Detecting gene expression in Caenorhabditis elegans

Idiopathic pulmonary arterial hypertension (IPAH) is a rare and severe cardiopulmonary disease with a challenging prognosis, and its underlying pathogenesis remains elusive. A comprehensive understanding of IPAH is crucial to unveil potential diagnostic markers and therapeutic targets. In this study, we investigated cellular heterogeneity and molecular pathology in IPAH using single-cell RNA sequencing (scRNA-seq) analysis. Our scRNA-seq results revealed significant alterations in three crucial signaling pathways in IPAH: the hypoxia pathway, TGF β pathway, and ROS pathway, primarily attributed to changes in gene expression within arterial endothelial cells. Moreover, through bulk RNA sequencing analysis, we identified differentially expressed genes (DEGs) enriched in GO and KEGG pathways, implicated in regulating cell adhesion and oxidative phosphorylation in IPAH lungs. Similarly, DEGs-enriched pathways in IPAH arterial endothelial cells were also identified. By integrating DEGs from three IPAH datasets and applying protein-protein interaction (PPI) analysis, we identified 12 candidate biomarkers. Subsequent validation in two additional PAH datasets led us to highlight five potential biomarkers (CTNNB1, MAPK3, ITGB1, HSP90AA1, and DDX5) with promising diagnostic significance for IPAH. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed significant differences in the expression of these five genes in pulmonary arterial endothelial cells from PAH mice. In conclusion, our findings shed light on the pivotal role of arterial endothelial cells in the development of IPAH. Furthermore, the integration of single-cell and bulk RNA sequencing datasets allowed us to pinpoint novel candidate biomarkers for the diagnosis of IPAH. This work opens up new avenues for research and potential therapeutic interventions in IPAH management.

In a mouse model of menstruation stromal cells respond to breakdown of the tissue by changing their identity to become epithelial cells that are incorporated into the luminal epithelium which is rapidly 'healed' without scarring.

Identification of inflammation-related biomarkers and therapeutic targets for neurogenic bladder fibrosis via multi-omics analysis.

