Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with good predictive performance for ICB outcomes in the ccRCC context, we reanalyzed two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients receiving ICB treatment. A subtype of proliferative CD4+ T cells and regulatory T cells and a subtype of antigen-presenting monocytes that have good predictive capability and are correlated with ICB outcomes were identified. These findings were corroborated in independent ccRCC ICB pretreatment bulk RNA-seq datasets. By incorporating the cluster-specific marker genes of these three immunocyte subtypes, we developed a prediction model, which reached an AUC of 93% for the CheckMate cohort (172 samples). Our study shows that the ICB response prediction model can serve as a valuable clinical decision-making tool for guiding ICB treatment of ccRCC patients.

Peroxisome proliferator activated receptor gamma coactivator-1 alpha (PGC-1α/PPARGC1A) is a pivotal transcriptional coactivator involved in the regulation of mitochondrial metabolism, including biogenesis and oxidative metabolism. PGC-1α is finely regulated by AMP-activated protein kinases (AMPKs), the role of which in tumors remains controversial to date. In recent years, a growing amount of research on PGC-1α and tumor metabolism has emphasized its importance in a variety of tumors, including prostate cancer (PCA). Compelling evidence has shown that PGC-1α may play dual roles in promoting and inhibiting tumor development under certain conditions. Therefore, a better understanding of the critical role of PGC-1α in PCA pathogenesis will provide new insights into targeting PGC-1α for the treatment of this disease. In this review, we highlight the procancer and anticancer effects of PGC-1α in PCA and aim to provide a theoretical basis for targeting AMPK/PGC-1α to inhibit the development of PCA. In addition, our recent findings provide a candidate drug target and theoretical basis for targeting PGC-1α to regulate lipid metabolism in PCA.

BackgroundMost patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC.MethodsTwo HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1+ B cells, invasive metastasis-related ACTB+ Tregs, and proinflammatory-related macrophage subtypes with good predictive power and associated with chemotherapy response were identified. These results were verified in an independent HGSOC bulk RNA-seq dataset for chemotherapy. Further validation in clinical cohorts used quantitative real-time PCR (qRT-PCR).ResultsBy combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 (p = 2.1e-07) for the GSE156699 cohort (88 samples). A huge improvement was achieved compared to existing prediction models with a maximum AUC of 0.74. In addition, its predictive capability was validated in multiple independent bulk RNA-seq datasets. The qRT-PCR results demonstrate that the expression of the six genes has the highest diagnostic value, consistent with the trend observed in the analysis of public data.ConclusionsThe developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.