Bioinformatics Analysis of RNA-seq Data Reveals Genes Related to Cancer Stem Cells in Colorectal Cancerogenesis

Urh, Kristian; Zidar, Nina; Boštjančič, Emanuela

doi:10.3390/ijms232113252

Cited by 5 publications

(5 citation statements)

References 103 publications

(135 reference statements)

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“…The top 10 hub genes in CRC in the PPI network interaction map were CDK1, CCNA2, CCNB1, CDC20, BUB1B, BUB1, DLGAP5, CCNB2, KIF11, and KIF20A. CDK1 is signi cantly differentially expressed in CRC with lymph node metastasis compared with that in normal mucosa [25] . CCNB1 is associated with survival in stage II CRC [26] .…”

Section: Discussionmentioning

confidence: 98%

Bioinformatics analysis reveals link between alternative complement cascade pathway and colorectal cancer liver metastasis

Shi,

Li,

Wei

et al. 2023

Preprint

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Background Colorectal cancer (CRC) has a high incidence mortality rate and is characterized by liver metastasis, which is the main cause of CRC patient death. In this study, a transcriptome sequencing dataset (GSE81558) from the integrated Gene Expression Omnibus database was evaluated to gain new insights into the pathogenesis of CRC and potential therapeutic targets. Methods All raw data were processed using R and screened for differentially expressed genes (DEGs) using LIMMA software. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted and visualized using R and Cytoscape software. Protein‒protein interactions (PPIs) associated with the DEGs were assessed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. A mouse model of CRC liver metastasis of CRC was used to verify key associated signaling pathways. Results The GO biological processes (BPs) and KEGG pathway analyses revealed that DEGs between the normal colon and CRC samples were mainly involved in the cell cycle and the P53 signaling pathway, which regulate the cell cycle and alter tumor signaling pathways. The 10 hub genes identified by PPI were cell cycle-related. In CRC versus and CRC liver metastasis samples, the GO BPs were mainly associated with platelets and coagulation, and the KEGG pathways were mainly enriched in the complement and coagulation cascades and drug metabolism. The PPI hub genes were blood protein-related, such as ALB, AHSG, and APOH, or plasma protease inhibitors, such as SERPINC1. To confirm bioinformatics analysis results, we used wild-type (WT), C4 (an important molecule in the classical and lectin complement cascade pathways), and complement factor B (fB, an important molecule in the alternative complement cascade pathway) knockout (KO) mice to construct a CRC liver metastasis model. Compared with WT mice, fB-KO mice demonstrated significantly reduced liver metastasis and inflammation, while there was no difference in C4-KO mice. Conclusion Bioinformatics analyses revealed that the complement cascade is related to CRC liver metastasis and that the cell cycle is related to CRC. The role of the alternative complement pathway in CRC liver metastasis was confirmed in mice, indicating that this pathway is a potential therapeutic target in CRC liver metastasis and providing a theoretical basis for further research.

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Section: Discussionmentioning

confidence: 98%

Bioinformatics analysis reveals link between alternative complement cascade pathway and colorectal cancer liver metastasis

Shi,

Li,

Wei

et al. 2023

Preprint

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“…Conventionally, clinicians often depend on clinical characteristics, such as age, sex and TNM stage, to predict the survival of cancer patients and guide the treatment 14 . With the advent of high‐throughput sequencing and bioinformatics, people can draw on the transcriptome data and find the critical genes tightly associated with the patient survival, and constructed prognostic models to evaluate cancer prognosis more accurately 15 . In this study, we established a riskscore model in light of Treg‐associated genes.…”

Section: Discussionmentioning

confidence: 99%

“…14 With the advent of high-throughput sequencing and bioinformatics, people can draw on the transcriptome data and find the critical genes tightly associated with the patient survival, and constructed prognostic models to evaluate cancer prognosis more accurately. 15 In this study, we established a riskscore model in light of Treg-associated genes. The riskscore of each patient can be calculated from the expression value of seven key genes.…”

Section: Association Of Key Gene Expression With Treg Infiltrationmentioning

confidence: 99%

A Treg‐related riskscore model may improve the prognosis evaluation of colorectal cancer

