Abstract
Renal cell carcinoma (RCC), also called as renal adenocarcinoma or hypernephroma, is the most common form cancer that occurs in kidney. About 9 out 10 malignant complications that occurs in kidney are RCC, and it accounts for 4.2% of all cancer types. And, patients with RCC tend to have poor prognosis with declining survival probability as the pathology advances. However, the current oncogenic detection procedures are quite inept in precisely predicting the prognostic outcomes for RCC patients in due time. Concurrently it is also well established that to prevent allograft rejection, induced immunosuppression can also actuate tumor progression. Conversely, the biomarkers that are involved in allograft rejection can also be used to asses the prognosis of cancer progression. Based on this notion, in the present study we aim to formulate immune response based prognostic biomarkers to aid clinicians to effectively asses and detect RCC prognosis. Methods: The biomarkers based out of allograft rejections were used as prognostic markers in RCC and were bolstered by series of statistical data analysis performed on kidney renal clear cell carcinoma (KIRC) cohorts based out of The Cancer Genome Atlas (TCGA) dataset. Results: Based on differential gene expression analysis between diseased and control group. a prognostic signature consisting of 14 allograft rejection associated genes (ARGs) CCL22, CSF1, CXCL13, ETS1, FCGR2B, GBP2, HLA-E, IL4R, MAP4K1, ST8SIA4, TAP2, TIMP1, ZAP70, TLR6 were delineated and were validated with the help of series of statistical analysis to assed their robustness. Consistent findings form univariate and multivariate regression analysis, survival analysis and risk prediction analysis, indicates that aforementioned set of genes can indeed be used as biomarkers to aid in RCC prognosis. And the cox regression analysis based out of these markers predicted the largest area under the curve (AUC = 0.8) in the receiver operating characteristic (ROC). Conclusions: The immune system based prognostic, predictive model formulated here can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.
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Acknowledgement
This study was supported by Provincial Science and Technology Grant of Shanxi Province (20210302124588), Science and technology innovation project of Shanxi province universities (2019L0683).
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Liu, H., Chen, Z., Gopalakrishnan, C., Ramalingam, R., Han, P., li, Z. (2022). The Prognosis Model of Clear Cell Renal Cell Carcinoma Based on Allograft Rejection Markers. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_33
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