Introduction: Cancers display intratumoral heterogeneity which interferes with the precise analyses of the tumor entity, and which may affect therapeutic outcomes of targeted treatments. The aim of this study was to evaluate the feasibility of single cell RNA sequencing on primary breast cancer cells and to demonstrate the key gene expression signatures and transcriptome heterogeneity of breast cancer subtypes. Methods: We performed RNA sequencing on 246 individual cells from 4 primary breast tumors and 2 metastatic lymph nodes from 4 patients, using C1™Single-Cell Auto Prep System (Fluidigm, South San Francisco, CA). RNA sequencing reads were aligned to the human genome reference (hg19) using the 2-pass default mode of STAR_2.4.0d, and gene expression was quantified by RSEM v1.2.18 as the sum of isoform expression. Results: Pathologic characteristics of the 4 patients is summarized in Table 1. Table 1. Pathologic characteristics of patients BC01BC02BC03BC04pathologic stagepT1N0 (IA)pT2N0 (IIA)pT1N3 (IIIC)pT2N1 (IIB)Immunohistochemistry estrogen receptorpositivenegativenegativepositiveprogesterone receptornegativenegativenegativepositiveHER2 (FISH)2+/3 (negative)3+/3 (positive)1+/3 (negative)3+/3 (positive)No. of single cells tumor (lymph node)214947(50)28(51)HER2, human epidermal growth factor receptor 2; FISH, fluorescence in situ hybridization To distinguish tumor cells, we chose epithelial cell adhesion molecule (EpCAM) as an archetypal epithelial tumor marker and performed gene signature enrichment analysis for the EpCAM-positive cells. A total of 673 genes with enrichment score > |0.5| were selected and used as an "epithelial breast cancer" signature. Consensus clustering with the epithelial breast cancer signature separated 246 cells into 145 epithelial tumor and 101 non-tumor cell groups. Gene expression profiling in the tumor cells revealed many co-regulated genes in the estrogen receptor (ER)-positive tumor at a single cell resolution, which represent previously known ER-associated genes including MYC, BCL-2, and GATA3. Individual tumor cells from the two human epidermal growth factor receptor 2 (HER2)-amplified tumors demonstrated drastically different degree of HER2 signaling pathway activation, indicating the necessity of molecular subtyping for the identification of HER2-activated tumors. TNBC tumor cells showed an overall upregulation of activator protein 1 transcriptional pathway and strong activation of epithelial-mesenchymal transition signatures in a small sub-population. Immune cells comprised all the non-epithelial population, with mostly T lymphocytes in the primary tumor samples and B lymphocytes in the lymph nodes. The tumor-infiltrating T cells expressed an activated phenotype and many cytotoxic components. Altogether, the single cell RNA sequencing revealed the true identity of the tumor cells and the tumor-associated immune cells. Conclusion: Single cell RNA sequencing of breast cancer was used to reveal the true gene expression characteristics of the tumor cells and tumor-associated non-tumor compartments. The results showed key gene expression signatures of specific tumor subtypes and a wide range of transcriptome heterogeneity which is shaped by the tumor and microenvironments. Citation Format: Lee H-B, Eum HH, Chung W, Lee H-O, Lee K-M, Kim K-T, Moon H-G, Noh D-Y, Han W, Park W-Y. Single cell RNA sequencing reveals key expression signatures of primary breast cancer cells and immune infiltrates. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-04-01.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major respiratory disorder that is characterized by persistent airflow limitation and an abnormal inflammation response to noxious particles, such as cigarette smoke and environmental pollution. COPD is a leading cause of mortality, causing a significant economic and social burden in many countries worldwide. Umbelliferone, which is a coumarin derivative, has been shown to possess anti-inflammatory and antioxidant properties, which may be useful in alleviating COPD. METHODS: An integrative bioinformatics approach was adopted to analyze gene expression datasets from gene expression omnibus (GSE275503 for bulk RNA sequencing and GSE183974 for single-cell RNA sequencing). Differentially expressed genes (DEGs) were identified and overlapped with COPD-associated genes from GeneCards. Common genes were analyzed using the STRING database to construct a protein-protein interactions (PPI) network, and further processed in Cytoscape using the “cytoHubba” plug-in to identify the top 10 hub genes. Molecular docking of these genes, along with three key genes from single-cell analysis, was performed using AutoDock vina in SAMSON software. A molecular dynamic (MD) simulation study was carried out using Desmond and Schrodinger software. The MD simulations were performed to investigate the stability and dynamic behavior of the UMB with 6E3K and 1S9V over time 200 ns. RESULTS: From bulk and single-cell RNA sequencing analyses, 50 and 1053 DEGs were extracted, respectively, with the cut-off value of LogFC > 1 and [Formula: see text]-value < 0.05. A total number of 562 overlapping genes were identified between the DEGs and COPD-associated genes from GeneCards. The top 10 hub genes from the PPI network were ITGAM, CCL2, IFN-[Formula: see text], CCL5, FN1, IL-1B, BCL2, CDH1, IL-1A and CXCL8. Among these, molecular docking revealed the highest binding affinities for IFN-[Formula: see text] (PDB ID: 6E3K, −7.4 kcal/mol), ITGAM (PDB ID: 1NA5, −6.6 kcal/mol), IL-1B (PDB ID: 8C3U, −6.2 kcal/mol), FN1 (PDB ID: 3M7P, −6.1 kcal/mol) and BCL2 (PDB ID: 6YLD, −5.9 kcal/mol). Additionally, single-cell RNA analysis identified HLA-DQA2 (PDB ID: 1H15, −2.7 kcal/mol), HLA-DRB5 (PDB ID: 1S9V, −4.9 kcal/mol) and S100A9 (PDB ID: 6ZDY, −5.3 kcal/mol) as the top three genes. An MD simulation study evaluated protein-ligand stability, binding dynamics and conformational changes. Root mean square deviation (RMSD) and Root mean square fluctuation (RMSF) analyses identified key interacting residues involved in hydrogen bonding, hydrophobic interactions and water bridges. The 6E3K complex exhibited protein backbone RMSD of 5−7 Å, ligand RMSD of 2−5 Å and residue fluctuations <3 Å. The 1S9V complex showed protein RMSD of 2.85 Å, ligand RMSD of 1.95−2.30 Å and residue fluctuations around 1.25−3.25 Å. CONCLUSION: The top 10 Genes from the PPI network and the top 3 genes from the single-cell RNA analysis were associated with COPD-related pathways. Molecular docking with the UMB showed strong binding affinities, indicating its potential to inhibit these pathways and serve as a therapeutic option for COPD. The MD simulation confirmed stable binding, key interactions and structural flexibility.

