Bioinformatics Analysis of Key Genes and Pathways in Colorectal Cancer

2019 ◽  
Vol 26 (4) ◽  
pp. 364-375 ◽  
Author(s):  
Yuewen Qi ◽  
Haowen Qi ◽  
Zeyuan Liu ◽  
Peiyuan He ◽  
Bingqing Li
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Key Genes

scholarly journals Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

2019 ◽  
Author(s):  
Gong‑Peng Dai ◽  
Li‑Ping Wang ◽  
Yu‑Qing Wen ◽  
Xue‑Qun Ren ◽  
Shu‑Guang Zuo
Keyword(s):  
Colorectal Cancer ◽  
Bioinformatics Analysis ◽  
Cancer Prognosis ◽  
Key Genes

scholarly journals LINC00963 affects the development of colorectal cancer via MiR-532-3p/HMGA2 axis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinjun Ye ◽  
Jidong Liu ◽  
Tao Tang ◽  
Le Xin ◽  
Xing Bao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Tumor Growth ◽  
Cell Cycle Progression ◽  
Bioinformatics Analysis ◽  
Scratch Test ◽  
Luciferase Assay ◽  
Migration And Invasion ◽  
Cycle Progression ◽  
Ki67 Expression ◽  
And Function

Abstract Background LINC00963 is high-expressed in various carcinomas, but its expression and function in colorectal cancer (CRC) have not been explored. This study explored the role and mechanism of LINC00963 in CRC. Methods The expression of LINC00963 in CRC and its relationship with prognosis were examined by starBase and survival analysis. The effects of LINC00963, miR-532-3p and HMGA2 on the biological characteristics and EMT-related genes of CRC cells were studied by RT-qPCR, CCK-8, clone formation experiments, flow cytometry, scratch test, Transwell, and Western blot. Xenograft assay and immunohistochemistry were performed to verify the effect of LINC00963 on tumor growth. The correlation among LINC00963, miR-532-3p, and HMGA2 was analyzed by bioinformatics analysis, luciferase assay, and Pearson test. Results LINC00963 was high-expressed in CRC, and this was associated with poor prognosis of CRC. Silencing LINC00963 inhibited the activity, proliferation, migration, and invasion of CRC cells, MMP-3 and MMP-9 expressions, moreover, it also blocked cell cycle progression, and inhibited tumor growth and Ki67 expression. However, overexpression of LINC00963 showed the opposite effects to silencing LINC00963. LINC00963 targeted miR-532-3p to regulate HMGA2 expression. Down-regulation of miR-532-3p promoted cell proliferation, migration and invasion, and expressions of MMP-3 and MMP-9, and knockdown of HMGA2 reversed the effect of miR-532-3p inhibitor. Up-regulation of miR-532-3p inhibited the biological functions of CRC cells, and overexpression of HMGA2 reversed the miR-532-3p mimic effect. Conclusion LINC00963 affects the development of CRC through the miR-532-3p/HMGA2 axis.

scholarly journals Prediction of Prognostic Biomarkers and Construction of an Autophagy Prognostic Model for Colorectal Cancer Using Bioinformatics

2020 ◽  
Vol 19 ◽  
pp. 153303382098417
Author(s):  
Ting-ting Liu ◽  
Shu-min Liu
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Small Gtpase ◽  
Functional Enrichment ◽  
Clinical Correlation ◽  
Cox Regression Analysis ◽  
Prognostic Analysis ◽  
Key Genes

Objective: The 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). Methods: This 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. Results: Of 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. Conclusions: In 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.

scholarly journals Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Hao Long ◽  
Chaofeng Liang ◽  
Xi’an Zhang ◽  
Luxiong Fang ◽  
Gang Wang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Prediction Model ◽  
Basic Science ◽  
Prediction Accuracy ◽  
Genetic Basis ◽  
Bioinformatics Analysis ◽  
Clinical Treatment ◽  
Structural Level ◽  
Biotechnological Application ◽  
Key Genes

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.

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