Dataset Information

Construction and validation of a prognostic model for kidney renal clear cell carcinoma based on podocyte-associated genes.

ABSTRACT:

Background

As the most common renal malignancy, kidney renal clear cell carcinoma (KIRC) has a high prevalence and death rate as well as a poor response to treatment. Developing an efficient prognostic model is essential for accurately predicting the outcome and therapeutic benefit of KIRC patients.

Methods

Gene expression profiles of podocyte-associated genes (PAGs) were obtained from The Cancer Genome Atlas and GEO datasets. Cox regression and Lasso regression analyses were then used for filtering prognosis-associated PAGs. Risk score (RS) was computed from these genetic characteristics. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were applied for ascertaining the prognostic value. Stratified analysis was used to sufficiently validate model performance. Concordance index was used to compare the predictive ability of different models. Immuno-infiltration analysis and immunophenoscore were utilized for the prediction of patient reaction to immune checkpoint inhibitors (ICIs).

Results

WT1, ANLN, CUBN, OSGEP, and RHOA were significantly associated with KIRC prognosis. Prognostic analysis indicated that high-RS patients have a significantly poorer outcome. Cox regression analysis demonstrated a potential for RS to be an independent prognostic factor. Pathway enrichment results indicated a lower enrichment of cancer-related biological pathways in the low-RS subgroup. Immune infiltration analysis and IPS demonstrated greater responsiveness to ICIs in the high RS group.

Conclusions

This podocyte-associated KIRC prognostic model can effectively predict KIRC prognosis and immunotherapy response, which may help to provide clinicians with more effective treatment strategies.

SUBMITTER: Chen C

PROVIDER: S-EPMC9554457 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Construction and validation of a prognostic model for kidney renal clear cell carcinoma based on podocyte-associated genes.

Chen Can C Yang Rui-Xia RX Zhang Jie-Xin JX Xu Hua-Guo HG

Cancer medicine 20220404 19

<h4>Background</h4>As the most common renal malignancy, kidney renal clear cell carcinoma (KIRC) has a high prevalence and death rate as well as a poor response to treatment. Developing an efficient prognostic model is essential for accurately predicting the outcome and therapeutic benefit of KIRC patients.<h4>Methods</h4>Gene expression profiles of podocyte-associated genes (PAGs) were obtained from The Cancer Genome Atlas and GEO datasets. Cox regression and Lasso regression analyses were then ...[more]

