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Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.

ABSTRACT: Background:Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer worldwide, and it has a poor prognosis. The current study is aimed at developing the prediction of LSCC prognosis by integrating multiomics data including transcriptome, copy number variation data, and mutation data analysis, so as to predict patients' survival and discover new therapeutic targets. Methods:RNASeq, SNP, CNV data, and LSCC patients' clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were randomly divided into two groups, namely, the training set and the validation set. In the training set, the genes related to prognosis and those with different copy numbers or with different SNPs were integrated to extract features using random forests, and finally, robust biomarkers were screened. In addition, a gene-related prognostic model was established and further verified in the test set and GEO validation set. Results:We obtained a total of 804 prognostic-related genes and 535 copy amplification genes, 621 copy deletions genes, and 388 significantly mutated genes in genomic variants; noticeably, these genomic variant genes were found closely related to tumor development. A total of 51 candidate genes were obtained by integrating genomic variants and prognostic genes, and 5 characteristic genes (HIST1H2BH, SERPIND1, COL22A1, LCE3C, and ADAMTS17) were screened through random forest feature selection; we found that many of those genes had been reported to be related to LSCC progression. Cox regression analysis was performed to establish 5-gene signature that could serve as an independent prognostic factor for LSCC patients and can stratify risk samples in training set, test set, and external validation set (p < 0.01), and the 5-year survival areas under the curve (AUC) of both training set and validation set were >?0.67. Conclusion:In the current study, 5 gene signatures were constructed as novel prognostic markers to predict the survival of LSCC patients. The present findings provide new diagnostic and prognostic biomarkers and therapeutic targets for LSCC treatment.

SUBMITTER: Ma H

PROVIDER: S-EPMC7298313 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.

Ma Hongxia H Tong Lihong L Zhang Qian Q Chang Wenjun W Li Fengsen F

BioMed research international 20200608

<h4>Background</h4>Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer worldwide, and it has a poor prognosis. The current study is aimed at developing the prediction of LSCC prognosis by integrating multiomics data including transcriptome, copy number variation data, and mutation data analysis, so as to predict patients' survival and discover new therapeutic targets.<h4>Methods</h4>RNASeq, SNP, CNV data, and LSCC patients' clinical follow-up information were downloaded from The ...[more]

PMID: 32596344

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Project description:Purpose: Alternative splicing (AS) is a post-transcriptional process that plays a significant role in enhancing the diversity of transcription and protein. Accumulating evidences have demonstrated that dysregulation of AS is associated with oncogenic processes. However, AS signature specifically in lung squamous cell carcinoma (LUSC) remains unknown. This study aimed to evaluate the prognostic values of AS events in LUSC patients. Methods: The RNA-seq data, AS events data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was performed to identify survival-related AS events and survival-related parent genes were subjected to Gene Ontology enrichment analysis and gene network analysis. The least absolute shrinkage and selection operator (LASSO) method and multivariate Cox regression analysis were used to construct prognostic prediction models, and their predictive values were assessed by Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. Then a nomogram was established to predict the survival of LUSC patients. And the interaction network of splicing factors (SFs) and survival-related AS events was constructed by Spearman correlation analysis and visualized by Cytoscape. Results: Totally, 467 LUSC patients were included in this study and 1,991 survival-related AS events within 1,433 genes were identified. SMAD4, FOS, POLR2L, and RNPS1 were the hub genes in the gene interaction network. Eight prognostic prediction models (seven types of AS and all AS) were constructed and all exhibited high efficiency in distinguishing good or poor survival of LUSC patients. The final integrated prediction model including all types of AS events exhibited the best prognostic power with the maximum AUC values of 0.778, 0.816, 0.814 in 1, 3, 5 years ROC curves, respectively. Meanwhile, the nomogram performed well in predicting the 1-, 3-, and 5-year survival of LUSC patients. In addition, the SF-AS regulatory network uncovered a significant correlation between SFs and survival-related AS events. Conclusion: This is the first comprehensive study to analyze the role of AS events in LUSC specifically, which improves our understanding of the prognostic value of survival-related AS events for LUSC. And these survival-related AS events might serve as novel prognostic biomarkers and drug therapeutic targets for LUSC.