BackgroundGiven the limitations of conventional therapies in prostate cancer (PCa) management, identifying novel biomarkers capable of predicting tumor prognosis and immunotherapy response is critically important. This article revealed the prognosis, immunological characteristics, and potential mechanisms of HKR1 in PCa via bulk and single-cell RNA sequencing (scRNA-Seq).MethodsBulk and scRNA-Seq analyses of HKR1 in PCa were collected from online databases. Differential expression and Cox regression analyses were carried out to evaluate its expression and prognosis values in PCa, respectively. Correlation analyses were performed to evaluate associations between HKR1 expression and enriched pathways, immune cell infiltration, and other relevant biological processes.ResultsHKR1 showed higher expression in PCa than in normal tissues, as verified by qPCR in both PCa cell lines and tissue samples (p < 0.05). ScRNA-seq analysis demonstrated HKR1 expression in malignant cells, epithelial cells, and immune cell populations. Moreover, PCa sufferers with higher HKR1 expressions were linked with poorer prognoses, and Cox regression analysis suggested it was an independent indicator in PCa (p < 0.05). Further, we shed light on the fact that the toll-like receptor, the TGF-beta, and the p53 pathways were significantly related to HKR1 expression in PCa. HKR1 was also found to be markedly linked to immunity in PCa (p < 0.05). Notably, we characterized two novel lncRNA-RBP-HKR1 regulatory axes that potentially modulate HKR1 transcriptional dynamics in prostate carcinogenesis.ConclusionsHKR1 played an undeniable role in the prognosis and immunological potential of PCa, providing evidence for the molecular mechanisms of HKR1 in PCa.

BackgroundIt was previously reported that the production of exerkines is positively associated with the beneficial effects of exercise in lung adenocarcinoma (LUAD) patients. This study proposes a novel scoring system based on muscle failure-related genes, to assist in clinical decision making.MethodsA comprehensive analysis of bulk and single cell RNA sequencing (scRNA-seq) of early, advanced and brain metastatic LUAD tissues and normal lung tissues was performed to identify muscle failure-related genes in LUAD and to determine the distribution of muscle failure-related genes in different cell populations. A novel scoring system, named MFI (Muscle failure index), was developed and validated. The differences in biological functions, immune infiltration, genomic alterations, and clinical significance of different subtypes were also investigated.ResultsFirst, we conducted single cell analysis on the dataset GSE131907 and identified eight cell subpopulations. We found that four muscle failure-related genes (BDNF, FNDC5, IL15, MSTN) were significantly increased in tumor cells. In addition, IL15 was widely distributed in the immune cell population. And we have validated it in our own clinical cohort. Then we created the MFI model based on 10 muscle failure-related genes using the LASSO algorithm, and MFI remained an independent prognostic factor of OS in both the training and validation cohorts. Moreover, we generated MFI in the single-cell dataset, in which cells with high MFI received and sent more signals compared to those with low MFI. Biological function analysis of both subtypes revealed stronger anti-tumor immune activity in the low MFI group, while tumor cells with high MFI had stronger metabolic and proliferative activity. Finally, we systematically assessed the immune cell activity and immunotherapy responses in LUAD patients, finding that the low MFI group was more sensitive to immunotherapy.ConclusionOverall, our study can improve the understanding of the role of muscle failure-related genes in tumorigenesis and we constructed a reliable MFI model for predicting prognosis and guiding future clinical decision making.

