Accumulating evidence has supported the effect of antibody-dependent cellular phagocytosis (ADCP) on the tumor microenvironment (TME) and cancer therapy. However, an ADCP-based signature to predict the prognosis of gastric cancer (GC) has not been established. We aimed to develop an ADCP-based signature to improve the prognosis prediction of GC. Antibody-dependent cellular phagocytosis genes that exhibited a differential expression were characterized, followed by the construction and validation of the ADCP-based signature. The potential association between the ADCP-based signature and TME was explored, and the features of the signature genes were investigated. Finally, a predictive nomogram was established based on the ADCP-based signature. Four ADCP-related genes, MKNK2, VCAN, LRAT, and GNGB, were identified to construct the ADCP-based signature, and a high ADCP score predicted an unfavorable prognosis in GC patients (p < 0.05). The ADCP-based signature was significantly associated with immune cells, immune checkpoints and immune signaling pathways (p < 0.05). Gastric cancer patients with high ADCP scores benefited less from immunotherapy compared to those with low ADCP scores. A nomogram including age, stage and risk score of the ADCP-based signature was constructed to predict the 1-, 3- and 5-year survival probabilities, with an area under the curve (AUC) of 0.669, 0.675 and 0.685, respectively. The ADCP-based signature may serve as a new option for prognosis prediction and the personalized treatment of GC patients.

During the course of treating non-small cell lung cancer (NSCLC) with epithelial growth factor receptor (EGFR) mutant, gefitinib resistance (GR) is unavoidable. As the environment for tumor cells to grow and survive, tumor microenvironment (TME) can significantly affect therapeutic response and clinical outcomes, offering new opportunities for addressing GR. Dynamic changes within the TME were identified during the treatment of gefitinib, suggesting the close relationship between TME and GR. Various dynamic processes like angiogenesis, hypoxia-pathway activation, and immune evasion can be blocked so as to synergistically enhance the therapeutic effects of gefitinib or reverse GR. Besides, cellular components like macrophages can be reprogrammed for the same purpose. In this review, we summarized recently proposed therapeutic targets to provide an overview of the potential roles of TME in treating gefitinib-resistant NSCLC, and discussed the difficulty of applying these targets in cancer treatment.

Introduction: Tumor heterogeneity challenges the accuracy of existing prognostic models for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Prognostic models that correct for tumor heterogeneity remain unexplored. Methods: Based on the GSE5787 dataset comprising 33 multiregional samples from 11 patients with CESC, we calculated both interpatient heterogeneity (IPH) and intratumoral heterogeneity (ITH) scores. Genes with low heterogeneity were selected to construct a prognostic model using the CESC cohort from The Cancer Genome Atlas (TCGA). Key gene expression was validated in HeLa and SiHa cell lines. The functional consequences of gene knockout were assessed through Cell Counting Kit-8 (CCK-8) proliferation assays, wound healing migration assays, and Transwell assays. Results: Genes exhibiting low heterogeneity were predominantly associated with energy metabolism pathways, whereas those displaying high heterogeneity were enriched in immune-related signaling pathways. The risk score based on Armadillo Repeat Containing X-Linked 1 (ARMCX1) and Signal Transducing Adaptor Family Member 2 (STAP2) achieved area under the curve (AUC) values of 0.74, 0.68, and 0.74 for predicting 1-year, 3-year, and 5-year overall survival, respectively. Importantly, this signature demonstrated robust performance with a risk classification inconsistency rate of merely 18%, outperforming 10 previously published CESC prognostic models. The integrative nomogram incorporating both the risk score and clinical staging demonstrated enhanced prognostic accuracy. Although patients in the high-risk group exhibited diminished immunotherapy efficacy, drug sensitivity screening identified seven potentially effective therapeutic agents as alternative treatment options. In vitro experiments demonstrated that STAP2 knockdown suppressed HeLa cell proliferation, migration, and invasive capacity. Conclusion: This study establishes a robust gene signature through ITH correction, which may mitigate single-region sampling bias and offers a promising tool for prognostic stratification in CESC.

