Epigenetic downregulation of desmin in gall bladder cancer reveals its potential role in disease progression

Construction and validation of a prognostic model based on immune-metabolic-related genes in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC), a significant type of head and neck cancer, has witnessed increasing incidence and mortality rates. Immune-related genes (IRGs) and metabolic-related genes (MRGs) play essential roles in the pathogenesis, metastasis, and progression of OSCC. This study exploited data from The Cancer Genome Atlas (TCGA) to identify IRGs and MRGs related to OSCC through differential analysis. Univariate Cox analysis was utilized to determine immune-metabolic-related genes (IMRGs) associated with patient prognosis. A prognostic model for OSCC was constructed using Lasso-Cox regression and subsequently validated with datasets from the Gene Expression Omnibus (GEO). Non-Negative Matrix Factorization (NMF) clustering identified three molecular subtypes of OSCC, among which the C2 subtype showed better overall survival (OS) and progression-free survival (PFS). A prognostic model based on nine IMRGs was developed to categorize OSCC patients into high- and low-risk groups, with the low-risk group demonstrating significantly longer OS in both training and testing cohorts. The model showed strong predictive capabilities, and the risk score served as an independent prognostic factor. Additionally, expression levels of programmed death 1 (PD1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) differed between the risk groups. Gene Set Enrichment Analysis (GSEA) indicated distinct enriched pathways between high-risk and low-risk groups, highlighting the crucial roles of immune and metabolic processes in OSCC. The nine IMRGs prognostic model presented excellent predictive performance and has potential for clinical application.

Lactate-induced activation of tumor-associated fibroblasts and IL-8-mediated macrophage recruitment promote lung cancer progression

Alterations in the tumor microenvironment are closely associated with the metabolic phenotype of tumor cells. Cancer-associated fibroblasts (CAFs) play a pivotal role in tumor growth and metastasis. Existing studies have suggested that lactate produced by tumor cells can activate CAFs, yet the precise underlying mechanisms remain largely unexplored. In this study, we initially identified that lactate derived from lung cancer cells can promote nuclear translocation of NUSAP1, subsequently leading to the recruitment of the transcriptional complex JUNB-FRA1-FRA2 near the DESMIN promoter and facilitating DESMIN transcriptional activation, thereby promoting CAFs' activation. Moreover, DESMIN-positive CAFs, in turn, secrete IL-8, which recruits TAMs or promotes M2 polarization of macrophages, further contributing to the alterations in the tumor microenvironment and facilitating lung cancer progression. Furthermore, we observed that the use of IL-8 receptor antagonists, SB225002, or Navarixin, significantly reduced TAM infiltration and enhanced the therapeutic efficacy of anti-PD-1 or anti-PD-L1 treatment. This finding indicates that inhibiting IL-8R activity can attenuate the impact of CAFs on the tumor microenvironment, thus restraining the progression of lung cancer.

Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Background

Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary.

Methods

Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed.

Results

We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3, and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59).

Conclusion

Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.