Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma

Abstract

Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.

The Role of the L-Arginine–Nitric Oxide Molecular Pathway in Autosomal Dominant Polycystic Kidney Disease

“…in creatine[5,43,44]. The final step in this pathway is catalyzed by ARG2 (an enzyme situated in the renal cortex and proximal tubes) and allows the urea to be used for excretion and ornithine regeneration for this cycle[2,17].The present study shows that ADPKD is associated with the serum deficiency of Larginine. CKD is also associated with low levels of L-arginine, but the variations were weak compared to ADPKD.…”

“…Arginine is limited in the rapid turnover of immune cells and tumoral cells [ 4 , 5 , 6 , 7 , 8 , 9 ]. This metabolic vulnerability has been documented in cellular proliferation, sepsis, inflammation, immunological tolerance, immunosuppression, carcinogenesis, cyst formation, and carcinogenesis (cutaneous melanoma, squamous cell carcinoma), solid cancers (prostate, liver, pancreas, lung, pleural mesothelioma, head and neck, clear cell renal carcinoma), and blood cancers (acute myeloid leukemia, lymphomas) [ 4 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

“…Cells’ dependence on arginine could be influenced by different factors, such as the following: cell type, gene expression that encodes enzymes responsible for arginine metabolism, the cellular microenvironment, gene mutations, chromosomal structural changes, Th1/Th2 immunological polarization, spatial and temporal heterogeneity, metabolic plasticity, disease progression, and interconnection with other metabolic and inflammatory pathways [ 1 , 4 , 6 , 8 , 10 , 17 , 20 , 21 , 22 , 23 , 24 ].…”

“…OTC, which catalyzes ornithine conversion in carbamoyl phosphate and citrulline in mitochondria, is very important in arginine recirculation[6,[36][37][38][39][40][41][42] (Figure1).Due to accelerated metabolism in ADPKD, the hyperproliferative cells need high concentrations of arginine. Arginine is transported from the extracellular microenvironment in the cytoplasm by cationic amino-acid transporters (SLC7A1, SLC7A2, SLC7A3, SLC7A4, SLC3A2)[17]. Arginine is degraded by arginase 1 (ARG1) and arginase 2 (ARG2) in ornithine and urea; inducible nitric oxide synthase (NOSi, E.C.3.1.14.1.39) in NO and citrulline; arginine deaminase (ADI, E.C.…”

Multi-omics and immune cells’ profiling of COVID-19 patients for ICU admission prediction: in silico analysis and an integrated machine learning-based approach in the framework of Predictive, Preventive, and Personalized Medicine

“…In the majority of cases, integrated these algorithms might give better performances. With the development of genomics and improvement of gene chip, studies would go deep into the molecule level, and many researchers began to use RNA sequencing (RNA-seq) data to build prediction models [ 59 – 61 ]. Yet, it is worth noting that RNA-seq could not provide accurate absolute measurements, and often produced gene-specific biases due to different laboratories, different platforms or different analysis pipelines.…”