A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes.

BACKGROUND: Renal cell carcinoma (RCC) is a third most common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state. METHODS: In this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. Then we established a prognostic risk model consisting of the genes most related to prognosis from four signatures to value prognosis of the RCC samples via Kaplan-Meier (KM) survival analysis. An independent data from International Cancer Genome Consortium (ICGC) database were used to test the predictive stability of the model. Furthermore, we performed landscape analysis to assess the difference of gene mutant in the RCC samples from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform. RESULTS: We used four genetic signatures to construct prognostic risk models respectively and found that each of the models could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. A comprehensive prognostic risk model was constructed by 8 candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from TCGA database into high-risk and low-risk groups with a significant difference in cancer-specific survival (CSS). The stability of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. Landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC. CONCLUSIONS: Sum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.

Predictive Value of Heart-Type Fatty Acid-Binding Protein for Mortality Risk in Critically Ill Patients.

Objective: Our study assessed the predictive value of heart-type fatty acid-binding protein (H-FABP) for critically ill patients. Methods: 150 critically ill patients admitted to the emergency department of Beijing Chaoyang Hospital, Capital Medical University, were included in our study from August 2021 to April 2022. Serum H-FABP, procalcitonin (PCT), lactate (LAC), and other markers were determined within 1 h after admission. The Sequential Organ Failure Assessment (SOFA) score and the Acute Physiology and Chronic Health Evaluation II (APACHE II) were calculated. The independent predictors of 28-day mortality in critically ill patients were analyzed by logistic regression, and the receiver operating characteristic curve (ROC) was used to analyze the predictive value for 28-day mortality in critically ill patients. Results: Age, APACHE II, SOFA, GCS, LAC, H-FABP, IL-6, Scr, and D-dimer were significantly different in the nonsurvivor vs. survivor groups (P < 0.05), with H-FABP correlating with cTNI, Scr, PCT, and SOFA scores (P < 0.05). Logistic regression analysis showed that H-FABP, APACHE II, LAC, and age were independent predictors for 28-day mortality in critically ill patients (P < 0.05). The AUC of ROC curve in H-FABP was 0.709 (sensitivity 72.9%, specificity 66.1%, and cut-off 4.35), which was slightly lower than AUC of ROC curve in LAC (AUC 0.750, sensitivity 58.3%, specificity 76.1%, and cut-off 1.95) and APACHE II (AUC 0.731, sensitivity 77.1%, specificity 58.7%, and cut-off 12.5). However, statistically, there was no difference in the diagnostic value of H-FABP compared with the other two indicators (Z 1 = 0.669, P = 0.504; Z 2 = 0.383, P = 0.702). But H-FABP (72.9%) has higher sensitivity than LAC (58.3%). The combined evaluation of H-FABP+APACHE II score (AUC 0.801, sensitivity 71.7%, and specificity 78.2%; Z = 2.612, P = 0.009) had better diagnostic value than H-FABP alone and had high sensitivity (71.7%) and specificity (78.2%). Conclusion: H-FABP, LAC, APACHE II, and age can be used as independent risk factors affecting the prognosis of critically ill patients. Compared with using the above indicators alone, the H-FABP+APACHE II has a high diagnostic value, and the early and rapid evaluation is particularly important for the adjustment of treatment plans and prognosis.