Background and Aims: Our previous studies of human coronary atherosclerosis and ApoE-/- mice indicated that at least 50% and ~70% of foam cells are of smooth muscle cell (SMC) origin, respectively. We also found that SMCs generate a distinct class of foam cells, due to several gene expression and regulatory differences compared to macrophage foam cells. In the current investigation we utilized transcriptomic approaches to identify and characterize the phenotype of SMC foam cells of human coronary plaques and in SMCs treated with aggregated LDL (ag-LDL). Methods: Sections of fresh human coronary artery were subjected to gentle digestion and the isolated cells used for single-cell RNA Sequencing (scRNA-Seq) (n=3) using 10X Genomics Chromium Single Cell 3’ Reagent Kits v3 Technology. Human vascular SMCs were treated with 100 μg/mL ag-LDL for 24 h (n=3) and stained with the fluorescent lipid probe BODIPY493/503. BODIPY high SMCs were collected and subjected to bulk RNA sequencing along with non-treated SMCs as control. Results: Single cell RNA sequencing of cells isolated from human coronary arteries followed by unsupervised Seurat-based clustering (R version 4.2.0) identified 15 distinct clusters including 8 SMC clusters with varying degrees of differentiation and macrophage foam and non-foam cells. The top 70 upregulated genes in ag-LDL treated SMCs were used to identify clusters corresponding to SMC foam cells in the scRNA-seq dataset. We found that these genes are mostly enriched in 2 clusters of less differentiated SMCs. SMC and macrophage foam cell clusters display distinct characteristics. SMC foam cell clusters exhibit upregulation of complement and coagulation cascades, as well as ECM-receptor interaction genes. In contrast, the macrophage foam cell cluster shows enrichment of proinflammatory genes, genes involved in the response to unfolded protein, and both pro- and anti-apoptotic genes. Conclusion: Our studies provide novel tools to investigate the nature of SMC foam cells in human atherosclerosis and may provide unique markers for SMC foam cells. Gaining a comprehensive insight of the significance and characteristics of SMC foam cells is crucial in elucidating their ultimate role and potential as a therapeutic target in atherosclerosis.

Bulk RNA-sequencing, single-cell RNA-sequencing analysis, and experimental validation reveal iron metabolism-related genes CISD2 and CYP17A1 are potential diagnostic markers for recurrent pregnancy loss