Various recent studies have shown a link between the gut microbiome and bone mineral density (BMD). Whether the link represents a causative relationship, however, remains unclear. This study aims to thoroughly investigate the causal effects of the gut microbiome on BMD and pinpoint which particular gut microbe taxa have causal effects on BMDs at different specific skeletal sites. Based on summary statistics from genome-wide association studies (GWAS), a two-sample Mendelian randomization (MR) study was carried out to examine the effects of the gut microbiome at five taxonomic ranks (phylum, class, order, family, and genus) on BMDs at five skeletal sites (total body BMD [TB-BMD], femoral neck BMD [FN-BMD], forearm BMD [FA-BMD], heel BMD [H-BMD], lumbar spine BMD [LS-BMD]). After Bonferroni correction, a negative causal effect of the genus Eisenbergiella was shown exclusively on H-BMD (inverse variance weighted (IVW): β = -0.037, 95% confidence interval (CI) = -0.055, -0.018, P = 0.0001). On the contrary, the genus Oscillibacter had a positive causal effect exclusively on H-BMD (IVW: β = 0.029, 95% CI = 0.013, 0.044, P = 0.0003). At other skeletal sites, only nominal causal effects were observed. The reliability of these findings was strengthened by our following several sensitivity studies. A reverse MR analysis was conducted, which showed no reverse causation between H-BMD and the beforementioned two genera, Eisenbergiella and Oscillibacter. These findings show insights into the pathophysiology of low BMD and the association between the gut microbiome and bone health.

Background: As the core treatment for cervical cancer, chemoradiotherapy efficacy is often limited by tumor heterogeneity-driven resistance. During chemoradiotherapy, the tumor microenvironment undergoes dynamic remodeling, with residual malignant cells evolving adaptively. However, the key regulatory genes driving therapeutic resistance remain elusive at single-cell resolution, which hinders the development of novel targeted therapies. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on paired clinical samples from three cervical cancer patients before and after chemoradiotherapy to identify the cellular changes and the molecular mechanisms induced by chemoradiotherapy. Through integrated multi-omics data analysis, non-heterogeneity-dependent genes with prognostic value were identified. Furthermore, the biological function of the candidate gene in chemoradiotherapy-resistant HeLa cells was verified through in vitro functional experiments, including the detection of cell proliferation, migration and invasion abilities. Results: Chemoradiotherapy significantly reprogrammed the cellular composition of the tumor microenvironment (TME) in cervical cancer. Residual malignant cells showed activation of angiogenesis, epithelial-mesenchymal transition, stress-response, and cell-cycle programs. Eight heterogeneity-filtered prognostic genes were identified, and phosphoglucomutase 2 (PGM2) was prioritized because it was upregulated in cervical cancer, enriched in malignant cells, associated with poor prognosis, and supported by independent transcriptomic, spatial, and immunohistochemical evidence. PGM2 knockdown reduced proliferation, migration, and invasion in resistant HeLa cells. Conclusion: PGM2 may contribute to the malignant phenotype of chemoradiotherapy-resistant cervical cancer cells and represents a candidate biomarker and therapeutic target. Further validation in larger clinical cohorts and laboratory work is needed before clinical translation.

Commercial molecular classifiers such as Prediction Analysis of Microarray 50 (PAM50), Oncotype DX, and MammaPrint have revolutionized breast cancer management. However, their clinical reliability is undermined by tumor heterogeneity. We quantified the discordance in clinical molecular typing across multi-regional samples. Gene set enrichment analysis (GSEA) was employed to explore potential sources of intratumoral heterogeneity (ITH). Gene-level heterogeneity was evaluated by quantifying expression variation within versus between tumors. A robust subtyping framework was constructed via Non-negative Matrix Factorization (NMF) based on a screened low-ITH gene set. ITH was observed in over 50% of tumors, triggering significant classification discordance across commercial tests. GSEA revealed that transcriptomic heterogeneity might be driven by stromal and matrix-related pathways. The low-ITH-based NMF framework established an optimal three-subtype classification that exhibited superior prognostic stratification and maintained high stability across metastatic sites. We established a low-ITH subtyping framework that overcomes the limitations of single-biopsy-based molecular typing. Our findings offer a novel strategy to enhance the precision of breast cancer management.