Li,

Chu,

Yao

et al. 2024

The Journal of Gene Medicine

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BackgroundColorectal cancer (CRC) poses a significant health challenge. This study aims to investigate the prognostic value of a regulatory T cell (Treg)‐related gene signature in CRC.MethodsWe extracted the gene expression and clinical data on CRC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The gene module related to Treg was identified by weighted gene co‐expression network analysis (WGCNA). The genes in the significant module were filtered by univariate Cox, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. A riskscore model was established in terms of the key Treg‐related genes. The reliability of this riskscore model was validated using the external GEO dataset. The association of riskscore with clinical features, mutation patterns and signaling pathways was explored.ResultsGenes in the blue module showed the strongest association with Tregs. After a series of filtering cycles, seven Treg‐related key genes, GDE1, GSR, HSPB1, AOC2, TBX19, TAMM41 and TIGD6, were selected to construct a riskscore model. This model performed well in evaluating the patients’ survival in TCGA cohort, and was further affirmed by the GSE17536 validation cohort. For precise evaluation of the patients’ survival, we established a nomogram in light of riskscore and clinical factors. Patients in different risk groups had distinct clinical features, mutation patterns and signaling pathway activities. The expression of five key genes was significantly associated with Treg infiltration in the CRC samples.ConclusionWe established a useful riskscore model in light of seven Treg‐related genes. This model may contribute to the prognosis evaluation, direct tailored treatment, and hopefully improve clinical outcomes of the CRC patients.

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“…In our study, the population size was set to 10 individuals, the maximum number of evolutions was 1,000, the roulette wheel method was used as the selection operator, and the crossover rate and variation rate were both 0.1. The fitness function was particularly important here, and the fitness function cannot be obtained directly in this task formula, which consists of two parts; the first part is calculated from the value of the predicted drug treatment effect by the deep learning model, and the other part is calculated from the number of drug species, and its specific formula is shown in Equation (11), where N denotes the number of best drug species in the combination.…”

Section: Genetic Algorithm Modulementioning

confidence: 99%

“…Jiang et al ( 10 ) found that CDNK3 was the key gene in the progression of cirrhosis to hepatocellular carcinoma. In addition, Urh et al ( 11 ) bioinformatically analyzed normal mucosal, colorectal adenoma, and colorectal cancer differential genes from GEO as well as The Cancer Genome Atlas (TCGA) databases and found that tumor stem cell-related genes (ANLN, CDK1, ECT2, and TNC) were associated with colorectal carcinogenesis, while ANLN and PDGFD genes were associated with the progression of colorectal cancer, but the mechanism of action of these genes with the development of colorectal cancer still needs further study and validation.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics-based and multiscale convolutional neural network screening of herbal medicines for improving the prognosis of liver cancer: a novel approach

Chen,

Peng,

Wang

et al. 2023

Front. Med.

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BackgroundLiver cancer is one of the major diseases threatening human life and health, and this study aims to explore new methods for treating liver cancer.MethodsA deep learning model for the efficacy of clinical herbal medicines for liver cancer was constructed based on NDCNN, combined with the natural evolutionary rules of a genetic algorithm to obtain the herbal compound for liver cancer treatment. We obtained differential genes between liver cancer tissues and normal tissues from the analysis of TCGA database, screened the active ingredients and corresponding targets of the herbal compound using the TCMSP database, mapped the intersection to obtain the potential targets of the herbal compound for liver cancer treatment in the Venny platform, constructed a PPI network, and conducted GO analysis and KEGG analysis on the targets of the herbal compound for liver cancer treatment. Finally, the key active ingredients and important targets were molecularly docked.ResultsThe accuracy of the NDCNN training set was 0.92, and the accuracy of the test set was 0.84. After combining with the genetic algorithm for 1,000 iterations, a set of Chinese herbal compound prescriptions was finally the output. A total of 86 targets of the herbal compound for liver cancer were obtained, mainly five core targets of IL-6, ESR1, JUN, IL1β, and MMP9. Among them, quercetin, kaempferol, and stigmasterol may be the key active ingredients in hepatocellular carcinoma, and the herbal compound may be participating in an inflammatory response and the immune regulation process by mediating the IL-17 signaling pathway, the TNF signaling pathway, and so on. The anticancer effects of the herbal compound may be mediated by the IL-17 signaling pathway, the TNF signaling pathway, and other signaling pathways involved in inflammatory response and immune regulation. Molecular docking showed that the three core target proteins produced stable binding to the two main active ingredients.ConclusionThe screening of effective herbal compounds for the clinical treatment of liver cancer based on NDCNN and genetic algorithms is a feasible approach and will provide ideas for the development of herbal medicines for the treatment of liver cancer and other cancers.

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Bioinformatics Analysis of RNA-seq Data Reveals Genes Related to Cancer Stem Cells in Colorectal Cancerogenesis

Cited by 5 publications

References 103 publications

Bioinformatics analysis reveals link between alternative complement cascade pathway and colorectal cancer liver metastasis

Bioinformatics analysis reveals link between alternative complement cascade pathway and colorectal cancer liver metastasis

A Treg‐related riskscore model may improve the prognosis evaluation of colorectal cancer

Bioinformatics-based and multiscale convolutional neural network screening of herbal medicines for improving the prognosis of liver cancer: a novel approach

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