD patients among these 49 genes. Based on the data from TCGA and GTEx databases, we found that the mRNA level of TAF10 in most tumors (including LUAD) was upregulated compared with corresponding normal tissues (Fig. 7 a). Notably, the stemness features analyses showed that TAF10 was positively correlated with the RNA expression-based stemness scores (RNAss) in a variety of tumors, including LUAD (R = 0.325, p = 0.009) (Fig. 7 b), and high TAF10 expression was correlated with poor prognosis in LUAD patient (Fig. 7 c, d). Next, we analyzed the mRNA expression of TAF10, S100P, PAFAH1B3, CCT6A, and DCBLD2 (the top five among the 49 genes) in LUAD cell lines. RT-PCR results indicated that the mRNA expression of all these genes was significantly higher in LUAD cell lines than in human normal bronchial epithelioid cells (16HBE) (Fig. 7 e), with TAF10 exhibited the greatest fold change in expression among these genes. WB results further confirmed the high protein expression of TAF10 in LUAD cells (Fig. 7 f). Furthermore, the IHC results showed that TAF10 was highly expressed in clinical LUAD samples (Fig. 7 g). To further investigate the role of TAF10 in LUAD, we used loss-of-function method to evaluate the impact of TAF10 on LUAD cells (Fig. 7 h). We found that TAF10 silencing significantly reduced the ability of colony formation (Fig. 7 i) and suppressed cell proliferation of LUAD cell lines (Fig. 7 j). These results indicate that up-regulated TAF10 expression promoted cell proliferation in LUAD, which demonstrate TAF10 as an ideal gene for further mechanistic studies in LUAD. Discussion Previous efforts have been dedicated to establishing prognostic models in LUAD based on tumor-associated cancer-associated fibroblasts (CAFs) and immune cells [ 33 – 37 ]. In this study, we focused on the tumor stem cell in LUAD. Through the integration of stemness-associated genes and scRNA-seq datasets, we have effectively pinpointed distinct epithelial cell clusters originating from the tumor microenvironment that exhibit stemness characteristics. This endeavor led us to the development of a novel prognostic risk model termed TSCMS. Notably, this model doesn't solely rely on clinical parameters but also provides a heightened precision in predicting patient survival outcomes, thereby serving as a robust aid for informed clinical decision-making. The model TSCMS was constructed from a set of 49 key genes, among which TAF10, S100P, PAFAH1B3, CCT6A, DCBLD2, CCDC85B, PSMD11, TFAP2A, TM4SF1, and DRG1 hold prominent coefficients. Notably, the TSCMS exhibits robust predictive capacity. In the TCGA training dataset, it reveals a substantial median survival difference of more than 5 years between high-risk and low-risk patients. Similarly, across two independent external validation datasets, the TSCMS indicates a median survival discrepancy of 3 years between the high and low-risk patient groups. Furthermore, when evaluating the accuracy of TSCMS for predicting survival rates at 1 year, 3 years, and 5 years, our results consistently surpass an average threshold of 0.7 across the three cohorts. Importantly, in comparison to risk models proposed by Ren et al. centered on CAFs [ 33 ] and Zhang et al. focusing on T-cell markers [ 35 ], our TSCMS displays superior accuracy in prognosticating patient survival. This underscores the robust predictive power of TSCMS for patient survival. Immune cells play a pivotal role within the tumor microenvironment, exerting influence over tumor development and therapeutic responses. Employing methodologies such as CIBERSORT and ESTIMATE, we observed distinctions in the distribution of B cells, monocytes, mast cells, and macrophages between the high-risk and low-risk groups. Sarvaria et al. have highlighted the crucial role of B cells in promoting inflammation and carcinogenesis [ 38 ]. Macrophage type M0 exhibited significantly higher infiltration in the high-risk group, a trend consistent with the findings of Huang et al., who reported M0 macrophages promoting malignant growth in glioma [ 39 ]. Mast cells can promote angiogenesis by releasing classical pro-angiogenic factors, and support tumor invasion by releasing matrix metalloproteinases [ 40 ], we found the proportion of activated mast cells is notably higher in the high-risk group compared to the low-risk group. Additionally, the low-risk group exhibited higher enrichment of immune-related pathways, including T cell receptor signaling and chemokine-chemokine receptor signaling pathways. In the tumor microenvironment, tumor infiltration of T cells was driven by chemokines [ 41 ], and T cells integrate chemokine signals to enhance antitumor responses in peripheral tissues [ 42 ]. Those results suggest a potential association between tumor stemness and immune infiltration, thereby providing valuable leads for further exploration into immune-based therapies. Immunotherapy and drug treatment are effective strategies in combating cancer. TSCMS has demonstrated robust predictive capability within the IMvigor210 immunotherapy cohort. In the high-risk group, PD-L1 expression levels are significantly elevated compared to low-risk patients. Additionally, patients who respond favorably to anti-PD-L1 treatment exhibit lower risk scores. Moreover, we successfully predicted the response of different risk groups to various anti-cancer drugs. These findings provide substantial support for personalized treatment and drug selection, holding the potential to make a positive impact in clinical practice. Out of the 49 key genes, TAF10 holds the highest prognostic correlation coefficient. Numerous studies have demonstrated that TAF10 plays an oncogenic role within a wide variety of tumors, including transcription, the cell cycle, and apoptosis [ 43 – 45 ], such as in gastric cancer cells, the high expression of TAF10 plays an important role in maintaining tumor cell survival [ 46 ]. We demonstrated that TAF10 was over-expressed in LUAD cell lines and tumor tissues of clinical patients, and high TAF10 expression was correlated with poor prognosis in LUAD patients. Silencing TAF10 inhibited LUAD cell proliferation and clone formation, which provided a new target for the targeted therapy of LUAD. However, our study has certain limitations. Firstly, the main limitation lies in the absence of further experiments to investigate the association between stemness and TAF10. Secondly, the clinical utility of the TSCMS needs independent validation in a larger cohort of LUAD patients. In conclusion, our research emphasizes the prognostic value of the TSCMS model in evaluating the clinical outcomes of LUAD patients, provides crucial insights into immune cell infiltration and therapeutic response, and identifies TAF10 as a novel therapeutic target for LUAD. Abbreviations LUAD Lung adenocarcinoma CSCs Cancer stem cells TSCMS Tumor stem cell marker signature OS Overall survival TME Tumor microenvironment KM Kaplan–Meier GSVA Gene set variation analysis DEGs Differentially expressed genes LogFC LogFoldChange scRNA-seq Single-Cell RNA Sequencing TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus PCA Principal components analysis KEGG Kyoto encyclopedia of genes and genomes GSEA Gene Set Enrichment Analysis ROC Receiver operating characterastic AUC Area under the curve RNAss RNA expression-based stemness score CCK-8 Cell Counting Kit-8 qRT‒PCR Quantitative Rea-time PCR LASSO Least absolute shrinkage and selection operator shRNA Short hairpin RNA Declarations Acknowledgments This study was supported by the grants from Zhongshan Social Welfare Science and Technology Research Project (No. 2022B3001). Author Contributions Fengyun Zhao : Writing–original draft, Investigation, Visualization, Methodology, Data curation, Validation. Zhaowei Ding : Writing–original draft, Visualization, Methodology, Data curation. Tianjiao Wu : Writing–original draft, Visualization. Mingfang Ji : Resources, Conceptualization. Fugui Li : Conceptualization, Validation, Software, Supervision, Funding acquisition, Writing–review & editing. Availability of data and materials: Databases analyzed for this study are available in online repositories. Detailed information can be found in the article. Ethics approval and consent to participate The protocol and any procedures involving the care in this study were reviewed and approved by the Use Committee of Zhongshan City People's Hospital. All the paraffin-embedded tissues of patients used in this study were obtained by informed patient consent. Competing interests The authors declare no conflicts of interest. References Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis. 2018;9(2):117. 