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. 2023 Jul 12;23(1):649.
doi: 10.1186/s12885-023-11150-4.

Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma

Ziwei Gui #  1 , Juan Du #  1 , Nan Wu #  2 , Ningning Shen  3 , Zhiqing Yang  3 , Huijun Yang  1 , Xuzhi Wang  1 , Na Zhao  3 , Zixin Zeng  1 , Rong Wei  3 , Wenxia Ma  4 , Chen Wang  5
Affiliations

Immune regulation and prognosis indicating ability of a newly constructed multi-genes containing signature in clear cell renal cell carcinoma

Ziwei Gui et al. BMC Cancer. .

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal malignancy, although newly developing targeted therapy and immunotherapy have been showing promising effects in clinical treatment, the effective biomarkers for immune response prediction are still lacking. The study is to construct a gene signature according to ccRCC immune cells infiltration landscape, thus aiding clinical prediction of patients response to immunotherapy.

Methods: Firstly, ccRCC transcriptome expression profiles from Gene Expression Omnibus (GEO) database as well as immune related genes information from IMMPORT database were combine applied to identify the differently expressed meanwhile immune related candidate genes in ccRCC comparing to normal control samples. Then, based on protein-protein interaction network (PPI) and following module analysis of the candidate genes, a hub gene cluster was further identified for survival analysis. Further, LASSO analysis was applied to construct a signature which was in succession assessed with Kaplan-Meier survival, Cox regression and ROC curve analysis. Moreover, ccRCC patients were divided as high and low-risk groups based on the gene signature followed by the difference estimation of immune treatment response and exploration of related immune cells infiltration by TIDE and Cibersort analysis respectively among the two groups of patients.

Results: Based on GEO and IMMPORT databases, a total of 269 differently expressed meanwhile immune related genes in ccRCC were identified, further PPI network and module analysis of the 269 genes highlighted a 46 genes cluster. Next step, Kaplan-Meier and Cox regression analysis of the 46 genes identified 4 genes that were supported to be independent prognosis indicators, and a gene signature was constructed based on the 4 genes. Furthermore, after assessing its prognosis indicating ability by both Kaplan-Meier and Cox regression analysis, immune relation of the signature was evaluated including its association with environment immune score, Immune checkpoint inhibitors expression as well as immune cells infiltration. Together, immune predicting ability of the signature was preliminary explored.

Conclusions: Based on ccRCC genes expression profiles and multiple bioinformatic analysis, a 4 genes containing signature was constructed and the immune regulation of the signature was preliminary explored. Although more detailed experiments and clinical trials are needed before potential clinical use of the signature, the results shall provide meaningful insight into further ccRCC immune researches.

Keywords: Clear cell renal cell carcinoma (ccRCC); Gene signature; Immune response; LASSO analysis; Prediction biomarker.

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Conflict of interest statement

All of the authors agreed the publication of the paper and declare no conflicts of interests.

