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Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

SUBMITTER: Chao JY

PROVIDER: S-EPMC8280477 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer.

Chao Jeffrey Yung-Chuan JY Chang Hsin-Chuan HC Jiang Jeng-Kai JK Yang Chih-Yung CY Chen Fang-Hsin FH Lai Yo-Liang YL Lin Wen-Jen WJ Li Chia-Yang CY Wang Shu-Chi SC Yang Muh-Hwa MH Lin Yu-Feng YF Cheng Wei-Chung WC

Computational and structural biotechnology journal 20210701

Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation o ...[more]

PMID: 34306573

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Project description:BackgroundLung cancer has the highest cancer-related mortality worldwide. Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC). Chromatin licensing and DNA replication factor 1 (CDT1), a key regulator of cell cycle control and replication in eukaryotic cells, has been implicated in various cancer-related processes. Given its significant role in cancer, the focus on CDT1 in this study is justified as it holds promise as a potential biomarker or therapeutic target for cancer treatment. However, its prognostic value in lung adenocarcinoma (LUAD) remains unclear.MethodsBioinformatics analysis was conducted using data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized to predict biological processes and signaling pathways, respectively. The LinkedOmics database was employed to identify differentially expressed genes (DEGs) associated with CDT1. Nomograms and Kaplan-Meier plots were generated to assess the survival rates of patients with lung adenocarcinoma (LUAD). To determine the RNA and protein expression levels of CDT1 in LUAD and adjacent normal tissues, quantitative polymerase chain reaction (qPCR) and immunohistochemistry techniques were employed, respectively.ResultsCDT1 was upregulated in the vast majority of cancer tissues, based on pan-cancer analysis in TCGA and GEO datasets, as to lung cancer, the level of CDT1 expression was much higher in LUAD tissue than in healthy lung tissue. Our clinical data supported these findings. In our study, we used a specific cutoff value to dichotomize the patient samples into high and low CDT1 expression groups. The Kaplan-Meier survival curve revealed poor survival rates in CDT1 high expression group than the low expression group. To determine if CDT1 expression was an independent risk factor in LUAD patients, univariate and multivariate Cox regression analyses were performed. The result showed that CDT1 was a potential novel prognosis factor for LUAD patients, whose prognosis was poorer when CDT1 expression was higher. Based on functional enrichment analysis, highly expressed DEGs of CDT1-high patients were predicted to be involved in the cell cycle. According to our analysis of immune infiltration, CDT1 exhibited a strong correlation with specific immune cell subsets and was found to be a significant predictor of poor survival in patients with LUAD.ConclusionsOur research found that CDT1 was upregulated in LUAD and that high CDT1 expression predicted poor prognosis. We comprehensively and systematically analyzed the expression level in the datasets as well as in our own clinical samples, we also evaluated the prognostic and diagnostic value of CDT1, and finally, the potential mechanisms of CDT1 in the progression of LUAD. These results suggested that CDT1 may be a prognostic marker and therapeutic target for LUAD.

Project description:ObjectiveThe incidence of colorectal cancer is increasing every year, and autophagy may be related closely to the pathogenesis of colorectal cancer. Autophagy is a natural catabolic mechanism that allows the degradation of cellular components in eukaryotic cells. However, autophagy plays a dual role in tumorigenesis. It not only promotes normal cell survival and tumor growth but also induces cell death and suppresses tumors survival. In addition, the pathogenesis of various conditions, including inflammation, neurodegenerative diseases, or tumors, is associated with abnormal autophagy. The present work aimed to examine the significance of autophagy-related genes (ARGs) in prognosis prediction, to construct an autophagy prognostic model, and to identify independent prognostic factors for colorectal cancer (CRC).MethodsThis study discovered a total of 36 ARGs in CRC cases using The Cancer Genome Atlas (TCGA) and Human Autophagy-dedicated (HADd) databases along with functional enrichment analysis. Then, an autophagy prognostic model was constructed using univariate Cox regression analysis, and the key prognostic genes were screened. Finally, independent prognostic markers were determined through independent prognostic analysis and clinical correlation analysis of key genes.ResultsOf the 36 differentially expressed ARGs, 13 were related to prognosis, as determined by univariate Cox regression analysis. A total of 6 key genes were obtained by a multivariate Cox regression analysis. Independent prognostic values were shown by 3 genes, namely, microtubule-associated protein 1 light chain 3 (MAP1LC3C), small GTPase superfamily and Rab family (RAB7A), and WD-repeat domain phosphoinositide-interacting protein 2 (WIPI2) by independent prognostic analysis and clinical correlation.ConclusionsIn this study, molecular bioinformatics technology was employed to determine and construct a prognostic model of autophagy for colon cancer patients, which revealed 3 autophagy-related features, namely, MAP1LC3C, WIPI2, and RAB7A.

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.

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Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer.

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Using bioinformatics approaches to investigate driver genes and identify BCL7A as a prognostic gene in colorectal cancer.

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