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Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma. Front Immunol. 2022;13:989286. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–59. Li G, Zhang J, Liu Y, Cheng X, Sun K, Hong W, Sha K. Analyzing Prognostic Hub Genes in the Microenvironment of Cutaneous Melanoma by Computer Integrated Bioinformatics. Comput Intell Neurosci 2022, 2022:4493347. Yi L, Huang P, Zou X, Guo L, Gu Y, Wen C, Wu G. Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer. Pharmacol Res. 2020;161:105144. Malta TM, Sokolov A, Gentles AJ, Burzykowski T, Poisson L, Weinstein JN, Kaminska B, Huelsken J, Omberg L, Gevaert O, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell. 2018;173(2):338–e354315. Ren Q, Zhang P, Lin H, Feng Y, Chi H, Zhang X, Xia Z, Cai H, Yu Y. A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts. Front Immunol. 2023;14:1201573. Zhang H, Wang Y, Wang K, Ding Y, Li X, Zhao S, Jia X, Sun D. Prognostic analysis of lung adenocarcinoma based on cancer-associated fibroblasts genes using scRNA-sequencing. Aging. 2023;15(14):6774–97. Zhang J, Liu X, Huang Z, Wu C, Zhang F, Han A, Stalin A, Lu S, Guo S, Huang J, et al. T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing. Comput Biol Med. 2023;152:106460. Zhang P, Liu J, Pei S, Wu D, Xie J, Liu J, Li J. Mast cell marker gene signature: prognosis and immunotherapy response prediction in lung adenocarcinoma through integrated scRNA-seq and bulk RNA-seq. Front Immunol. 2023;14:1189520. Song P, Li W, Wu X, Qian Z, Ying J, Gao S, He J. Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on B cell marker genes to predict prognosis and immunotherapy response in lung adenocarcinoma. Cancer Immunol Immunother. 2022;71(10):2341–54. Sarvaria A, Madrigal JA, Saudemont A. B cell regulation in cancer and anti-tumor immunity. Cell Mol Immunol. 2017;14(8):662–74. Huang L, Wang Z, Chang Y, Wang K, Kang X, Huang R, Zhang Y, Chen J, Zeng F, Wu F, et al. EFEMP2 indicates assembly of M0 macrophage and more malignant phenotypes of glioma. Aging. 2020;12(9):8397–412. Komi DEA, Redegeld FA. Role of Mast Cells in Shaping the Tumor Microenvironment. Clin Rev Allergy Immunol. 2020;58(3):313–25. Abdulrahman Z, Santegoets SJ, Sturm G, Charoentong P, Ijsselsteijn ME, Somarakis A, Hollt T, Finotello F, Trajanoski Z, van Egmond SL et al. Tumor-specific T cells support chemokine-driven spatial organization of intratumoral immune microaggregates needed for long survival. J Immunother Cancer 2022, 10(2). Groom JR. Regulators of T-cell fate: Integration of cell migration, differentiation and function. Immunol Rev. 2019;289(1):101–14. Iturbide A, Pascual-Reguant L, Fargas L, Cebria JP, Alsina B, Garcia de Herreros A, Peiro S. LOXL2 Oxidizes Methylated TAF10 and Controls TFIID-Dependent Genes during Neural Progenitor Differentiation. Mol Cell. 2015;58(5):755–66. Xiong Y, Wang L, Xu S, Fu B, Che Y, Zaky MY, Tian R, Yao R, Guo D, Sha Z, et al. Small molecule Z363 co-regulates TAF10 and MYC via the E3 ligase TRIP12 to suppress tumour growth. Clin Transl Med. 2023;13(1):e1153. Soutoglou E, Demeny MA, Scheer E, Fienga G, Sassone-Corsi P, Tora L. The nuclear import of TAF10 is regulated by one of its three histone fold domain-containing interaction partners. Mol Cell Biol. 2005;25(10):4092–104. Zhao X, Lu J, Wu W, Li J. METTL14 inhibits the malignant processes of gastric cancer cells by promoting N6-methyladenosine (m6A) methylation of TAF10. Heliyon. 2024;10(11):e32014. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted 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-4752786","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331681507,"identity":"0305aa3f-cb38-4869-b2f1-83f928e539c3","order_by":0,"name":"Fengyun Zhao","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengyun","middleName":"","lastName":"Zhao","suffix":""},{"id":331681508,"identity":"d94b08c9-2b29-433e-9b56-2db67d97219d","order_by":1,"name":"Zhaowei Ding","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaowei","middleName":"","lastName":"Ding","suffix":""},{"id":331681510,"identity":"e42d504a-42c2-4dd6-ae49-62eaba0926b6","order_by":2,"name":"Tianjiao Wu","email":"","orcid":"","institution":"Guangdong Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tianjiao","middleName":"","lastName":"Wu","suffix":""},{"id":331681511,"identity":"4810480e-0b5f-4269-9709-9c2ea302bfbc","order_by":3,"name":"Mingfang Ji","email":"","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingfang","middleName":"","lastName":"Ji","suffix":""},{"id":331681512,"identity":"b84a16e9-a746-4a3f-a876-eba7470056c1","order_by":4,"name":"Fugui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIie3PMQrCMBTG8RcCcYnWMS56hZRCXXITF4uQSXcHwUghjl0VLyF4AcsDXQSvEE9guzvY1UEaN4f84dveb3gAodAf1mtWVVIMI0pL50UYANnvlioZbNlMepMDv+nseOdp34908tJ0LZITQgqwUpN2wi+ZGVikKYJ2cNEL00rEfOxii6whV0kMepDRszaZRZ7kxAo/Ijgx55sWklLmSbiON2appEBG5dTnl6iDj/wlxboo7rWrVqqdfDb97TwUCoVC33oD2YI93Vd28l8AAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fugui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-17 01:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4752786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4752786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62658805,"identity":"49091c38-2b5b-41c1-b83b-0dabbd747f02","added_by":"auto","created_at":"2024-08-17 02:19:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":441521,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLandscape of cell type in LUAD and normal tissues.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Workflow of this study. \u003cstrong\u003e(b)\u003c/strong\u003e UMAP plot of major nine cell types of LUAD. \u003cstrong\u003e(c)\u003c/strong\u003e UMAP plot of Site. Different cell types and sites are grouped by different colors.\u003cstrong\u003e (d)\u003c/strong\u003e The proportion of different cell types within each sample.\u003cstrong\u003e (e)\u003c/strong\u003e Expression of representative genes for different cell types. Bubble size reflects expression proportion, while color gradient from blue to red signifies higher expression levels.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/8e27f007879888f6657cfa2b.png"},{"id":62657636,"identity":"c2ef65b6-c781-4a40-a0ce-8aae7b724960","added_by":"auto","created_at":"2024-08-17 02:11:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1333725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional analysis of tumor stem cells.\u003c/strong\u003e \u003cstrong\u003e(a, b)\u003c/strong\u003e UMAP plot of 7 distinct tumor epithelial cell types with CytoTRACE stemness scores \u003cstrong\u003e(a)\u003c/strong\u003e and cell clusters \u003cstrong\u003e(b)\u003c/strong\u003e. \u003cstrong\u003e(c)\u003c/strong\u003eTumor stemness scores of 7 epithelial cell clusters using CytoTRACE. \u003cstrong\u003e(d)\u003c/strong\u003eVolcano Plot of differentially expressed genes in Epithelial_C1. \u003cstrong\u003e(e)\u003c/strong\u003eHallmark enrichment analysis of 7 epithelial cell clusters. The intensity of enrichment Increases from blue to red.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/350904765c756c94d5cf0698.png"},{"id":62656792,"identity":"b418597d-e19c-4e45-8fc9-9ee272b8e5e0","added_by":"auto","created_at":"2024-08-17 02:03:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":791648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the prognostic model TSCMS.