The advent of single-cell RNA sequencing (scRNA-Seq) has brought with it the ability to gain greater insights into the cellular composition of tissues and heterogeneity in gene expression within specific cell types. For tissue-engineered blood vessels, this is particularly impactful to better understand how neotissue forms and remodels into tissue resembling a native vessel. A notable challenge, however, is the ability to separate cells from synthetic biomaterials to generate high-quality single-cell suspensions to interrogate the cellular composition of our tissue-engineered vascular grafts (TEVGs) during active remodeling in situ. We present here a simple, commercially available approach to separate cells within our TEVG from the residual scaffold for downstream use in a scRNA-Seq workflow. Utilizing this method, we identified the cell populations comprising explanted TEVGs and compared these with results from immunohistochemical analysis. The process began with explanted TEVGs undergoing traditional mechanical and enzymatic dissociation to separate cells from scaffold and extracellular matrix proteins. Magnetically labeled antibodies targeting murine origin cells were incubated with enzymatic digests of TEVGs containing cells and scaffold debris in suspension allowing for separation by utilizing a magnetic separator column. Single-cell suspensions were processed through 10 × Genomics and data were analyzed utilizing R to generate cell clusters. Expression data provided new insights into a diverse composition of phenotypically unique subclusters within the fibroblast, macrophage, smooth muscle cell, and endothelial cell populations contributing to the early neotissue remodeling stages of TEVGs. These populations were correlated qualitatively and quantitatively with immunohistochemistry highlighting for the first time the potential of scRNA-Seq to provide exquisite detail into the host cellular response to an implanted TEVG. These results additionally demonstrate magnetic cell isolation is an effective method for generating high-quality cell suspensions for scRNA-Seq. While this method was utilized for our group's TEVGs, it has broader applications to other implantable materials that use biodegradable synthetic materials as part of scaffold composition. Impact statement Single-cell RNA sequencing is an evolving technology with the ability to provide detailed information on the cellular composition of remodeling biomaterials in vivo. This present work details an effective approach for separating nondegraded biomaterials from cells for downstream RNA-sequencing analysis. We applied this method to implanted tissue-engineered vascular grafts and for the first time describe the cellular composition of the remodeling graft at a single-cell gene expression level. While this method was effective in our scaffold, it has broad applicability to other implanted biomaterials that necessitate separation of cell from residual scaffold materials for single-cell RNA sequencing.

e16517 Background: While active surveillance for small renal cell carcinoma (RCC) gained acceptance due to harmless behavior, controversy still continues on aggressive potential of small RCC. Despite small tumor size, there still remains a risk for distant metastasis and recurrence. The biology of small RCC, especially the risk factors for metastasis and recurrence, has not been fully elucidated. It is therefore important to evaluate the aggressive potential of small RCCs to ensure that necessary treatments are given, but also to avoid overtreatment. Therefore, we used bulk RNA sequencing and single cell RNA sequencing (scRNA-seq) to reveal genetic biomarkers and distinct immune microenvironment in aggressive small RCC patients. Methods: Five cases of T1a (≤ 4cm) clear cell RCCs (ccRCCs) that were reported to have synchronous metastasis or recurrence were identified as an aggressive group and four cases with age, sex, and tumor size-matched ccRCCs patients with no metastasis or recurrence were identified as a non-aggressive group among prospective cohort (ClinicalTrials.gov Identifier: NCT03694912) RCC database. For the control, two healthy kidney donors were enrolled. We performed scRNA-seq using the 10X Genomics platform and bulk analysis for the total eleven cases of peripheral blood mononuclear cell (PBMC) samples. We demultiplexed pooled 11 samples using Souporcell and sequencing reads were normalized and analyzed using R/Seurat package. Cellular components of each sample were determined based on known marker genes. Bulk analysis involved differential gene expression analysis and pathway enrichment analysis. Results: The mean tumor size and age was 2.6 cm and 63 years old, respectively. Among 5 aggressive ccRCC patients, two patients had synchronous lung metastasis and two patients had synchronous bone metastasis. One patient had recurrence after partial nephrectomy. We analyzed 36,303 cells and identified 31 cell subpopulations. Our single-cell analysis unveiled distinct variations in T cell and natural killer (NK) cell proportions among aggressive, non-aggressive, and normal groups, highlighting significant differences in these ratios. Additionally, a notable pattern of gene expression levels was found between non-aggressive and aggressive groups, with a substantial increase in the expression levels of GSTP, ASRG, and SNORD142 genes in aggressive group. Conclusions: As T1a ccRCCs present low but non-negligible risk of metastasis and recurrence, patients with small RCCs should be counseled on such risk when offered active surveillance. Patients with higher NK cells and lower naïve CD8+ T cell are associated with higher risk of metastasis and recurrence in T1a RCC. This study suggests a promising potential for leveraging genetic markers and immune cell dynamics in the diagnosis of aggressive ccRCC using non-invasive screening methods.