PMID: 35373928

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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a highly heterogeneous tumor and is the most common subtype of renal cell carcinoma (RCC). Surgery is used to cure most early ccRCC, but the 5-year overall survival (OS) of ccRCC patients is far from satisfactory. Thus, new prognostic features and therapeutic targets for ccRCC need to be identified. Since complement factors can influence tumor development, we aimed to develop a model to predict the prognosis of ccRCC through complement-related genes.MethodsDifferentially expressed genes were screened from an International Cancer Genome Consortium (ICGC) data set, and the genes associated with prognosis were screened by univariate regression and least absolute shrinkage and selection operator-Cox regression, and column line plots were generated using the rms R package to predict OS. The C-index was used to show the accuracy of the survival prediction and the prediction effects were verified using a data set from The Cancer Genome Atlas (TCGA). An immuno-infiltration analysis was performed with CIBERSORT analysis, and a drug sensitivity analysis was performed using the Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/) database.ResultsWe identified 5 complement-related genes (i.e., A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4) for risk-score modeling to predict OS at 1, 2, 3, and 5 years, and the C-index of the prediction mode was 0.795. In addition, the model was successfully validated in TCGA data set. The CIBERSORT analysis showed that M1 macrophages were downregulated in the high-risk group. The GSCA database analysis showed that DOCK4, COL4A2, and A2M were positively correlated with the half maximal inhibitory concentration (IC50) of 10 drugs and small molecules, and COL4A2, NOTCH4, A2M, and APOBEC3G were negatively correlated with the IC50 of dozens of different drugs and small molecules.ConclusionsWe developed and validated a survival prognostic model based on 5 complement-related genes for ccRCC. We also elucidated the relationship with tumor immune status and developed a new predictive tool for clinical purposes. In addition, our results showed that A2M, APOBEC3G, COL4A2, DOCK4, and NOTCH4 may be potential targets for the treatment of ccRCC in the future.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the human urinary system. Macrophage differentiation is associated with tumorigenesis. Therefore, exploring the prognostic value of macrophage differentiation-associated genes (MDGs) may contribute to better clinical management of ccRCC patients. Methods: The RNA sequence data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed MDGs were unveiled in ccRCC and normal samples. The prognostic model was established according to the univariate and multivariate Cox regression analyses. By combining clinico-pathological features and prognostic genes, a nomogram was established to predict individual survival probability. The Tumor Immune Estimation Resource (TIMER) database was utilized to analyze the correlation between prognostic genes and immune infiltrating cells. Eventually, the mRNA and protein expression levels of prognostic genes were verified. Results: A total of 52 differentially expressed prognosis-related MDGs were identified in ccRCC. Afterward, a six-gene prognostic model (ABCG1, KDF1, KITLG, TGFA, HAVCR2, and CD14) was constructed through the Cox analysis. The overall survival in the high-risk group was relatively poor. Moreover, the risk score was identified as an independent prognostic factor. We constructed a prognostic nomogram with a well-fitted calibration curve based on risk score and clinical data. Furthermore, the prognostic genes were significantly related to the level of immune cell infiltration including B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells. Finally, the mRNA expression of prognostic genes in clinical ccRCC tissues showed that the ABCG1, HAVCR2, CD14, and TGFA mRNA in tumor samples were increased compared with the adjacent control tissue samples, while KDF1 and KITLG were decreased, which was consistent with the verification results in the GSE53757. Conclusion: In conclusion, this study identified and validated a macrophage differentiation-associated prognostic model for ccRCC that could be used to predict the outcomes of the ccRCC patients.

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Project description:Introduction: Kidney renal clear cell carcinoma (KIRC), a kind of malignant disease, is a severe threat to public health. Tracking the information of tumor progression and conducting a related dynamic prognosis model are necessary for KIRC. It is crucial to identify hypoxia-immune-related genes and construct a prognostic model due to immune interaction and the influence of hypoxia in the prognosis of patients with KIRC. Methods: The hypoxia and immune status of KIRC patients were identified by utilizing t-SNE and ImmuCellAI for gene expression data. COX and Lasso regression were used to identify some hypoxia-immune-related signature genes and further construct a prognostic risk model based on these genes. Internal and external validations were also conducted to construct a prognostic model. Finally, some potentially effective drugs were screened by the CMap dataset. Results: We found that high-hypoxia and low-immune status tend to induce poor overall survival (OS). Six genes, including PLAUR, UCN, PABPC1L, SLC16A12, NFE2L3, and KCNAB1, were identified and involved in our hypoxia-immune-related prognostic risk model. Internal verification showed that the area under the curve (AUC) for the constructed models for 1-, 3-, 4-, and 5-year OS were 0.768, 0.754, 0.775, and 0.792, respectively. For the external verification, the AUC for 1-, 3-, 4-, and 5-year OS were 0.768, 0.739, 0.763, and 0.643 respectively. Furthermore, the decision curve analysis findings demonstrated excellent clinical effectiveness. Finally, we found that four drugs (including vorinostat, fludroxycortide, oxolinic acid, and flutamide) might be effective and efficient in alleviating or reversing the status of severe hypoxia and poor infiltration of immune cells. Conclusion: Our constructed prognostic model, based on hypoxia-immune-related genes, has excellent effectiveness and clinical application value. Moreover, some small-molecule drugs are screened to alleviate severe hypoxia and poor infiltration of immune cells.

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Dataset Information

Construction and validation of a prognostic model for kidney renal clear cell carcinoma based on podocyte-associated genes.

Background

Methods

Results

Conclusions

Publications

Construction and validation of a prognostic model for kidney renal clear cell carcinoma based on podocyte-associated genes.

Similar Datasets

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