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Overlapping CytoTRACE predicted stemness-associated genes and marker genes of Epithelial_C1. \u003cstrong\u003e(b)\u003c/strong\u003e Each independent variable’s trajectory and distribution for the lambda. \u003cstrong\u003e(c)\u003c/strong\u003e Expression of 49 TSCMS genes in TCGA-LUAD cohort. \u003cstrong\u003e(d-f) \u003c/strong\u003eKaplan-Meier plot of prognostic survival for TCGA\u003cstrong\u003e (d)\u003c/strong\u003e, validation sets GSE26939\u003cstrong\u003e (e)\u003c/strong\u003e and GSE72094 \u003cstrong\u003e(f)\u003c/strong\u003e. \u003cstrong\u003e(g-I)\u003c/strong\u003e ROC curves for TCGA \u003cstrong\u003e(g) \u003c/strong\u003etest set, validation sets GSE26939 \u003cstrong\u003e(h)\u003c/strong\u003e and GSE72094\u003cstrong\u003e(i)\u003c/strong\u003e. Red for 1-year, blue for 3-year, and black for 5-year survival rates.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/84875e6f0288eaf67b9ad5a3.png"},{"id":62656795,"identity":"8b4ae928-cc0a-4e43-b51e-209cec6a1daf","added_by":"auto","created_at":"2024-08-17 02:03:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1173775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration and functional analysis of TSCMS.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Fraction scores of 22 immune cell infiltration using CIBERSORTx software. \u003cstrong\u003e(b-e)\u003c/strong\u003e Box plots of immune scores \u003cstrong\u003e(b)\u003c/strong\u003e, stromal scores\u003cstrong\u003e(c)\u003c/strong\u003e, ESTIMATE scores \u003cstrong\u003e(d)\u003c/strong\u003e, and tumor purity \u003cstrong\u003e(e) \u003c/strong\u003efor TSCMS high- and low-risk groups using ESTIMATE software. \u003cstrong\u003e(f)\u003c/strong\u003e Enhanced GSEA Plot for TSCMS gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/7836195749504663ee623586.png"},{"id":62657637,"identity":"796e333a-9639-40e5-b259-b30ae716a867","added_by":"auto","created_at":"2024-08-17 02:11:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2127169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of immunotherapy efficacy using TSCMS in the IMvigor210 cohort.\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e Box plot of PD1 expression in high-risk and low-risk groups.\u003cstrong\u003e (b)\u003c/strong\u003e Box plot of PD-L1 expression in high-risk and low-risk groups. \u003cstrong\u003e(c)\u003c/strong\u003e Box plot of TSCMS scores in the anti-PD-L1 treatment group. \u003cstrong\u003e(d)\u003c/strong\u003e Box plot of CTLA4 expression in high-risk and low-risk groups. \u003cstrong\u003e(e)\u003c/strong\u003e Kaplan-Meier Plot of TSCMS in the IMvigor210 Cohort. \u003cstrong\u003e(f)\u003c/strong\u003e Bar chart showing treatment response proportions in high-risk and low-risk groups.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/81d586c6fdd8c6be4cc36934.png"},{"id":62656799,"identity":"97e08f7b-ea1c-4399-9b4a-e3aaaf71a281","added_by":"auto","created_at":"2024-08-17 02:03:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":441159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of anti-tumor drug sensitivity between high-risk and low-risk groups. (a)\u003c/strong\u003e Bortezomib, Pazopanib, AKT inhibitor VIII, AZD6482, CGP.082996, and CEP-701 demonstrated enhanced drug sensitivity in the high-risk group.\u003cstrong\u003e (b)\u003c/strong\u003e CCT007093, GDC.0449, and Lapatinib exhibited superior drug sensitivity in the low-risk group. Statistics based on Wilcoxon test.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/f638463e481f3cfa8d923f05.png"},{"id":62656798,"identity":"7b46bb15-a02b-47a0-bf12-20145665ad8e","added_by":"auto","created_at":"2024-08-17 02:03:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":674714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTAF10 plays oncogenic role in LUAD\u003c/strong\u003e. \u003cstrong\u003e(a)\u003c/strong\u003e TAF10 mRNA expression levels in different tumors and corresponding normal tissues from TCGA and GTEx database by SangerBox. The abbreviations were showed in Supplementary Table S13. \u003cstrong\u003e(b)\u003c/strong\u003e The stemness features analyses of TAF10 across different types of tumors in the TCGA database. \u003cstrong\u003e(c, d)\u003c/strong\u003e Overall survival\u003cstrong\u003e (c)\u003c/strong\u003eand disease free survival \u003cstrong\u003e(d)\u003c/strong\u003e analyses of TAF10 in LUAD samples from the TCGA database. \u003cstrong\u003e(e, f)\u003c/strong\u003e The mRNA and protein expression level of TAF10 in human normal bronchial epithelioid cells (16HBE) and LUAD cell lines. \u003cstrong\u003e(g)\u003c/strong\u003eRepresentative IHC analysis for TAF10 expression in LUAD tumor tissues. \u003cstrong\u003e(h)\u003c/strong\u003eKnockdown of TAF10 in LUAD cells was confirmed by western blot. \u003cstrong\u003e(i)\u003c/strong\u003e The effect of knocking down TAF10 on colony formation in LUAD cells was tested using a colony formation assay. \u003cstrong\u003e(j)\u003c/strong\u003e LUAD cell lines were steady transfected with shCtl or shTAF10 for 24 h, 48 h and 72 h and cell viability was measured by CCK-8 assay. Data are means ± SD.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/e89c3bdef6eb27576381cbb1.png"},{"id":65393984,"identity":"78ece51a-2b4f-45c2-88fa-e241d1f7550f","added_by":"auto","created_at":"2024-09-27 01:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7813189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/bb9f978c-8594-4ebc-bc80-0261b6919839.pdf"},{"id":62657638,"identity":"4a9f97ca-a3c7-4573-8601-49fd8ba35580","added_by":"auto","created_at":"2024-08-17 02:11:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2953498,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4752786/v1/d12a098f1b0406fcd6a1fb9f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integration of Single-cell and Bulk RNA Sequencing to Identify a Distinct Tumor Stem Cells and Construct a Novel Prognostic Signature for Evaluating Prognosis and Immunotherapy in LUAD","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung adenocarcinoma (LUAD), a prevalent and challenging malignancy of the lungs, has exhibited a steady rise in its incidence in recent years [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Single-cell RNA sequencing (scRNA-seq) has emerged as a potent tool for delving deeper into the intricate landscape of this disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Characterized by rapid technological advancements, single-cell technologies have garnered substantial attention and application across diverse solid and hematologic malignancies, such as utilizing scRNA-seq to unveil the landscape of infiltrating T cells in liver cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and investigating the clonal evolution of circulating tumor cells within peripheral blood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In LUAD, by furnishing high-resolution gene expression profiles, single-cell sequencing has endowed researchers with an unprecedented ability to decipher the intricate heterogeneity and underlying molecular mechanisms driving the pathogenesis [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLeveraging the capabilities of single-cell sequencing, researchers are navigating complex gene expression signatures, functional attributes, and intricate cellular interactions within distinct subpopulations in LUAD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Of particular significance within the landscape of LUAD research is the burgeoning interest in tumor stem cells, which are recognized for their pivotal contribution to tumor initiation, progression, therapeutic resistance, and prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This systematic exploration has unveiled the existence of tumor stem cells within the intricate tapestry of cancer and has underscored their potential role in orchestrating the course of the disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn pursuit of refining prognostic, numerous studies have embarked on the development of prognostic model [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The publicly