Identification and validation of anoikis-associated gene SNCG as a prognostic biomarker in gastric cancer

Development and validation of a prognostic and drug sensitivity model for gastric cancer utilizing telomere-related genes

BACKGROUND

Gastric cancer (GC) poses a major global health challenge because of its unfavorable prognosis. Elevated telomerase activity has been linked to the rapid growth and invasiveness of GC tumors. Investigating the expression profiles of telomerase could improve our understanding of the mechanisms underlying telomere-related GC advancement and its applicability as potential targets for diverse therapeutic strategies for GC.

METHODS

The TCGA and GEO databases were utilized to access transcriptome and clinical data related to GC. After assessing differentially expressed genes (DEGs), a prognostic risk model was developed through Cox univariate regression, LASSO-Cox regression. The prognostic risk model was validated using data from the GSE62254 cohort. The significant influence of the risk model on the tumor immune microenvironment (TIME) and its sensitivity to various drugs was assessed.

RESULTS

Differential expression analysis identified 328 significantly telomere-related DEGs in GC, with 35 of them showing a significant association with GC prognosis. A predictive risk model composed of four telomere-related genes (TRGs) was established, enabling the accurate stratification of GC patients into two distinct prognostic groups. The LASSO risk model demonstrated notable variations in immune-cell infiltration and drug sensitivity patterns between high- and low-risk groups.

CONCLUSIONS

The study establishes suggestive relationships between four TRGs (LRRN1, SNCG, GAMT, and PDE1B) and the prognosis of GC. The comprehensive characterization of the TRG model reveals their possible roles in the prognosis, TIME, and drug sensitivity in GC.

Characterize molecular signatures and establish a prognostic signature of gastric cancer by integrating single-cell RNA sequencing and bulk RNA sequencing

Gastric cancer is a significant global health concern with complex molecular underpinnings influencing disease progression and patient outcomes. Various molecular drivers were reported, and these studies offered potential avenues for targeted therapies, biomarker discovery, and the development of precision medicine strategies. However, it was posed that the heterogeneity of the disease and the complexity of the molecular interactions are still challenging. By seamlessly integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq), we embarked on characterizing molecular signatures and establishing a prognostic signature for this complex malignancy. We offered a holistic view of gene expression landscapes in gastric cancer, identified 226 candidate marker genes from 3 different dimensions, and unraveled key players' risk stratification and treatment decision-making. The convergence of molecular insights in gastric cancer progression occurs at multiple biological scales simultaneously. The focal point of this study lies in developing a prognostic model, and we amalgamated four molecular signatures (COL4A1, FKBP10, RNASE1, SNCG) and three clinical parameters using advanced machine-learning techniques. The model showed high predictive accuracy, with the potential to revolutionize patient care by using clinical variables. This will strengthen the reliability of the model and enable personalized therapeutic strategies based on each patient's unique molecular profile. In summary, our research sheds light on the molecular underpinnings of gastric cancer, culminating in a powerful prognostic tool for gastric cancer. With a firm foundation in biological insights and clinical implications, our study paves the way for future validations and underscores the potential of integrated molecular analysis in advancing precision oncology.

Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing

Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.

Association between Parkinson's Disease and Cancer: New Findings and Possible Mediators

Epidemiological evidence points to an inverse association between Parkinson's disease (PD) and almost all cancers except melanoma, for which this association is positive. The results of multiple studies have demonstrated that patients with PD are at reduced risk for the majority of neoplasms. Several potential biological explanations exist for the inverse relationship between cancer and PD. Recent results identified several PD-associated proteins and factors mediating cancer development and cancer-associated factors affecting PD. Accumulating data point to the role of genetic traits, members of the synuclein family, neurotrophic factors, the ubiquitin-proteasome system, circulating melatonin, and transcription factors as mediators. Here, we present recent data about shared pathogenetic factors and mediators that might be involved in the association between these two diseases. We discuss how these factors, individually or in combination, may be involved in pathology, serve as links between PD and cancer, and affect the prevalence of these disorders. Identification of these factors and investigation of their mechanisms of action would lead to the discovery of new targets for the treatment of both diseases.

The anoikis-related gene signature predicts survival and correlates with immune infiltration in osteosarcoma

Anoikis is essential for the progression of many malignant tumors. However, the understanding of anoikis' roles in osteosarcoma remains scarce. This study conducted an extensive bioinformatics analysis to identify anoikis-related genes (ARGs), developed ARGs modeles for predicting OS and RFS, and evaluated the effect of these ARGs on osteosarcoma cell migration and invasion. The GSE16088 and GSE28425 datasets provided the differentially expressed genes (DEGs). The prognostic significance and functions of these DEGs were systematically investigated using several bioinformatics techniques. Transwell assays were conducted to determine the effect of OGT on osteosarcoma cell migration and invasion. Seven genes were identified as hub genes, including FN1, CD44, HRAS, TP53, PPARG, CTNNB1, and VEGFA, while 71 ARGs were identified as DEGs. Four ARGs-BRMS, COL4A2, FGF2, and OGT-were used to develop an RFS-predicting model, whereas seven ARGs-CD24, FASN, MMP2, EIF2AK3, ID2, PPARG, and PIK3R3-were used to develop an OS-predicting model in patients with osteosarcoma. In both the training and validation cohorts, high-risk group patients had significantly shorter OS and RFS duration than low-risk group patients. Furthermore, using the aforementioned ARGs, we developed clinically applicable nomograms for OS and RFS prediction. The proportion of tumor-infiltrating immune cells was significantly linked to risk scores. In vitro experiments revealed that knocking down OGT significantly inhibited the ability of MG63 and U2OS cells to invade and migrate. ARG-based gene signatures reliably predicted RFS and OS in osteosarcoma, and OGT showed promise as a potential biomarker. These findings contribute to a better understanding of ARGs' prognostic roles in osteosarcoma.