LAUSR

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.