accessible databases, including TCGA and GEO repositories, provide abundant LUAD samples and associated clinical data, thereby facilitating the construction and rigorous validation of these prognostic models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, we harnessed the single-cell sequencing methodologies to unravel the intricate tapestry of tumor stem cells in LUAD, discern hallmark gene signatures, and establish refined prognostic risk models. First, a publicly available single-cell RNA sequencing (scRNA-seq) dataset (11 normal and 11 LUAD) was annotated, obtaining 88,144 cells and 16 clusters. Next, we assessed the stemness of 7,252 tumor-derived epithelial cells using CytoTRACE software, delineated 7 distinct cell clusters, and identified Epi C1 exhibited the highest stemness potential. Subsequently, we screened 49 cancer stem cell-related genes which were most relevant to the prognosis of LUAD patients to construct the tumor stem cell marker signature (TSCMS) prognostic risk model. This model had the potential to predict prognosis, efficacy of immune checkpoint blockade, and responsiveness to immunotherapy in LUAD patients. Moreover, we demonstrated that TAF10, the top one gene in terms of the stemness score and correlation with patient prognosis, played an oncogenic role in LUAD. These findings propose the TSCMS model exhibits an excellent predictive capacity for the prognosis of LUAD patients, and provide potential targets for LUAD treatment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eSingle-cell sequencing data (scRNA-seq) and Bulk RNA sequencing data were obtained online from the GEO and TCGA databases. The single-cell data originated from GEO (GSE1311907), while the Bulk RNA data were downloaded from TCGA and GEO (TCGA-LUAD cohort, GSE26939 and GSE72094). The LUAD immunotherapy cohort \u0026lsquo;IMvigor210CoreBiologies\u0026rsquo; was sourced from previously published research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Data for drug IC50 predictions were acquired from a statistical study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All these datasets were sourced from public databases or shared by others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePreprocessing of ScRNA-seq Data\u003c/h2\u003e \u003cp\u003eEmploying the R package Seurat, we imported the unprocessed expression matrix [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Subsequently, we performed filtering to include single-cell data originating from both LUAD and normal tissues. Cells exhibiting mitochondrial gene content exceeding 30% and those manifesting expression of more than 10,000 genes were excluded from the analysis. For normalization, we applied the SCTransform function, which mitigates technical noise and ensures uniform scaling across cells. Subsequently, the RunPCA function was applied with the parameter npcs\u0026thinsp;=\u0026thinsp;50, and the RunUMAP function used parameters reduction=\"pca\" and dim\u0026thinsp;=\u0026thinsp;1:30. The FindNeighbors function was employed with parameters reduction=\"pca\" and dims\u0026thinsp;=\u0026thinsp;1:30. Leveraging these neighborhood relationships, clustering was performed with the FindClusters function, wherein a resolution parameter of 0.1 was chosen to delineate 16 distinct cell clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnnotation of Cellular Subpopulations\u003c/h2\u003e \u003cp\u003eAfter obtaining the 16 clusters, we proceeded to annotate these clusters with cell types based on the expression of specific marker genes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Immunological cells were identified using a spectrum of markers, including PTPRC, and various subclasses such as B cells (CD79A, and MS4A1), plasma cells (IGLC2, and IGHM), T cells (CD3D, and CD3E), monocytes (CD14, and S100A8), NK cells (NKG7, and GNLY), mast cells (CPA3, and KIT), and macrophages (CD68, and MARCO). Additionally, non-immune cell types were characterized, including epithelial cells (EPCAM, and KRT8), endothelial cells (PECAM1, and VWF), and fibroblasts (COL1A1, and DCN). This analysis ultimately yielded the identification of 10 major cell types within the dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Analysis\u003c/h2\u003e \u003cp\u003eThe identification of highly expressed genes in scRNA-seq cells was performed using the Seurat package's FindAllMarkers function with parameters set as only.pos\u0026thinsp;=\u0026thinsp;T and logfc.threshold\u0026thinsp;=\u0026thinsp;0.25, while keeping other parameters as default. Differential gene analysis for the epithelial cell cluster in scRNA-seq was presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and results were visualized using the R package EnhancedVolcano. For Bulk RNA-seq differential analysis, the DESeq2 package was utilized with default parameters. Differential analysis was conducted by grouping samples into high and low-risk categories based on the median, and the results of differentially expressed genes between the high-risk and low-risk groups are available in Supplementary Table S7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Tumor Epithelial Cell Stemness\u003c/h2\u003e \u003cp\u003eCytoTRACE utilizes gene expression and an intrinsic stemness gene set to predict cell stemness at the single-cell level [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To identify the clusters of tumor epithelial cells with the highest stemness or lowest differentiation, we employed the CytoTRACE pipeline from the R package. The results of stemness-related genes (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.3) can be found in Supplementary Table S3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe enrichment analysis of seven types of tumor tissue-derived epithelial cells in scRNA-seq was conducted using the R package GSVA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Initially, 50 tumor Hallmark gene sets were obtained using the R package msigdbr. The GSVA function was applied with the parameter method=\"ssgsea\" to perform enrichment analysis on the expression matrices of the seven epithelial cell types. The ssgsea enrichment scores can be found in Supplementary Table S2. For the GSEA enrichment analysis of the TSCMS model, the R package fgsea was used with default parameters. Differential genes between high and low-risk groups based on the TCGA training set were ranked according to their FoldChange. The enrichment results of KEGG pathways from GSEA can be found in Supplementary Table S10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of the Prognostic Risk Model TSCMS\u003c/h2\u003e \u003cp\u003eIntersecting the stemness-related genes with the highly expressed genes within the tumor epithelial cell cluster Epi C1, we conducted a univariate Cox regression analysis to ascertain the prognostic significance of these overlapping genes in relation to overall survival among LUAD patients sourced from the TCGA dataset. Genes yielding a p-value of less than 0.05 were designated as prognostic candidates. Subsequently, we subjected the identified prognostic genes to a least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression, leveraging the \"glmnet\" package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Employing ten-fold cross-validation, we curated a gene list featuring nonzero coefficients, culminating from an optimal model feature selection process (Supplementary Table S5). The resultant risk model was meticulously formulated by a linear summation of the products of genes and their corresponding risk coefficients. Patient stratification into low-risk or high-risk groups hinged upon a median threshold (Supplementary Table S6). To methodically validate the prognostic efficacy of the TSCMS model, we computed the area under the curve (AUC) utilizing the \"timeROC\" package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Survival analysis, grounded in the Kaplan\u0026ndash;Meier methodology, was adeptly undertaken. Further statistical assessment of differences was facilitated through the application of the