Project description:Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most common type of oral cancer, which remains a major cause of morbidity and mortality in patients with head and neck cancers. However, the critical immune-related signatures and their prognostic values have rarely been investigated.Materials and methodsGene differential analysis was used to measure the differences of gene expression between the groups. Correlation analysis was used to assess the association between the gene expression levels and immune-related risk score/DNA methylation levels. The gene set enrichment analysis (GSEA) was used to identify the pathways or cell types enriched by those identified differentially expressed genes (DEGs).ResultsIn this study, we identified four immune-related gene signatures, including CTSG, TNFRSF4, LCORL, and PLAU, that were significantly associated with the overall survival in OSCC patients from the Cancer Genome Atlas (TCGA) OSCC cohort. Moreover, these four immune-related signatures were differentially expressed between the OSCC and nontumor tissues. The two groups (high and low risk) stratified by the immune-related risk scores had significantly different OS and mortality rates. The gene expression patterns and prognostic values of these immune-related signatures were also verified in two independent validation cohorts. Furthermore, the downregulated genes in the high-risk group (which were also upregulated in the low-risk group) were significantly enriched in the cell type-specific signatures of type 2 T helper cell (Th2), plasmacytoid dendritic cell (pDC), and memory B cell. In contrast, the upregulated genes in the high-score group were enriched in growth factor receptor-related signaling pathways, such as the VEGFA-VEGFR2 signaling pathway, PI3K-Akt signaling pathway, focal adhesion-PI3K-Akt-mTOR signaling pathway, and PDGF pathway, suggesting that those pathways were inversely correlated with immune cell infiltration.ConclusionIn summary, the immune-related signatures had the potential for predicting the risk of OSCC patients. Moreover, the present study also improved our understanding of the association between the growth factor receptor pathways and immune cell infiltration in OSCC.

Project description:BackgroundTumor mutational burden (TMB) has been identified as one of the predictors for the response to anti-programmed cell death-1 (anti-PD-1) antibody therapy and reported to correlate with smoking history in lung adenocarcinoma. However, in squamous cell carcinoma of the lung, the association between TMB and clinicopathological background factors, such as smoking history, has not been reported, including in our previous study. The mutational signature is a tool to identify the mutagens that are contributing to the mutational spectrum of a tumor by investigating the pattern of DNA changes. Here, we analyzed the mutational signature in lung squamous cell carcinoma to identify mutagens affecting the TMB.MethodsSeven representative mutational signatures including signature 7 (SI7) [ultraviolet (UV)-related], SI4 (smoking), SI6/15 [mismatch repair (MMR)], SI2/13 [apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)], and SI5 (clock-like) were analyzed in Japanese patients with lung squamous cell carcinoma (n=67) using data generated by next-generation sequencing consisting of a 415-gene panel. The relationships between signatures and clinico-pathological data including TMB and programmed death-ligand 1 (PD-L1) expression were analyzed.ResultsAlthough the reconstructed mutational counts were small with targeted sequencing (median: 30.1, range: 13.3-98.7), the distributions of signatures were comparable among samples, with 56 cases containing more than four signatures. The smoking-related SI4 was found in 45 cases and was significantly related with pack-year index (PYI) (P=0.026). The reconstructed mutation counts were highly correlated with SI4 (r=0.51, P<0.0001), whereas the correlation was weak with SI6/15 (MMR-related) and SI2/13 (APOBEC-related). There was no mutational signature related with PD-L1 expression. Some patients exhibited unique signatures; the patient with the highest mutational counts had a MMR signature, and another patient with a prominent UV signature had occupational exposure to UV, as he was employed as a neon sign engineer.ConclusionsMutational signatures can predict the cause of lung squamous cell carcinoma. Tobacco smoking is the mutagen most related with TMB.

Project description:BackgroundN6-methyladenine (m6A) is the most common modification of mRNA and IncRNA in higher organisms. m6A has been confirmed to be related to the formation and progression of tumors and m6A-related genes can be used as prognostic biomarkers in a variety of tumors. However, there have been no similar studies on lung squamous cell carcinoma. The main purpose of this study was aimed to explore the differential expression of m6A-related genes in lung squamous cell carcinoma tissues and its relationship with patient clinical prognosis.MethodsWe integrated three m6A writers that catalyze the methylation of adenine on mRNA molecules. The training set including 501 patients with LUSC was collected from The Cancer Genome Atlas (TCGA) database and the test set including 181 patients with LUSC was collected from the Gene Expression Omnibus (GEO) database. Based on the expression level of the m6A methylase gene, we established a tumor subgroup and risk-prognosis model to quantify the risk index and long-term patient prognosis, which were confirmed by principal component analysis (PCA) and receiver operating characteristic (ROC) curve analysis. After lung squamous cell carcinoma tissue specimens were obtained during surgery, immunohistochemistry (IHC) was used to verify the results in vitro.ResultsThe results of the study showed that the expression of the three m6A methylases in tumor tissues and normal tissues was significantly different (P < 0.05). The survival-prognostic model based on METTL3 gene expression showed better predictive performance (AUC: 0.706). Patients in the high-risk and low-risk groups exhibited significant differences in terms of survival time and 5-year and 10-year survival rates. Immunohistochemistry revealed that patients with high METTL3 expression exhibited a longer survival time than those with low METTL3 expression.ConclusionsOur study showed that the molecular phenotype based on the expression of METTL3 may be an independent risk factor affecting the prognosis of lung squamous cell carcinoma. These findings not only prove the important role of m6A methylase in lung squamous cell carcinoma, but are also expected to provide more accurate prognostic assessment and individualized treatment for patients with lung squamous cell carcinoma.

Dataset Information

Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.

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Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis.

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