Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with a poor prognosis. During the development of cancer cells, mitochondria influence various cell death patterns by regulating metabolic pathways such as oxidative phosphorylation. However, the relationship between mitochondrial function and cell death patterns in HCC remains unclear. In this study, we used a comprehensive machine learning framework to construct a mitochondrial functional activity-associated programmed cell death index (MPCDI) based on scRNA-seq and RNA-seq data from TCGA, GEO, and ICGC datasets. The index signature was used to classify HCC patients, and studied the multi-omics features, immune microenvironment, and drug sensitivity of the subtypes. Finally, we constructed the MPCDI signature consisting of four genes (S100A9, FYN, LGALS3, and HMOX1), which was one of the independent risk factors for the prognosis of HCC patients. The HCC patients were divided into high- and low-MPCDI groups, and the immune status was different between the two groups. Patients with a high MPCDI had higher TIDE scores and poorer responses to immunotherapy, suggesting that high-MPCDI patients might not be suitable for immunotherapy. By analyzing the drug sensitivity data of CTRP, GDSC, and PRISM databases, it was found that staurosporine has potential therapeutic significance for patients with a high MPCDI. In summary, based on the characteristics of mitochondria function and PCD patterns, we used single-cell and transcriptome data to identify four genes and construct the MPCDI signature, which provided new perspectives and directions for the clinical diagnosis and personalized treatment of HCC patients.

Insect wing development involves tissue patterning, cell fate transitions, and hormone signaling, yet its spatiotemporal logic remains unclear. The silkworm, with large wing discs and defined stages, provides an ideal model for high resolution analysis. Here, we construct a spatiotemporal single-cell atlas of the silkworm wing disc across 10 timepoints, identifying 12 major cell types and their developmental transitions. Wing morphogenesis (Wm) cells act as central progenitors, differentiating into epithelial and cuticle lineages under lineage-specific transcription factors. Time‑resolved snRNA‑seq reveals hierarchical transcriptional reprogramming, with Wm cells functioning as early signaling hubs. Functional modules and signaling pathways were activated in spatiotemporal controlled manner. 20‑hydroxyecdysone treatment rapidly accelerates fate transitions and gene expression, recapitulating natural development within hours. Integration of morphology, hormone levels, and gene expression supports a five-stage Gene Transition Model describing progressive fate resolution. This work reveals wing development in silkworm and provides insights into hormone-driven organogenesis and potential manipulation of insect development in agriculture.

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with a poor prognosis. During the development of cancer cells, mitochondria influence various cell death patterns by regulating metabolic pathways such as oxidative phosphorylation. However, the relationship between mitochondrial function and cell death patterns in HCC remains unclear. In this study, we used a comprehensive machine learning framework to construct a mitochondrial functional activity-associated programmed cell death index (MPCDI) based on scRNA-seq and RNA-seq data from TCGA, GEO, and ICGC datasets. The index signature was used to classify HCC patients, and studied the multi-omics features, immune microenvironment, and drug sensitivity of the subtypes. Finally, we constructed the MPCDI signature consisting of four genes ( S100A9 , FYN , LGALS3 , and HMOX1 ), which was one of the independent risk factors for the prognosis of HCC patients. The HCC patients were divided into high- and low-MPCDI groups, and the immune status was different between the two groups. Patients with high MPCDI had higher TIDE scores and poorer responses to immunotherapy, suggesting that high-MPCDI patients might not be suitable for immunotherapy. By analyzing the drug sensitivity data of CTRP, GDSC, and PRISM databases, it was found that staurosporine has potential therapeutic significance for patients with high MPCDI. In summary, based on the characteristics of mitochondria function and PCD patterns, we used single-cell and transcriptome data to identify four genes and construct the MPCDI signature, which provided new perspectives and directions for the clinical diagnosis and personalized treatment of HCC patients.

Uterine corpus endometrial carcinoma (UCEC) is a highly heterogeneous gynecological malignancy characterized by strong invasive and metastatic capabilities, high recurrence rates, and low survival rates. The ERBB family plays a ubiquitous role in signal transduction and the pathogenesis of various malignancies, yet studies related to the ERBB family in UCEC are limited. This research identifies key genes within key enriched pathways of the ERBB family based on the UCEC dataset from The Cancer Genome Atlas (TCGA). Subsequently, prognostic genes were further selected through differential gene analysis and univariate Cox analysis. These prognostic genes were then incorporated into a composite machine learning framework based on 10 machine learning algorithms, coupled with the GSE119041 validation cohort, to construct a six-gene (ERBB2, CCNE1, CLDN6, CLDN9, LAMC2, RAC3) risk score (RS) model. Additionally, the model's performance was evaluated, a Nomogram integrating clinical features was constructed, and tumor immune microenvironment and drug sensitivity analyses were performed. The RS model demonstrated good clinical predictive performance, with the high-RS group exhibiting worse prognosis, lower tumor immune microenvironment-related score, and significantly higher immune infiltration levels in Plasma cells and Mast cells resting. OSI-027 and Sepantronium bromide may serve as potential candidate drugs for treating UCEC. In summary, this research develops a prognosis-related risk model for UCEC based on the ERBB family, providing novel biomarkers and insights for personalized treatment of UCEC patients.