log-rank test, seamlessly integrated within the R package \"survminer\" [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, the predictive robustness of the model was subject to rigorous validation via survival analysis and AUC computation across two distinct GEO datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmunocellular Infiltration Analysis\u003c/h2\u003e \u003cp\u003eImmune cell infiltration analysis was conducted by using R Packages CIBERSORT and ESTIMATE in TCGA-LUAD Patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The infiltration scores for 22 distinct immune cell types were computed using CIBERSORT (Supplementary Table S11). Based on the median risk score, patients were divided into two groups, and differences in immune cell infiltration across the 22 types were compared between these groups. Furthermore, the ESTIMATE package was utilized to calculate overall immune scores, stromal scores, ESTIMATE scores, and tumor purity (Supplementary Table S12). Following the division into two groups based on the median risk score, inter-group differences were assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Immunotherapy Response\u003c/h2\u003e \u003cp\u003eThe IMvigor210 cohort is an immunotherapy-focused dataset for bladder cancer (BLCA), encompassing gene expression matrices, patient clinical information [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and records of immunotherapy responses. Patients were stratified into two groups based on the median cutoff of their risk scores. Comparative analysis was performed to assess differences in the expression of immune checkpoint markers between the two groups, as well as disparities in patients' immunotherapy responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDrug Response Prediction\u003c/h2\u003e \u003cp\u003eWe conducted drug response prediction using the pRRophetic package [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The gene expression profiles of high and low-risk groups were employed to estimate the IC50 values for various commonly used clinical or preclinical anti-tumor drugs. By leveraging statistical methods, we identified drugs with significantly distinct IC50 values between these risk groups (Supplementary Table S8 and S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression and Bioinformatics Analysis of TAF10 from Public Database\u003c/h2\u003e \u003cp\u003eThe expression and RNA expression-based stemness score (RNAss) data for TAF10 in various tumor types in TCGA database were obtained from the SangerBox database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://SangerBox.com/Tool\u003c/span\u003e\u003cspan address=\"http://SangerBox.com/Tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture\u003c/h2\u003e \u003cp\u003eHuman LUAD cell lines (A549, PC9, H1975) and human normal bronchial epithelioid cells (16HBE) were purchased and authenticated from the ATCC. All cell lines were maintained in either RPMI-1640 medium or DMEM (Thermo Fisher Scientific, MA, USA) medium supplemented with 10% fetal bovine serum. Cells were cultured at 37\u0026deg;C in a humidified atmosphere with 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell Proliferation and Clone Formation Assay\u003c/h2\u003e \u003cp\u003eCell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay according to the manufacturer's instructions. For the clone formation assay, cells were seeded in 6-well plates at a density of 500 cells/well and incubated at 37\u0026deg;C. The medium was refreshed every 3 days until colonies formed. After cell fixation with paraformaldehyde, crystal violet was used to stain the cells. Colonies with more than 50 cells per colony were counted under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Rea-time PCR (qRT‒PCR)\u003c/h2\u003e \u003cp\u003eFirst-strand cDNA was synthesized using the GenStar A212-05 kit according to the standard protocol. qPCR was performed using the SYBR Green Supermix and CFX96 real-time PCR detection system. The mRNA expression of genes was analyzed using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The primers for TAF10 were 5\u0026prime;-ATTGATGCCATACTCGCTGAG-3\u0026prime; and 5\u0026prime;- GAAGTGAAGCCCGTAGTGTCC-3\u0026prime;, and the primers for β-actin were 5\u0026prime;-TCGTGCGTGACATTAAGGAG-3\u0026prime; and 5\u0026prime;-ATGCCAGGGTACATGGTGGT-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot\u003c/h2\u003e \u003cp\u003eCells were lysed with RIPA buffer supplemented with protease inhibitor and boiled at 95\u0026deg;C for 5 min. Equal amounts of protein were added to sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane. The membrane was blocked with 5% nonfat dry milk for 1 h at room temperature and then incubated with primary antibodies overnight at 4\u0026deg;C. The next day, the membranes were incubated for 1 h at room temperature with HRP-conjugated anti-rabbit or anti-mouse secondary antibodies. Immunoreactive proteins were visualized using SuperSignal West Dura Chemiluminescent. Antibodies for TAF10 (NBP1-80706) were purchased from NOVUS Biologicals, and GAPDH antibody (H11459) was purchased from Sigma as a loading control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003eAll paraffin-embedded tissues of patients in this study were obtained with informed patient consent. For immunohistochemistry staining, deparaffinized and rehydrated sections were boiled in Na-citrate buffer (10 mM, pH 6.0) for 30 min for antigen retrieval. The sections were incubated with primary antibodies and developed using the Ultra Vision Detection System. Images were captured using an Olympus IX51 microscope and processed using cellSens Dimension software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePlasmids and Cell Transfections\u003c/h2\u003e \u003cp\u003eShort hairpin RNA (shRNA) sequences targeting TAF10 were cloned into psiF-copGFP vectors (System Biosciences, Mountain View, CA). The shRNA sequences for TAF10 were 5\u0026prime;-CCAGAAATTCATCTCAGATAT-3\u0026prime;, and the sequence for the negative control (shCtl) was 5\u0026prime;-GGTGTGCAGTTGGAATGTA-3\u0026prime;. To establish stably transfected cell lines, HEK-293T cells were transfected with lentivirus produced using the second-generation packaging system pMD2.G and psPAX2, and the virus was harvested 48 h after transfection. The LUAD cell lines were transduced with the virus in the presence of 8 \u0026micro;g/mL polybrene and screened with puromycin for 7 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn appropriate scenarios, we employed either the Student\u0026rsquo;s t-test or the Wilcoxon rank-sum test to assess the significance of differences between groups. The selection of the test depended on the distribution of the data and the assumption of normality. For survival analysis, the Log-Rank test was utilized to determine the significance of survival differences between different groups or conditions. P value less than 0.05 was considered statistically significant. Statistical significance levels were denoted as follows: * for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** for p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** for p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and **** for p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eWorkflow and Cell Population Landscape in LUAD\u003c/h2\u003e \u003cp\u003eTo explore the potential functions of LUAD tumor stem cells, we collected bulk RNA-seq data from TCGA-LUAD and GEO datasets (GSE26939 and GSE72094), as well as single-cell RNA-seq data from the GEO dataset (GSE131907). Using scRNA-seq, we predicted the stemness score of tumor epithelial cells. Then, we constructed a LUAD prognostic model based on tumor stemness genes and further validated its predictive ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). First, we conducted quality control on all cells, applying filters with a minimum cell count of 3, a minimum feature count of 200, and mitochondrial gene content of less than 30 (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Next, we annotated a total of 