Solute carrier-related signature for assessing prognosis and immunity in patients with clear-cell renal cell carcinoma

Cystine/cysteine metabolism regulates the progression and response to treatment of triple‑negative breast cancer (Review)

Breast cancer is the most prevalent neoplasm affecting women globally, of which a notable proportion of cases are triple-negative breast cancer (TNBC). However, there are limited curative treatment options for patients with TNBC, despite advancements in the field. Amino acids and amino acid transporters serve vital roles in the regulation of tumor metabolism. Notably, cystine and cysteine can interconvert via a redox reaction, with cysteine exerting control on cell survival and growth and exogenous cystine serving a crucial role in the proliferation of numerous types of cancers. Breast cancer has been reported to disrupt the cystine/cysteine metabolism pathway, as cystine and cysteine transporters affect the development and growth of tumors. The present review aims to provide a comprehensive overview of the metabolic pathways involving cystine and cysteine in normal and TNBC cells. Furthermore, the roles of cystine and cysteine transporters in TNBC progression and metastasis and their potential as therapeutic targets for treatment of TNBC are evaluated.

Integrating transcriptomics and machine learning for immunotherapy assessment in colorectal cancer

Colorectal cancer (CRC) is a highly prevalent and lethal malignancy worldwide. Although immunotherapy has substantially improved CRC outcomes, intolerance remains a major concern among most patients. Considering the pivotal role of the tumor microenvironment (TME) in tumor progression and treatment outcomes, profiling the TME at the transcriptomic level can provide novel insights for developing CRC treatment strategies. Seventy-seven TME-associated signatures were acquired from previous studies. To elucidate variations in prognosis, clinical features, genomic alterations, and responses to immunotherapy in CRC, we employed a non-negative matrix factorization algorithm to categorize 2595 CRC samples of 27 microarrays from the Gene Expression Omnibus database. Three machine learning techniques were employed to identify a signature specific to immunotherapy. Subsequently, the mechanisms by which this signature interacts with TME subtypes and immunotherapy were investigated. Our findings revealed five distinct TME subtypes (TMESs; TMES1-TMES5) in CRC, each exhibiting a unique pattern of immunotherapy response. TMES1, TMES4, and TMES5 had relatively inferior outcomes, TMES2 was associated with the poorest prognosis, and TMES3 had a superior outcome. Subsequent investigations revealed that activated dendritic cells could enhance the immunotherapy response rate, with their augmentation effect closely associated with the activation of CD8+T cells. We successfully classified CRC into five TMESs, each demonstrating varying response rates to immunotherapy. Notably, the application of machine learning to identify activated dendritic cells helped elucidate the underlying mechanisms contributing to these differences. We posit that these TMESs hold promising clinical implications for prognostic evaluation and guidance of immunotherapy strategies, thereby providing valuable insights to inform clinical decision-making.