Colorectal cancer poses a major global health issue, profoundly affecting both mortality and morbidity rates across the world. A key obstacle in understanding the pathogenesis of colorectal cancer lies in its high inter-patient and spatial heterogeneity, making standard treatments ineffective. Commonly, the study on colorectal cancer relies on bulk RNA sequencing, offering an average gene expression profile for a heterogenous cell population. However, this approach obscures the heterogeneous characteristics of the cancer cells and hinders a comprehensive understanding of the complex interplay among different cell populations. Recently, the advent of single-cell RNA sequencing has been revolutionary, enabling researchers to analyze individual immune cells and overcome the limitations of bulk RNA sequencing. Through single-cell RNA sequencing, researchers have gained insights into the dynamic nature of the immune response to cancer and potential targets for immunotherapy. In this review, we discuss the technical aspects of single-cell RNA sequencing, the application of single-cell RNA sequencing in cancer immunology, and the potential of single-cell RNA in clinical settings. We believe that harnessing singlecell RNA sequencing in cancer research holds immense potential to drive the development of personalized immunotherapies, aiming to improve patient outcomes in colorectal cancer.

The critical determinants of effective antitumor immune responses, whether pre-existing or induced by therapy, remain poorly understood due to the complexity and plasticity of the immune system. To better profile and track these responses, we have performed single cell RNA sequencing for paired gene expression and immune repertoire analysis on tumor fine needle aspirates and peripheral blood of lymphoma patients undergoing immunotherapy on one of two clinical trials (NCT02927964, NCT03410901). In these in situ vaccination studies, patients with low-grade lymphoma receive local low-dose radiation and a TLR9 agonist intratumorally to one site of disease, along with either ibrutinib or an OX40 agonist. Tumor fine needle aspirates (FNAs) and peripheral blood samples are obtained prior to treatment, and at one week and six weeks after treatment start. We performed single cell RNA-sequencing to an average targeted depth of 50,000 reads/cell for gene expression libraries and 5000 reads/cell for TCR sequencing. Identification of variable genes, principal component and/or canonical correlation analysis, graph-based clustering and differential expression analysis was performed using the Seurat algorithm. Single-cell TCR repertoires were analyzed using TCR-specific analysis software. Sequencing libraries have been prepared from 67 longitudinal samples from 9 patients thus far. We have successfully generated single cell gene expression and TCR libraries from 3,000-10,000 cells per sample from tumor fine needle aspirates and peripheral blood, with excellent sequencing quality metrics obtained. Analyzing 23 samples sequenced thus far, we consistently identify many B and T cell subsets of interest within tumors and reliably distinguish tumor B cells from normal B cells. In two responding patients, we confirm shrinkage of the tumor B cell compartment and find specific increases in CD8 and CD4 effector cells with decreases in T follicular helper cells and/or regulatory T cells at the treated site, results that are corroborated by multiparameter flow cytometry performed in parallel. Comparing pre-and post-treatment tumor FNAs from the same sites, we find evidence of increased cytotoxicity and interferon response following treatment. We also find distinct transcriptional shifts in tumor B cells both at the treated and at the distant untreated sites. Sample collection, sequencing, and analysis are ongoing. Deep profiling of serial biopsies during immunotherapy using single cell RNA sequencing promises to illuminate underlying cellular dynamics, and paired with clinical outcome data, determinants of response. Ultimately, this may provide a roadmap for successful translation of single-cell genomics into the clinic for treatment monitoring and response prediction. Citation Format: Tanaya Shree, Anuja Sathe, Hanlee Ji, Ronald Levy. Single cell RNA sequencing of serial tumor and blood biopsies from lymphoma patients undergoing in situ vaccination [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4045.

Unveiling hypoxia-related prognostic and immunotherapeutic biomarkers in lung adenocarcinoma through single-cell and bulk RNA sequencing: Including insights into PGF.

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.