A novel pan-cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic.

A sensitive and specific diagnosis biomarker, in principle scalable to most cancer types, is needed to reduce the prevalent cancer mortality. Meanwhile, the investigation of diagnosis determinants of a biomarker will facilitate the interpretation of its screening results in clinic. Here we design a large-scale (1558 enrollments), multicenter (multiple hospitals), and cross-validation (two datasets) clinic study to validate plasma Hsp90α quantified by ELISA as a pan-cancer biomarker. ROC curve shows the optimum diagnostic cutoff is 69.19 ng/mL in discriminating various cancer patients from all controls (AUC 0.895, sensitivity 81.33% and specificity 81.65% in test cohort; AUC 0.893, sensitivity 81.72% and specificity 81.03% in validation cohort). Similar results are noted in detecting early-stage cancer patients. Plasma Hsp90α maintains also broad-spectrum for cancer subtypes, especially with 91.78% sensitivity and 91.96% specificity in patients with AFP-limited liver cancer. In addition, we demonstrate levels of plasma Hsp90α are determined by ADAM10 expression, which will affect Hsp90α content in exosomes. Furthermore, Western blotting and PRM-based quantitative proteomics identify that partial false ELISA-negative patients secret high levels of plasma Hsp90α. Mechanism analysis reveal that TGFß-PKCγ gene signature defines a distinct pool of hyperphosphorylated Hsp90α at Theronine residue. In clinic, a mechanistically relevant population of false ELISA-negative patients express also higher levels of PKCγ. In sum, plasma Hsp90α is a novel pan-cancer diagnosis biomarker, and cancer diagnosis with plasma Hsp90α is particularly effective in those patients with high expression of ADAM10, but may be insufficient to detect the patients with low ADAM10 and those with hyperphosphorylated Hsp90α.

A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer.

BACKGROUND: There is currently a limited number of studies on transglutaminase type 1 (TGM1) in tumors. The objective of this study is to perform a comprehensive analysis across various types of cancer to determine the prognostic significance of TGM1 in tumors and investigate its role in the immune environment. METHOD: Pan-cancer and mutational data were retrieved from the TCGA database and analyzed using R (version 3.6.4) and its associated software package. The expression difference and prognosis of TGM1 were examined, along with its correlation with tumor heterogeneity, stemness, mutation landscape, and RNA modification. Additionally, the relationship between TGM1 expression and tumor immunity was investigated using the TIMER method. RESULTS: TGM1 is expressed differently in various tumors and normal samples and is associated with the overall survival and progression-free time of KIRC, ACC, SKCM, LIHC, and STES. In LICH, we found a negative correlation between TGM1 expression and 6 indicators of tumor stemness. The mutation frequencies of BLCA, LIHC, and KIRC were 1.7%, 0.3%, and 0.3% respectively. In BLCA and BRCA, there was a significant correlation between TGM1 expression and the infiltration of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells. CONCLUSION: TGM1 has the potential to serve as both a prognostic marker and a drug target.

A new survival model based on ferroptosis-related genes for prognostic prediction in clear cell renal cell carcinoma.

In this study, we analyzed the clinical significance of ferroptosis-related genes (FRGs) in 32 cancer types in the GSCA database. We detected a 2-82% mutation rate among 36 FRGs. In clear cell renal cell carcinoma (ccRCC; n=539) tissues from the The Cancer Genome Atlas database, 30 of 36 FRGs were differentially expressed (up- or down-regulated) compared to normal kidney tissues (n=72). Consensus clustering analysis identified two clusters of FRGs based on similar co-expression in ccRCC tissues. We then used LASSO regression analysis to build a new survival model based on five risk-related FRGs (CARS, NCOA4, FANCD2, HMGCR, and SLC7A11). Receiver operating characteristic curve analysis confirmed good prognostic performance of the new survival model with an area under the curve of 0.73. High FANCD2, CARS, and SLC7A11 expression and low HMGCR and NCOA4 expression were associated with high-risk ccRCC patients. Multivariate analysis showed that risk score, age, stage, and grade were independent risk factors associated with prognosis in ccRCC. These findings demonstrate that this five risk-related FRG-based survival model accurately predicts prognosis in ccRCC patients, and suggest FRGs are potential prognostic biomarkers and therapeutic targets in several cancer types.