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Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

ABSTRACT: Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non-small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ('high infiltration' cluster). The immune cells were responsible for a significantly favourable prognosis for the 'high infiltration' community. Cluster 1 had the lowest cytotoxic activity, tumour-infiltrating lymphocytes and interferon-gamma (IFN-?), as well as immune checkpoint molecules expressions. In addition, MHC-I and immune co-stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune-related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.

SUBMITTER: Mi K

PROVIDER: S-EPMC7754023 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

Mi Kai K Chen Fuhui F Qian Zhipeng Z Chen Jing J Lv Dongxu D Zhang Chunlong C Xu Yanjun Y Wang Hongguang H Zhang Yuepeng Y Jiang Yanan Y Shang Desi D

Journal of cellular and molecular medicine 20201112 24

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non-small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ( ...[more]

PMID: 33184998

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Project description:Background The tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression. Methods The TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts. Results The TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples. Conclusion Our study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Project description:BackgroundThe high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification.MethodsSingle-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation.FindingsWe selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum.InterpretationThis single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection.FundingSupport for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

Project description:Background:Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods:The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results:A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Project description:Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.

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Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

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Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

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