Prediction and prognostic significance of ALOX12B and PACSIN1 expression in gastric cancer by genome-wide RNA expression and methylation analysis

PACSIN1 promotes immunosuppression in gastric cancer by degrading MHC-I

Gastric cancer (GC) is a common gastrointestinal system malignancy. PACSIN1 functions as an oncogene in various cancers. This study aims to investigate the potential of PACSIN1 as a target in GC treatment. Gene expression is determined by RT-qPCR, immunofluorescence staining, and immunohistochemistry assay. FISH is performed to determine the colocalization of PACSIN1 and the major histocompatibility complex (MHC-I). Cytokine release and cell functions are analyzed by flow cytometry. In vivo assays are also conducted. Histological analysis is performed using H&E staining. The results show that PACSIN1 is overexpressed in GC patients, especially in those with immunologically-cold tumors. A high level of PACSIN1 is associated with poor prognosis. PACSIN1 deficiency inhibits autophagy but increases antigen presentation in GC cells. Moreover, PACSIN1 deficiency inhibits the lysosomal fusion and selective autophagy of MHC-I, increases CD8 + T-cell infiltration, and suppresses tumor growth and liver metastasis in vivo. Additionally, PACSIN1 knockout enhances the chemosensitivity of cells to immune checkpoint blockade. In summary, PACSIN1 mediates lysosomal fusion and selective autophagy of MHC-I and suppresses antigen presentation and CD8 + T-cell infiltration, thus inhibiting antitumor immunity in GC.

Prognostic significance of JAM 3 in gastric cancer: An observational study from TCGA and GEO

Junctional adhesion molecule 3 (JAM3) can be used as a prognostic marker in multiple cancer types. However, the potential prognostic role of JAM3 in gastric cancer (GC) remains unclear. The purpose of this research was to gauge JAM3 expression and methylation as potential biomarkers for GC patient survival. Through bioinformatics research, we analyzed JAM3 expression, methylation, prognosis, and immune cell infiltrations. JAM3 methylation acts as a negative regulator of JAM3, leading to reduced expression of JAM3 in GC tissues relative to normal tissues. Patients with GC who expressed little JAM3 have a better chance of living a long time free of the disease, according to the Cancer Genome Atlas (TCGA) database. Through univariate and multivariate Cox regression analysis, inadequate JAM3 expression was labeled as an isolated indicator for overall survival (OS). The GSE84437 dataset was also used to confirm JAM3 prognostic role in GC, with consistent findings. A meta-analysis also found that low levels of JAM3 expression were significantly associated with longer OS. Finally, there was a strong correlation between JAM3 expression and a subset of immune cells. According to the TCGA database, low JAM3 expression could predict favorable OS and progression-free-survival (PFS) in GC patients (P < .05). The univariate and multivariate Cox regression demonstrated that low JAM3 expression was independent biomarker for OS (P < .05). Moreover, GSE84437 dataset was utilized to verify the prognostic role of JAM3 in GC, and the similar results were reached (P < .05). A meta-analysis revealed that low JAM3 expression was closely relevant to better OS. Finally, JAM3 expression exhibited a close correlation with some immune cells (P < .05). JAM3 might be a viable predictive biomarker and likely plays a crucial part in immune cell infiltration in individuals with GC.

Longitudinal Plasma Proteomics-Derived Biomarkers Predict Response to MET Inhibitors for MET-Dysregulated NSCLC

MET inhibitors have shown promising efficacy for MET-dysregulated non-small cell lung cancer (NSCLC). However, quite a few patients cannot benefit from it due to the lack of powerful biomarkers. This study aims to explore the potential role of plasma proteomics-derived biomarkers for patients treated with MET inhibitors using mass spectrometry. We analyzed the plasma proteomics from patients with MET dysregulation (including MET amplification and MET overexpression) treated with MET inhibitors. Thirty-three MET-dysregulated NSCLC patients with longitudinal 89 plasma samples were included. We classified patients into the PD group and non-PD group based on clinical response. The baseline proteomic profiles of patients in the PD group were distinct from those in the non-PD group. Through protein screening, we found that a four-protein signature (MYH9, GNB1, ALOX12B, HSD17B4) could predict the efficacy of patients treated with MET inhibitors, with an area under the curve (AUC) of 0.93, better than conventional fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC) tests. In addition, combining the four-protein signature with FISH or IHC test could also reach higher predictive performance. Further, the combined signature could predict progression-free survival for MET-dysregulated NSCLC (p < 0.001). We also validated the performance of the four-protein signature in another cohort of plasma using an enzyme-linked immunosorbent assay. In conclusion, the four plasma protein signature (MYH9, GNB1, ALOX12B, and HSD17B4 proteins) might play a substitutable or complementary role to conventional MET FISH or IHC tests. This exploration will help select patients who may benefit from MET inhibitors.

Construction of mRNA prognosis signature associated with differentially expressed genes in early stage of stomach adenocarcinomas based on TCGA and GEO datasets

Background

Stomach adenocarcinomas (STAD) are the most common malignancy of the human digestive system and represent the fourth leading cause of cancer-related deaths. As early-stage STAD are generally mild or asymptomatic, patients with advanced STAD have short overall survival. Early diagnosis of STAD has a considerable influence on clinical outcomes.

Methods

The mRNA expression data and clinical indicators of STAD and normal tissues were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The gene expression differences were analyzed by R packages, and gene function enrichment analysis was performed. Kaplan–Meier method and univariate Cox proportional risk regression analysis were used to screen differential expressed genes (DEGs) related to survival of STAD patients. Multivariate Cox proportional risk regression analysis was used to further screen and determine the prognostic DEGs in STAD patients, and to construct a multigene prognostic prediction signature. The accuracy of predictive signature was tested by receiver operating characteristic (ROC) curve software package, and the nomogram of patients with STAD was drawn. Cox regression was used to investigate the correlation between multigene prognostic signature and clinical factors. The predictive performance of this model was compared with two other models proposed in previous studies using KM survival analysis, ROC curve analysis, Harrell consistency index and decision curve analysis (DCA). qRT-PCR and Western blot were used to verify the expression levels of prognostic genes. The pathways and functions of possible involvement of features were predicted using the GSEA method.

Results

A total of 569 early-stage specific DEGs were retrieved from TCGA-STAD dataset, including 229 up-regulated genes and 340 down-regulated genes. Enrichment analysis showed that the early-stage specific DEGs were associated with cytokine–cytokine receptor interaction, neuroactive ligand–receptor interaction, and calcium signaling pathway. Multiple Cox regression algorithm was used to identify 10 early-stage specific DEGs associated with overall survival (P < 0.01) of STAD patients, and a multi-mRNA prognosis signature was established. The patients were divided into high-risk group and low-risk group according to the risk score. In the training set, the prognostic signature was positively correlated with tumor size and stage (P < 0.05), survival curve (P < 0.001) and time-dependent ROC (AUC = 0.625). In the training dataset and test dataset, the both signatures had good predictive efficiencies. Cox regression and DCA analysis revealed that the prognostic signature was an independent factor and had a better predict effect than the conventional TNM stage classification method and the earlier published biomarkers on the prognosis of STAD patients.

Conclusion

In this study, based on the early-stage specifically expressed genes, the prognostic signature constructed through TCGA and GEO datasets may become an indicator for clinical prognosis assessment of STAD and a new strategy for targeted therapy in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-022-00827-4.