Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Patil, Abhijeet R.; Leung, Ming-Ying; Roy, Sourav

doi:10.3390/ijerph18115564

Cited by 5 publications

(2 citation statements)

References 83 publications

(94 reference statements)

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“…There have been some CRC hub gene studies based on bioinformatics analyses, but very few studies targeting mitophagy ( 33 , 34 ). The role of mitophagy in inflammation, immunity, and tumor progression has gained widespread attention from researchers.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer

Ke,

Wang,

Deng

et al. 2024

Transl Cancer Res

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Background Colorectal cancer (CRC) is one of the most commonly diagnosed cancers in the world. Mitophagy is associated with tumorigenesis and development of malignancy. However, the specific role of mitophagy has yet not been systematically explored in CRC. Methods The RNA-sequencing dataset of CRC from The Cancer Genome Atlas (TCGA) and microarray data of gene expression profiles of CRC from Gene Expression Omnibus (GEO) were downloaded. Mitophagy-related gene sets were obtained from the Pathway Unification database. The package “limma” was used for differential gene expression analysis. Kaplan-Meier (KM) survival analyses were utilized to evaluate the prognostic value of the mitophagy regulators. Single-sample gene set enrichment analysis (ssGSEA) was used to estimate the infiltrating immune cells and the activity of immune response. The ConsensusClusterPlus algorithm was used to determine mitophagy-related subtypes. Principal component analysis (PCA) was used to create composite measurement of mitophagy scores. The R packages “survminer” and “ReGlot” were used to plot the nomogram and calibration curves. Results Integrated analysis of the GEO and TCGA databases revealed some common differentially expressed genes (DEGs) in CRC. MFN2 , UBB , PINK1 , and PRKN were significantly downregulated in CRC samples as compared to normal samples, and other genes were significantly upregulated in CRC samples. KM survival analyses showed that high expression of ATG12 and MAP1LC3B predicted a poor prognosis, whereas high expression of TOMM22 and TOMM40 predicted a better prognosis. Mitophagy showed significant correlation with immune-related pathways in CRC samples. We identified 2 distinct CRC subtypes with different mitophagy accumulation, of which subtype B had better prognosis and immune activity. The mitophagy score may be employed as a new and efficient clinical predictor in conjunction with other clinical indicators to predict the prognosis of CRC patients. Conclusions We systematically investigated the CRC heterogeneity with reference to mitophagy based on bioinformatics analyses, and the findings of this study might provide some guidance for future research into potential biomarkers for diagnosis and prognosis prediction of CRC patients.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer

Ke,

Wang,

Deng

et al. 2024

Transl Cancer Res

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“…Four CCs term bicellular tight junction [ 100 ] apical junction complex [ 101 ] and cell–cell junction [ 102 ] were associated with CLCA1. Among the enriched KEGG pathways, two KEGG terms Nitrogen metabolism [ 22 ] and Proximal tubule bicarbonate reclamation [ 103 ] were associated with CA4. Three pathways Cell adhesion molecules [ 104 ], Pathogenic Escherichia coli infection [ 105 ] and Leukocyte transendothelial migration [ 106 ] were associated with CLDN1.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

et al. 2023

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Background Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches. Methods To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein–protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins. Results We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC. Conclusion The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

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Identification of hub genes and key pathways targeted by miRNAs in pancreatic ductal adenocarcinoma: MAPK3/8/9 and TGFBR1/2

Gungormez

2024

Human Gene

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Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Cited by 5 publications

References 83 publications

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer

Identification and validation of mitophagy-related signatures as a novel prognostic model for colorectal cancer

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

Identification of hub genes and key pathways targeted by miRNAs in pancreatic ductal adenocarcinoma: MAPK3/8/9 and TGFBR1/2

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