Predicting response of immunotherapy and targeted therapy and prognosis characteristics for renal clear cell carcinoma based on m1A methylation regulators

Abstract

In recent years, RNA methylation modification has been found to be related to a variety of tumor mechanisms, such as rectal cancer. Clear cell renal cell carcinoma (ccRCC) is most common in renal cell carcinoma. In this study, we get the RNA profiles of ccRCC patients from ArrayExpress and TCGA databases. The prognosis model of ccRCC was developed by the least absolute shrinkage and selection operator (LASSO) regression analysis, and the samples were stratified into low–high risk groups. In addition, our prognostic model was validated through the receiver operating characteristic curve (ROC). “pRRophetic” package screened five potential small molecule drugs. Protein interaction networks explore tumor driving factors and drug targeting factors. Finally, polymerase chain reaction (PCR) was used to verify the expression of the model in the ccRCC cell line. The mRNA matrix in ArrayExpress and TCGA databases was used to establish a prognostic model for ccRCC through LASSO regression analysis. Kaplan Meier analysis showed that the overall survival rate (OS) of the high-risk group was poor. ROC verifies the reliability of our model. Functional enrichment analysis showed that there was a obviously difference in immune status between the high-low risk groups. “pRRophetic” package screened five potential small molecule drugs (A.443654, A.770041, ABT.888, AG.014699, AMG.706). Protein interaction network shows that epidermal growth factor receptor [EGRF] and estrogen receptor 1 [ESR1] are tumor drivers and drug targeting factors. To further analyze the differential expression and pathway correlation of the prognosis risk model species. Finally, polymerase chain reaction (PCR) showed the expression of YTHN6-Methyladenosine RNA Binding Protein 1[YTHDF1], TRNA Methyltransferase 61B [TRMT61B], TRNA Methyltransferase 10C [TRMT10C] and AlkB Homolog 1[ALKBH1] in ccRCC cell lines. To sum up, the prognosis risk model we created not only has good predictive value, but also can provide guidance for accurately predicting the prognosis of ccRCC.

Genomic Fabrics of the Excretory System’s Functional Pathways Remodeled in Clear Cell Renal Cell Carcinoma

“…Thus, the Postulate of Transcriptomic Stoichiometry (PTS, [44]) also extends Dalton's Law of Multiple Proportions from chemistry [45] to gene networks. This analysis of the expression correlation among the genes of the "Endocrine and other factor-regulated calcium reabsorption" revealed an interesting partnership with the estrogen receptor 1 (ESR1), which is a tumor driver and drug-targeting factor in cancer [70], and regulator of age-related mitochondrial dysfunction and inflammation [71]. Thus, while its expression synergisms with the ATP2B3 (ATPase, Ca ++ transporting, plasma membrane 3) and KLK2 (kallikrein-related peptidase 2) in NOR are not modified by ccRCC, the antagonistic expression with the AP2A1 is switched to a synergistic one in all three cancer nodules.…”

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

“…Immunotherapies, including immune checkpoint inhibitors like nivolumab and pembrolizumab, seek to mobilize the immune system against cancer cells by disrupting evasion mechanisms . Molecularly targeted agents, such as sunitinib and pazopanib, leverage specific genetic alterations to inhibit angiogenesis and tumor growth pathways [23,30,38,[42][43][44][45][46][47][48]. These recent treatment options can complement the treatment landscape of RCC using complementary molecular mechanism based on the studies of biology, pathology, and genomics of renal carcinoma and can ultimately start the diamond age of precision treatment where RCC medicine can be tailored to specific patients based on biological and pathological circumstances [17].…”

“…Recognizing the strains imposed by clinical trials on patients' well-being, researchers have diligently sought optimized treatments and an expanded understanding of RCC through diverse models, including in vitro or in vivo models such as mice, cell lines, machine learning, and mathematical frameworks [49][50][51][52][53][54][55][56][57][58][59][60][61]. Moreover, researchers have attempted to predict patient outcomes and improve treatment options for patients using various models and findings [21,36,40,45,47,[62][63][64][65][66][67][68][69][70][71][72][73].…”