22 samples (11 normal and 11 LUAD) from the single-cell dataset, comprising 88,144 cells and 16 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, c, and Supplementary Figure S2). Based on the expression of cell markers within clusters, we identified ten major cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Compared to normal lung tissue, LUAD exhibited reduced infiltration of NK cells and macrophages. However, tumor patients demonstrated heterogeneity, with different LUAD samples showing varying proportions of epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We used common cell markers such as EPCAM for epithelial cells, PECAM1 for endothelial cells, PTPRC for immune cells, and COL1A1 for fibroblasts to define each cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Therefore, LUAD exhibits substantial tumor heterogeneity, with varying compositions of tumors and their microenvironments among different patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePrediction of Tumor Epithelial Stem Cells\u003c/h2\u003e \u003cp\u003eFurther exploration of tumor stem cells involved the selection of 7252 tumor-derived epithelial cells for calculating stemness scores using the CytoTRACE software (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). After applying dimensionality reduction and clustering techniques, 7 distinct cell clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Comparing the CytoTRACE-predicted stemness scores across these 7 tumor epithelial cell clusters revealed that Epi C1 exhibited the highest stemness potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Subsequent differential gene analysis of Epi C1 highlighted elevated expression of genes such as CDKN2A, TMSB10, SOO2A, PTGS2, and SNCG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, the Hallmark GSVA enrichment analysis demonstrated that Epi C1 displayed higher enrichment scores in pathways associated with hypoxia, EMT, Kras signaling, MYC signaling, as well as E2F targets and G2M checkpoint, which are closely linked to cell cycle regulation, compared to other epithelial cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and Table S2). Thus, the Epi C1 cluster is likely to represent a subpopulation of stem-like epithelial cells within LUAD tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and Validation of the Prognostic Model TSCMS\u003c/h2\u003e \u003cp\u003eTo investigate the impact of stem-like tumor epithelial cells on LUAD patients, we intersected 1068 highly expressed genes in Epi C1 with 2509 CytoTRACE-computed genes showing correlation (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.3), resulting in 964 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, 3). These genes were utilized for univariate Cox regression analysis to predict their association with survival in LUAD patients. We used the LUAD mRNA count expression matrix and corresponding clinical information from the TCGA database as the training set. Out of the 964 genes, 92 genes with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Supplementary Figure S3) were further subjected to Lasso regression and multiple-factor Cox regression with tenfold cross-validation, ultimately selecting 49 genes with non-zero coefficients as features to construct tumor stem cell marker signature (TSCMS) prognostic risk model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and Supplementary Table S4, 5). The risk score was calculated based on the cumulative expression values of the genes multiplied by their corresponding coefficients, and the TCGA training set samples were divided into high and low-risk groups using the median risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and Supplementary Table S6). In the training set, the model significantly stratified patients' survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with area under the curve (AUC) values of 0.818, 0.851, and 0.871 for 1-year, 3-year, and 5-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, g). Furthermore, we validated TSCMS using two independent external LUAD datasets, GSE26939 and GSE72094. The model demonstrated robust prognostic stratification ability in GSE26939 (p\u0026thinsp;=\u0026thinsp;0.012) and GSE72094 (p\u0026thinsp;=\u0026thinsp;0.00015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f), with corresponding AUC values of 0.707, 0.637, and 0.595 for 1-year, 3-year, and 5-year survival in GSE26939 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), and 0.702, 0.667, and 0.751 in GSE72094 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). In conclusion, the newly developed prognostic risk model based on stem-like tumor epithelial cells exhibits an excellent predictive capacity for the prognosis of LUAD patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eThe Association between TSCMS and Immune Cell Infiltration in the TME\u003c/h2\u003e \u003cp\u003eAs immune cells wield a pivotal role in tumor immunity and promotion, we delved into the relationship between TSCMS and immune cell infiltration within LUAD patients. Leveraging the cibersortx program, we investigated the infiltration of 22 distinct immune cell types. Notably, the high-risk group demonstrated diminished levels of B cell naive, CD4\u003csup\u003e+\u003c/sup\u003e T cell memory resting, monocytes, and mast cells when compared with the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Conversely, macrophage M0 infiltration exhibited heightened levels within the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Moreover, employing the ESTIMATE program, we calculated infiltration scores for both high and low-risk cohort, the high-risk group revealed markedly reduced immune scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), lower ESTIMATE scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), and heightened tumor purity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Remarkably, no pronounced disparities emerged in stromal scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). After partitioning TCGA-LUAD samples into two distinct groups according to the median risk score, we conducted an analysis of differential gene expression (Supplementary Table S7), subsequently followed by GSEA enrichment analysis ranked by fold change. Specifically, the high-risk group exhibited enrichments in pivotal pathways such as cell cycle regulation, DNA repair, and P53 signaling, while the low-risk group showcased enrichments in chemokine signaling, chemokine receptor interactions, and T cell receptor signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). These results imply a positive correlation between the TSCMS risk score and tumor cell proliferation, coupled with a negative correlation with immune functionality. The diminished predictive prognosis of TSCMS might be attributed to its association with reduced immune infiltration capacity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eTSCMS could Predict Immunotherapy Benefits in LUAD Patients\u003c/h2\u003e \u003cp\u003eBuilding upon the pivotal role of TSCMS in immune cell infiltration, we further explored its predictive influence on immune checkpoint blockade and immunotherapy response. Firstly, within the IMvigor210 cohort, we analyzed immune checkpoint expression including PD1, PD-L1, and CTLA4. Notably, there were no significant differences in PD1 and CTLA4 expression between high and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, d), while PD-L1 expression was higher in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Evaluating the response to anti-PD-L1 therapy, the risk score was notably lower in the R (complete response/partial response; CR/PR) group compared to the NR (stable disease/progressive disease; SD/PD) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Moreover, the stratification of patient prognosis by TSCMS within this cohort exhibited statistically significant implications (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). In terms of treatment response, the low-risk group showed a 9% higher population of CR/PR compared to