Figures

Fig. 1
Fig. 1
Differential expression genes in ccRCC comparing to normal renal samples identified from GEO profiles. Four GEO profiles (A) GSE53000, (B) GSE53757, (C) GSE68417 and (D) GSE71963 were accessed to identify differently expressed genes in ccRCC vs normal renal samples, and based on these profiles, the up-regulated (right side) and down-regulated (left side) differential expression genes in ccRCC were identified. The genes were then divided into four groups based on the expression difference level as: < twofold genes (orange-colored spots), 2 ~ fourfold genes (red-colored spots), 4 ~ eightfold genes (green-colored spots) and > eightfold genes (black-colored spots). E The intersection of the genes in four GEO profiles for revealing the genes that were shared in different profiles. F The intersection of differential expressed genes revealed by GEO profiles and immune related genes from IMMPORT database, thus the genes that were both differential expressed and immune related were revealed
Fig. 2
Fig. 2
PPI network construction of 269 differential expressed meanwhile immune related genes in ccRCC and function modules analysis. (A) Based on four GEO profiles as well as IMMPORT datasets, 269 differential expression meanwhile immune related genes in ccRCC were identified, and the PPI network of these 269 genes was constructed. And based on the PPI network, (B) the first, (C) second and (C) third genes function modules were analyzed, each module was shown with a diagrammatic sketch (left diagram) and the detailed module information (right table) including the computed module score, module description and detailed involving genes. (*The first module with the highest module score meanwhile immune regulation related was focused for further analysis)
Fig. 3
Fig. 3
Survival analysis and basic physicochemical properties exploration of four selected signature comprised genes. The overall survival (left) and disease free survival (right) analysis in ccRCC patients, including (A) MMP9 gene, (B) NFKB1 gene, (C) IRF7 gene and (D) HMOX1 gene. The predicted cellular location (left) and computed hydrophility/hydrophobicity property of (E) MMP9 protein, (F) NFKB1 protein, (G) IRF7 protein and (H) HMOX1 protein
Fig. 4
Fig. 4
The differential expression of four selected signature comprised genes in ccRCC included human cancers. UALCAN prediction of (A) MMP9 gene, (D) IRF7 gene, (G) NFKB1 gene and (J) HMOX1 gene expression in broad spectrum human cancers. GEPIA analysis of (B) MMP9 gene, (E) IRF7 gene, (H) NFKB1 gene and (K) HMOX1 gene in ccRCC comparing to normal renal samples. QPCR experiment using local hospital ccRCC samples for validating the changed expression of (C) MMP9 gene, (F) IRF7 gene, (I) NFKB1 gene and (L) HMOX1 gene in ccRCC comparing to normal renal samples
Fig. 5
Fig. 5
The association between four selected hub genes expression and ccRCC clinical parameters. The association between MMP9 expression and ccRCC (A) patients gender, (B) age, (C) cancer stage, (D) cancer grade and (E) lymph node metastasis. The association between IRF7 expression and ccRCC (F) patients gender, (G) age, (H) cancer stage, (I) cancer grade and (J) lymph node metastasis. The association between NFKB1 expression and ccRCC (K) patients gender, (L) age, (M) cancer stage, (N) cancer grade and (O) lymph node metastasis. The association between HMOX1 expression and ccRCC (P) patients gender, (Q) age, (R) cancer stage, (S) cancer grade and (T) lymph node metastasis. (* p < 0.05, **p < 0.01, ***p < 0.001. The first layer * which is right above the error bar representing comparison to normal group, and the above layers * which were above a secondary line represent the comparison between corresponding groups that were covered by the line)
Fig. 6
Fig. 6
PPI network centered on four selected hub genes and GO/KEGG analysis of their enriched biological pathways. (A) The PPI network which is centered on MMP9 gene for analyzing (B) the main biological signaling pathways MMP9 and its connected genes mainly participated in. (C) The PPI network which is centered on IRF7 gene for analyzing (D) the main biological signaling pathwaysIRF7 and its connected genes mainly participated in. (E) The PPI network which is centered on NFKB1 gene for analyzing (F) the main biological signaling pathways NFKB1 and its connected genes mainly participated in. (G) The PPI network which is centered on MMP9 gene for analyzing (H) the main biological signaling pathways MMP9 and its connected genes mainly participated in. (KEGG software analysis permitted by Kanehisa laboratory)
Fig. 7
Fig. 7
Construction of a four genes containing meanwhile immune and prognosis related ccRCC gene signature. LASSO analysis to calculate (A) the coefficient and (B) the likelihood deviance for constructing a suitable immune meanwhile prognosis related signature which was comprised of strictly calculated four genes. (C) TCGA ccRCC patients were divided into high-risk and low-risk groups based on the calculated signature score (the cut off value was set as the median signature score in all samples). (D) Survival analysis of the high-risk and low-risk groups of ccRCC patients. (E) ROC curve of the gene signature to predict ccRCC patients survival of 1 year, 3 years and 5 years respectively. (F) ccRCC patients prognosis prediction nomogram constructed based on genes signature and clinical parameters which were supported by Cox Regression to be independently related with patients survival. (G) Significant enrichment of immune-related phenotype including immune response and immune cells migration in high-risk group of ccRCC patients compared with that in low-risk group patients
Fig. 8
Fig. 8
Correlation between gene signature and ccRCC immune microenvironment landscape. (A) Relative expression of ICD related genes in high-risk and low-risk groups of ccRCC patients. (B) Correlation between gene signature and ccRCC computed immune score, stromal score and tumor purity calculated using ESTIMATE algorithm. (C) Estimated immune score, stromal score and tumor purity distribution in high-risk and low-risk ccRCC groups respectively. (D) Association between gene signature and immune checkpoints expression. (E) Correlation between gene signature and PD-L1, LAG-3, TIGIT and CALT-4 expression respectively. (F) Relative expression of five immune checkpoints including PD-L1, LAG-3, TIGIT, TIM3 and CALT-4 expression in high-risk and low-risk ccRCC groups respectively
Fig. 9
Fig. 9
Correlation between gene signature and 22 immune cells infiltration in ccRCC. (A) Relative distribution of 22 immune cells in high-risk and low-risk groups of ccRCC patients. (B, C) Correlation between gene signature and various immune cells infiltration in ccRCC. Association between (D) T cells CD4 memory activated, (E) T cells CD4 memory resting and (F) Mast cells resting microenvironment infiltration and ccRCC patients survival
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References

    1. Wu J, Xu WH, Wei Y, Qu YY, Zhang HL, Ye DW. An integrated score and nomogram combining clinical and immunohistochemistry factors to predict high isup grade clear cell renal cell carcinoma. Front Oncol. 2018;8:634. doi: 10.3389/fonc.2018.00634. - DOI - PMC - PubMed
    1. Browning L, Colling R, Verrill C. WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade. Diagn Pathol. 2021;16(1):75. doi: 10.1186/s13000-021-01130-2. - DOI - PMC - PubMed
    1. Stone L. Kidney cancer: activation of oncogenes driven by VHL loss in ccRCC. Nat Rev Urol. 2017;14(11):637. doi: 10.1038/nrurol.2017.162. - DOI - PubMed
    1. Expert Group of G, Urological Pathology CSoP [Recent advances and expert consensus on molecular pathology of renal cell carcinomas (2020 version)] Zhonghua Bing Li Xue Za Zhi. 2020;49(12):1232–1241. - PubMed
    1. Williamson SR, Gill AJ, Argani P, Chen YB, Egevad L, Kristiansen G, Grignon DJ, Hes O. Report from the International Society of Urological Pathology (ISUP) consultation conference on molecular pathology of urogenital cancers: III: molecular pathology of kidney cancer. Am J Surg Pathol. 2020;44(7):e47–e65. doi: 10.1097/PAS.0000000000001476. - DOI - PMC - PubMed