Up-regulation expression and prognostic significance of Syntaxin4 in kidney renal clear cell carcinoma

SNARE proteins: Core engines of membrane fusion in cancer

Vesicles are loaded with a variety of cargoes, including membrane proteins, secreted proteins, signaling molecules, and various enzymes, etc. Not surprisingly, vesicle transport is essential for proper cellular life activities including growth, division, movement and cellular communication. Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) mediate membrane fusion of vesicles with their target compartments that is fundamental for cargo delivery. Recent studies have shown that multiple SNARE family members are aberrantly expressed in human cancers and actively contribute to malignant proliferation, invasion, metastasis, immune evasion and treatment resistance. Here, the localization and function of SNARE proteins in eukaryotic cells are firstly mapped. Then we summarize the expression and regulation of SNAREs in cancer, and describe their contribution to cancer progression and mechanisms, and finally we propose engineering botulinum toxin as a strategy to target SNAREs for cancer treatment.

Exosome secretion related gene signature predicts chemoresistance in patients with colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a highly heterogeneous malignancy, and patients often have different responses to treatment. In this study, the genetic characteristics related to exosome formation and secretion procedure were used to predict chemoresistance and guide the individualized treatment of patients.

METHODS

Firstly, seven microarray datasets in Gene Expression Omnibus (GEO) and RNA-Seq dataset from the Cancer Genome Atlas (TCGA) were used to analysis the transcriptome profiles and associated characteristics of CRC patients. Then, a predictive model based on gene features linked to exosome formation and secretion was created and validated using Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning. Finally, we evaluated the model using chemoresistant/chemosensitive cells and tissues by immunofluorescence (IF), western blot (WB), quantitative real-time PCR (qRT-PCR) and immunocytochemistry (IHC) experiments, and the predictive value of integrated model in the clinical validation cohort were performed by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves analyses.

RESULTS

We established a risk score signature based on three genes related to exosome secretion in CRC. Better Overall Survival (OS) and greater chemosensitivity were seen in the low-risk group, whereas the high-risk group exhibited chemoresistance and a subpar response to immune checkpoint blockade (ICB) therapy. Higher expression of the model genes EXOC2, EXOC3 and STX4 were observed in chemoresistant cells and specimens. The AUC of 5-year disease-free survival (DFS) was 0.804. Compared with that in the low-risk group, patients' DFS was found to be significantly worse in the high-risk group.

CONCLUSIONS

In summary, the gene signature related to exosome formation and secretion could reliably predict patients' chemosensitivity and ICB treatment response, which providing new independent biomarkers for the treatment of CRC.

STX4 as a potential biomarker for predicting prognosis and guiding clinical treatment decisions in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) represents a frequent subtype of kidney cancer, with the prognosis remaining poor for individuals with metastatic disease. Given its resistance to both radiation and chemotherapy, targeted therapies and immunotherapies have emerged as critical for effective ccRCC treatment. Within this context, the SNARE protein STX4, which is associated with malignant cancer cell migration, provides a promising focus. The underlying mechanism, however, requires further illumination. Furthermore, the influence of STX4 on the ccRCC tumor microenvironment remains to be determined. In our research, we utilized multiple databases and immunohistochemical staining to confirm differential STX4 expression and its prognostic implications. We evaluated the potential tumor-promoting function of STX4 in ccRCC cell lines through molecular studies. Additionally, we conducted functional enrichment analysis to delve deeper into the underlying mechanisms and performed immune infiltration and drug sensitivity analyses to assess the potential of STX4 as a prognostic biomarker and therapeutic target. Our study reveals that STX4 contributes to cancer progression by enhancing AKT expression and stimulating the activation of VEGF signaling pathways. Additionally, STX4 further fosters CD8+ T-cell infiltration and diminishes the percentage of CAFs and M2-TAMs. Our findings suggest that patients presenting higher STX4 levels may exhibit enhanced responsiveness to immunotherapy and higher sensitivity to the medications axitinib and everolimus. Finally, we propose STX4 expression assessment as a novel approach to predict patient response to respective immunotherapies and targeted treatments, hence potentially improving patient outcomes.

Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer

Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis.

Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes.

Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines.

Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.