the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). In conclusion, these findings suggest that patients with a lower risk score may benefit more from anti-PD-L1 therapy, indicating TSCMS as a potentially helpful biomarker for anti-PD-L1 treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eTSCMS-Based Prediction of Anti-Tumor Drug Efficacy\u003c/h2\u003e \u003cp\u003eIn addition to immunotherapy, chemotherapy remains a pivotal approach in the battle against tumors. Thus, we computed the IC50 sensitivities of commonly used clinical or preclinical anti-tumor drugs between high and low-risk TSCMS groups. Among the findings, IC50 values for 61 drugs were observed to be lower in the high-risk group in comparison to the low-risk group (Supplementary Table S8). Furthermore, for 8 drugs, the IC50 values in the low-risk group were significantly lower than those in the high-risk group (Supplementary Table S9). Through prioritizing results based on significance, we revealed the top 6 drugs with better sensitivity in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) and the top 3 drugs with better sensitivity in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) in terms of IC50 outcomes. These results hold the potential to offer invaluable guidance for personalized treatment strategies in LUAD patient.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eTAF10 plays Oncogenic Role in LUAD\u003c/h2\u003e \u003cp\u003eTo validate the role of the genes used to construct the TSCM prognostic risk model, we selected TAF10 for further investigation, as it was the most relevant to the prognosis of LUAD patients among these 49 genes. Based on the data from TCGA and GTEx databases, we found that the mRNA level of TAF10 in most tumors (including LUAD) was upregulated compared with corresponding normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Notably, the stemness features analyses showed that TAF10 was positively correlated with the RNA expression-based stemness scores (RNAss) in a variety of tumors, including LUAD (R\u0026thinsp;=\u0026thinsp;0.325, p\u0026thinsp;=\u0026thinsp;0.009) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), and high TAF10 expression was correlated with poor prognosis in LUAD patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, d). Next, we analyzed the mRNA expression of TAF10, S100P, PAFAH1B3, CCT6A, and DCBLD2 (the top five among the 49 genes) in LUAD cell lines. RT-PCR results indicated that the mRNA expression of all these genes was significantly higher in LUAD cell lines than in human normal bronchial epithelioid cells (16HBE) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), with TAF10 exhibited the greatest fold change in expression among these genes. WB results further confirmed the high protein expression of TAF10 in LUAD cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). Furthermore, the IHC results showed that TAF10 was highly expressed in clinical LUAD samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). To further investigate the role of TAF10 in LUAD, we used loss-of-function method to evaluate the impact of TAF10 on LUAD cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eh). We found that TAF10 silencing significantly reduced the ability of colony formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei) and suppressed cell proliferation of LUAD cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ej). These results indicate that up-regulated TAF10 expression promoted cell proliferation in LUAD, which demonstrate TAF10 as an ideal gene for further mechanistic studies in LUAD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious efforts have been dedicated to establishing prognostic models in LUAD based on tumor-associated cancer-associated fibroblasts (CAFs) and immune cells [\u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, we focused on the tumor stem cell in LUAD. Through the integration of stemness-associated genes and scRNA-seq datasets, we have effectively pinpointed distinct epithelial cell clusters originating from the tumor microenvironment that exhibit stemness characteristics. This endeavor led us to the development of a novel prognostic risk model termed TSCMS. Notably, this model doesn't solely rely on clinical parameters but also provides a heightened precision in predicting patient survival outcomes, thereby serving as a robust aid for informed clinical decision-making.\u003c/p\u003e \u003cp\u003eThe model TSCMS was constructed from a set of 49 key genes, among which TAF10, S100P, PAFAH1B3, CCT6A, DCBLD2, CCDC85B, PSMD11, TFAP2A, TM4SF1, and DRG1 hold prominent coefficients. Notably, the TSCMS exhibits robust predictive capacity. In the TCGA training dataset, it reveals a substantial median survival difference of more than 5 years between high-risk and low-risk patients. Similarly, across two independent external validation datasets, the TSCMS indicates a median survival discrepancy of 3 years between the high and low-risk patient groups. Furthermore, when evaluating the accuracy of TSCMS for predicting survival rates at 1 year, 3 years, and 5 years, our results consistently surpass an average threshold of 0.7 across the three cohorts. Importantly, in comparison to risk models proposed by Ren et al. centered on CAFs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and Zhang et al. focusing on T-cell markers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], our TSCMS displays superior accuracy in prognosticating patient survival. This underscores the robust predictive power of TSCMS for patient survival.\u003c/p\u003e \u003cp\u003eImmune cells play a pivotal role within the tumor microenvironment, exerting influence over tumor development and therapeutic responses. Employing methodologies such as CIBERSORT and ESTIMATE, we observed distinctions in the distribution of B cells, monocytes, mast cells, and macrophages between the high-risk and low-risk groups. Sarvaria et al. have highlighted the crucial role of B cells in promoting inflammation and carcinogenesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Macrophage type M0 exhibited significantly higher infiltration in the high-risk group, a trend consistent with the findings of Huang et al., who reported M0 macrophages promoting malignant growth in glioma [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mast cells can promote angiogenesis by releasing classical pro-angiogenic factors, and support tumor invasion by releasing matrix metalloproteinases [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], we found the proportion of activated mast cells is notably higher in the high-risk group compared to the low-risk group. Additionally, the low-risk group exhibited higher enrichment of immune-related pathways, including T cell receptor signaling and chemokine-chemokine receptor signaling pathways. In the tumor microenvironment, tumor infiltration of T cells was driven by chemokines [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and T cells integrate chemokine signals to enhance antitumor responses in peripheral tissues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Those results suggest a potential association between tumor stemness and immune infiltration, thereby providing valuable leads for further exploration into immune-based therapies.\u003c/p\u003e \u003cp\u003eImmunotherapy and drug treatment are effective strategies in combating cancer. TSCMS has demonstrated robust predictive capability within the IMvigor210 immunotherapy cohort. In the high-risk group, PD-L1 expression levels are significantly elevated compared to low-risk patients. Additionally, patients who respond favorably to anti-PD-L1 treatment exhibit lower risk scores. Moreover, we successfully predicted the response of different risk groups to various anti-cancer drugs. These findings provide substantial support for personalized treatment and drug selection, holding the potential to make a positive impact in clinical practice.