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. 2024 Mar 4:12:e16927.
doi: 10.7717/peerj.16927. eCollection 2024.

Identification and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19

Jianqiang Nie #  1 , Hailang Yang #  1 , Xiaoqiang Liu  1   2 , Wen Deng  1   2 , Bin Fu  1   2
Affiliations

Identification and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19

Jianqiang Nie et al. PeerJ. .

Abstract

Background: COVID-19 is a severe infectious disease caused by the SARS-CoV-2 virus, and previous studies have shown that patients with kidney renal clear cell carcinoma (KIRC) are more susceptible to SARS-CoV-2 infection than the general population. Nevertheless, their co-pathogenesis remains incompletely elucidated.

Methods: We obtained shared genes between these two diseases based on public datasets, constructed a prognostic risk model consisting of hub genes, and validated the accuracy of the model using internal and external validation sets. We further analyzed the immune landscape of the prognostic risk model, investigated the biological functions of the hub genes, and detected their expression in renal cell carcinoma cells using qPCR. Finally, we searched the candidate drugs associated with hub gene-related targets from DSigDB and CellMiner databases.

Results: We obtained 156 shared genes between KIRC and COVID-19 and constructed a prognostic risk model consisting of four hub genes. Both shared genes and hub genes were highly enriched in immune-related functions and pathways. Hub genes were significantly overexpressed in COVID-19 and KIRC. ROC curves, nomograms, etc., showed the reliability and robustness of the risk model, which was validated in both internal and external datasets. Moreover, patients in the high-risk group showed a higher proportion of immune cells, higher expression of immune checkpoint genes, and more active immune-related functions. Finally, we identified promising drugs for COVID-19 and KIRC, such as etoposide, fulvestrant, and topotecan.

Conclusion: This study identified and validated four shared genes for KIRC and COVID-19. These genes are associated with immune functions and may serve as potential prognostic biomarkers for KIRC. The shared pathways and genes may provide new insights for further mechanistic research and treatment of comorbidities.

Keywords: COVID-19; Co-pathogenesis; Comorbidity; KIRC; Shared gene.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Identification and functional annotation of shared genes.
(A) Volcano plot illustrating differential genes in COVID-19. (B) WGCNA analysis displaying the COVID-19 associated module. (C) Volcano plot showing differential genes in KIRC. (D) Venn plot demonstrating the shared genes between COVID-19 and KIRC. (E) GO enrichment analysis of the shared genes. (F) KEGG analysis of the shared genes.
Figure 2
Figure 2. Construction of risk models and validation in TCGA training set.
(A and B) Lasso-Cox regression analysis for gene screening. (C) Forest plot presenting hub genes obtained from multivariate analysis. (D–F) Validation of the risk model in the TCGA training set (KM curve, risk plot, ROC curve).
Figure 3
Figure 3. Validation of models in TCGA-KIRC testing set and entire TCGA-KIRC cohort and external independent cohort.
(A–C) Validation of the risk model in the TCGA-KIRC testing set (KM curve, ROC curve, risk plot). (D–F) Validation of the risk model in the entire TCGA-KIRC cohort (KM curve, ROC curve, risk plot). (G–I) Validation of the risk model in the external validation set ICGC-RECA-EU cohort (KM curve, ROC curve, risk plot).
Figure 4
Figure 4. Nomograms and PCA analysis of the risk model.
(A and B) Nomograms and calibration plots constructed in the TCGA-KIRC training set to predict OS at 1, 3, and 5 years. (C and D) Nomograms and calibration plots constructed in the TCGA-KIRC testing set to predict OS at 1, 3, and 5 years. (E and F) Nomograms and calibration plots constructed in the entire TCGA-KIRC cohort to predict OS at 1, 3, and 5 years. (G and H) Nomograms and calibration plots constructed in the external validation set to predict OS at 1, 3, and 5 years. (I–L) PCA scatter plots of risk models for high- and low-risk groups in (I) the TCGA-KIRC training set, (J) the TCGA-KIRC testing set, (K) the entire TCGA-KIRC cohort, and (L) the external validation set.
Figure 5
Figure 5. Immune landscape and tumor mutation load in low- and high-risk groups of the entire TCGA-KIRC cohort.
(A) Differential analysis of the tumor microenvironment in low- and high-risk groups. (B) Differential analysis of immune cells in low- and high-risk KIRC groups. (C) Differential analysis of immune checkpoints between low- and high-risk groups. (D) Analysis of differences in immune-related functions between low- and high-risk groups. (E) Correlation of risk score with TMB. (F) KM curve showing survival difference between high and low TMB patients. (G) KM curve showing survival difference analysis of patients with combined analysis of TMB and risk score. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 6
Figure 6. Expression validation and biological functions of hub genes and drug screening.
(A) PPI network presentation of hub genes by GeneMANIA portal. (B) The top five GO terms in the high-risk group. (C) The top five KEGG pathways in the high-risk group. (D) Expression of hub genes in GEO-COVID-19 cohort. (E) Expression of hub genes in TCGA-KIRC cohort. (F–I) Detection of hub gene expression in renal cancer cell lines by qRT-PCR. *p < 0.05, **p < 0.01, ***p < 0.001.
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