A research team used flowering data from 169 rice genotypes—each with over 700,000 SNP markers—across multiple environments to develop a robust framework for phenotypic prediction.

HPVTIMER is a comprehensive web-based analysis tool based on the GEO database for HPV-associated cancers. HPVTIMER has four embedded analysis modules: Differential expression analysis module, Correlation analysis module, Immune infiltration analysis module, and Pathway analysis module. HPVTIMER supports users in performing longitudinal systematic analyses and cross-sectional comparisons of data, which can help users explore the tumour immune microenvironment of HPV-associated cancers and search for potential immune regulatory mechanisms and immunotherapeutic targets.

Researchers have systematically elucidated the anti-tumour mechanisms of regulatory T cells (Tregs) for the first time. These immune cells not only suppress pro-tumour inflammation but also enhance anti-tumour immunity, challenging the long-held view that Tregs inevitably promote tumour progression. This discovery provides key insights for developing next-generation immunotherapy strategies.

Drug sensitivity analysis is crucial for precision cancer therapy. We developed CPADS, a web tool integrating transcriptomic data from 29,000+ samples (44 cancers, 288 drugs, 9,000+ gene perturbations). It enables differential expression, pathway, drug, and gene perturbation analyses with interactive visualization. CPADS aids researchers in exploring drug resistance mechanisms at gene/pathway levels. Access: https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

We are thrilled to announce the publication of our groundbreaking work in PLoS Computational Biology, introducing PESSA (Pathway Enrichment Score-based Survival Analysis) – a robust, user-friendly web platform designed to revolutionize cancer survival data analysis. PESSA uniquely integrates pathway enrichment status as a critical biomarker, offering oncologists and researchers unprecedented insights. Our platform boasts an expansive curated database of over 200 cancer datasets from leading sources (GEO, TCGA, EGA, and published literature), encompassing 51 cancer types, 13 distinct survival outcome measures, and over 13,000 tumor-relevant pathways. PESSA is meticulously designed to accelerate the discovery and validation of novel cancer-related pathway biomarkers. Access PESSA today at: https://smuonco.shinyapps.io/PESSA/ or http://robinl-lab.com/PESSA.

This review synthesizes current knowledge on the triggers and characteristics of T cell senescence in the tumor microenvironment (TME), elucidates how senescent T cells interact with other immune cells, and assesses the impact of these cells on tumor prognosis. In addition, this review systematically examines targeted therapeutic strategies aimed at mitigating the detrimental effects of T cell senescence on cancer treatment.