\u003c/p\u003e \u003cp\u003eOut of the 49 key genes, TAF10 holds the highest prognostic correlation coefficient. Numerous studies have demonstrated that TAF10 plays an oncogenic role within a wide variety of tumors, including transcription, the cell cycle, and apoptosis [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], such as in gastric cancer cells, the high expression of TAF10 plays an important role in maintaining tumor cell survival [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. We demonstrated that TAF10 was over-expressed in LUAD cell lines and tumor tissues of clinical patients, and high TAF10 expression was correlated with poor prognosis in LUAD patients. Silencing TAF10 inhibited LUAD cell proliferation and clone formation, which provided a new target for the targeted therapy of LUAD.\u003c/p\u003e \u003cp\u003eHowever, our study has certain limitations. Firstly, the main limitation lies in the absence of further experiments to investigate the association between stemness and TAF10. Secondly, the clinical utility of the TSCMS needs independent validation in a larger cohort of LUAD patients.\u003c/p\u003e \u003cp\u003eIn conclusion, our research emphasizes the prognostic value of the TSCMS model in evaluating the clinical outcomes of LUAD patients, provides crucial insights into immune cell infiltration and therapeutic response, and identifies TAF10 as a novel therapeutic target for LUAD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLUAD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eCSCs\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cancer stem cells\u003c/p\u003e\n\u003cp\u003eTSCMS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor stem cell marker signature\u003c/p\u003e\n\u003cp\u003eOS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall survival\u003c/p\u003e\n\u003cp\u003eTME\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eKM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kaplan\u0026ndash;Meier\u003c/p\u003e\n\u003cp\u003eGSVA\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene set variation analysis\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eLogFC\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;LogFoldChange\u003c/p\u003e\n\u003cp\u003escRNA-seq \u0026nbsp; \u0026nbsp; \u0026nbsp;Single-Cell RNA Sequencing\u003c/p\u003e\n\u003cp\u003eTCGA\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003ePCA\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Principal components analysis\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kyoto encyclopedia of genes and genomes\u003c/p\u003e\n\u003cp\u003eGSEA\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receiver operating characterastic\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eRNAss\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;RNA expression-based stemness score\u003c/p\u003e\n\u003cp\u003eCCK-8\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cell Counting Kit-8\u003c/p\u003e\n\u003cp\u003eqRT‒PCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Quantitative Rea-time PCR\u003c/p\u003e\n\u003cp\u003eLASSO\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eshRNA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Short hairpin RNA\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the grants from Zhongshan Social Welfare Science and Technology Research Project (No. 2022B3001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFengyun Zhao\u003c/strong\u003e: Writing\u0026ndash;original draft, Investigation, Visualization, Methodology, Data curation, Validation. \u003cstrong\u003eZhaowei Ding\u003c/strong\u003e: Writing\u0026ndash;original draft, Visualization, Methodology, Data curation.\u0026nbsp;\u003cstrong\u003eTianjiao Wu\u003c/strong\u003e: Writing\u0026ndash;original draft, Visualization. \u003cstrong\u003eMingfang Ji\u003c/strong\u003e: Resources, Conceptualization. \u003cstrong\u003eFugui Li\u003c/strong\u003e: Conceptualization, Validation, Software, Supervision, Funding acquisition, Writing\u0026ndash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eDatabases analyzed for this study are available in online repositories. Detailed information can be found in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol and any procedures involving the care in this study were reviewed and approved by the Use Committee of Zhongshan City People\u0026apos;s Hospital. All the paraffin-embedded tissues of patients used in this study were obtained by informed patient consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDenisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma. Cell Death Dis. 2018;9(2):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Ding Y, Liu S, Wang C, Zhang E, Chen C, Zhu M, Zhang J, Zhu C, Ji M, et al. 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Heliyon. 2024;10(11):e32014.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"single-cell RNA, gene signature, prognostic model, LUAD, tumor stem cell","lastPublishedDoi":"10.21203/rs.3.rs-4752786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4752786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCancer stem cells (CSCs) play a crucial role in the progression of lung adenocarcinoma (LUAD).This study aimed to explore the gene signatures of tumor stem cells in LUAD through Single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) data, and establish a tumor stem cell marker signature(TSCMS)prognostic risk model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe LUAD scRNA-seq data and bulk RNA-seq data from the GEO and TCGA databases were collected. CytoTRACE software was used to quantify the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) was performed to identify potential biological functions in different clusters. The TSCMS prognostic risk model was constructed using Lasso-Cox regression analysis, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. The Cibersortx algorithm was used to evaluate immune infiltration, and drug response prediction was conducted using the pRRophetic package. Functional investigations of TAF10 in LUAD cells were performed using bioinformatics analysis, qRT-PCR, Western blot, Immunohistochemistry, cell proliferation and clone formation assay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven distinct cell clusters were identified by CytoTRACE (Epi C1 to C7), with Epi C1 demonstrating the highest stemness potential. The TSCMS prognostic risk model incorporated 49 tumor stemness-related genes, and high-risk patients exhibited reduced immune scores, lower ESTIMATE scores, and increased tumor purity. Furthermore, significant differences in immune landscapes and chemotherapy sensitivity were observed between high and low risk groups. TAF10 was found to be positively correlated with the RNA expression-based stemness score (RNAss) in various tumors, including LUAD. And we demonstrated that TAF10 was over-expressed in LUAD cell lines and tumor tissues of clinical patients, and high TAF10 expression was correlated with poor prognosis in LUAD patients. Silencing TAF10 inhibited LUAD cell proliferation and clone formation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur investigation highlights the prognostic utility of the TSCMS model for evaluating the clinical outcomes of LUAD patients, uncovering critical insights into immune cell infiltration and therapeutic response, and positions TAF10 as a novel therapeutic target for LUAD.\u003c/p\u003e","manuscriptTitle":"Integration of Single-cell and Bulk RNA Sequencing to Identify a Distinct Tumor Stem Cells and Construct a Novel Prognostic Signature for Evaluating Prognosis and Immunotherapy in LUAD","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 02:03:32","doi":"10.21203/rs.3.rs-4752786/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c08d6ea-c474-4f0e-9155-28dc537402ea","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-27T01:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 02:03:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4752786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4752786","identity":"rs-4752786","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}