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Identification of hub genes predicting sensitivity to neoadjuvant chemoradiation in locally advanced rectal cancer

Qunye Zhao

Qunye Zhao

Department of Colorectal Surgery, Harbin Medical University Cancer HospitalHarbin, China
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Zhao, Qunye
, 
Chuang Zhang

Chuang Zhang

Department of General surgery, The First People’s Hospital of KunshanKunshan, China
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Zhang, Chuang
, 
Xiaotain Zhang

Xiaotain Zhang

Center for Disease Control and PreventionHarbin, China
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Zhang, Xiaotain
, 
Yanlong Liu

Yanlong Liu

Department of Colorectal Surgery, Harbin Medical University Cancer HospitalHarbin, China
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Liu, Yanlong
 e 
Binbin Cui

Binbin Cui

Department of Colorectal Surgery, Harbin Medical University Cancer HospitalHarbin, China
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Cui, Binbin
  
06 ago 2025
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Categoria dell'articolo: Research Article
Pubblicato online: 06 ago 2025
Pagine: 435 - 449
Ricevuto: 12 giu 2024
Accettato: 28 ott 2024
DOI: https://doi.org/10.2478/raon-2025-0005
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© 2025 Qunye Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

FIGURE 1.

Homogenization of the three datasets. The preprocessCore package was used for data normalization of (A-B) GSE119409, (C-D) GSE123390, and (E-F) GSE150082 datasets.
Homogenization of the three datasets. The preprocessCore package was used for data normalization of (A-B) GSE119409, (C-D) GSE123390, and (E-F) GSE150082 datasets.

FIGURE 2.

Differentially expressed genes (DEGs) volcano plot as well as heatmap. (A, C, E) DEGs volcano plot in GSE119409, GSE123390, and GSE150082 datasets, respectively. Pink: up-regulated; Red: down-regulated. (B, D, F) DEGs heatmap between resistant (blue) and sensitive (pink) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation (NACR). Blue: low expression level; Red:high expression level.
Differentially expressed genes (DEGs) volcano plot as well as heatmap. (A, C, E) DEGs volcano plot in GSE119409, GSE123390, and GSE150082 datasets, respectively. Pink: up-regulated; Red: down-regulated. (B, D, F) DEGs heatmap between resistant (blue) and sensitive (pink) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation (NACR). Blue: low expression level; Red:high expression level.

FIGURE 3.

Exploration of differentially expressed genes between neoadjuvant chemoradiation (NACR)-resistant and NACR-sensitive rectal adenocarcinoma (READ) samples based on GSE119409, GSE123390, GSE150082 datasets. (A) The up-regulated gene intersections among three datasets. (B) The down-regulation gene intersection among three datasets.
Exploration of differentially expressed genes between neoadjuvant chemoradiation (NACR)-resistant and NACR-sensitive rectal adenocarcinoma (READ) samples based on GSE119409, GSE123390, GSE150082 datasets. (A) The up-regulated gene intersections among three datasets. (B) The down-regulation gene intersection among three datasets.

FIGURE 4.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment analysis of up- and downregulated genes. (A-C) GO function enrichment analysis including biological processes (BP), cellular component (CC), and molecular function (MF). (D) KEGG term analysis of differentially expressed genes (DEGs).
Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment analysis of up- and downregulated genes. (A-C) GO function enrichment analysis including biological processes (BP), cellular component (CC), and molecular function (MF). (D) KEGG term analysis of differentially expressed genes (DEGs).

FIGURE 5.

Protein interaction network construction (PPI) network. (A) PPI for differential genes using string database, visualized using Cytoscape software. (B) The Betweenness algorithm of cytoHubba plug-in of cytoscape software was used to identify the top 20 hub genes in the PPI network.
Protein interaction network construction (PPI) network. (A) PPI for differential genes using string database, visualized using Cytoscape software. (B) The Betweenness algorithm of cytoHubba plug-in of cytoscape software was used to identify the top 20 hub genes in the PPI network.

FIGURE 6.

Hub gene validation. (A) Heatmap of logFC values of top 20 genes in the GSE119409, GSE123390, GSE150082. (B-G) 6 hub genes validation at gene expression level in rectal adenocarcinoma (READ) tissues and normal tissues using The Cancer Genome Atlas database (TCGA) and Genotype-Tissue Expression (GTEx) databases.
Hub gene validation. (A) Heatmap of logFC values of top 20 genes in the GSE119409, GSE123390, GSE150082. (B-G) 6 hub genes validation at gene expression level in rectal adenocarcinoma (READ) tissues and normal tissues using The Cancer Genome Atlas database (TCGA) and Genotype-Tissue Expression (GTEx) databases.

FIGURE 7.

Hub gene validation for diagnostic value. (A-C) Correlation analysis of 6 hub genes in GSE119409, GSE123390 as well as GSE150082. (D-F) ROC curves of the 6 hub genes for the prediction of neoadjuvant chemoradiation (NACR) resistance in patients in GSE119409, GSE123390 and GSE150082.
Hub gene validation for diagnostic value. (A-C) Correlation analysis of 6 hub genes in GSE119409, GSE123390 as well as GSE150082. (D-F) ROC curves of the 6 hub genes for the prediction of neoadjuvant chemoradiation (NACR) resistance in patients in GSE119409, GSE123390 and GSE150082.

FIGURE 8.

Assessment of the degree of immune cell infiltration in GSE119409, GSE123390 and GSE150082 datasets. (A, C, E) showing the proportion of immune cell infiltration per dataset.
Assessment of the degree of immune cell infiltration in GSE119409, GSE123390 and GSE150082 datasets. (A, C, E) showing the proportion of immune cell infiltration per dataset.

FIGURE 8.

(B, D, F) showing the difference in immune cell infiltration between the resistant and sensitive groups.
(B, D, F) showing the difference in immune cell infiltration between the resistant and sensitive groups.
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Medicina, Medicina clinica, Medicina interna, Ematologia, Oncologia, Radiologia
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