Analysis of Polymorphisms in the MATN3 and DOT1L Genes and CTX-II Urinary Levels in Patients with Knee Osteoarthritis in a Northeast Mexican-Mestizo Population

Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC

BACKGROUND

Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC).

MATERIALS AND METHODS

The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset.

RESULTS

According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset.

CONCLUSION

The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.

The effect of ITLN1, XCL2 and DOT1L variants on knee osteoarthritis risk in the Han population

BACKGROUND

Knee osteoarthritis (KOA) is a complex multifactorial disease, in which genetic factors account for 50% of the risk of osteoarthritis. This study investigated the association between ITLN1, XCL2 and DOT1L variants and KOA risk in the Han population.

METHODS

We applied Agena MassARRAY technology platform to genotype five single-nucleotide polymorphisms (SNPs) in 710 KOA patients and 709 controls. The correlation between ITLN1, XCL2 and DOT1L SNPs (rs2274908, rs4282797, rs4301615 and rs3815308) and KOA risk was calculated by logistic regression analysis, with odds ratio (OR) and 95% confidence intervals (CIs). Also, the relationship between genotypes at these different loci and clinical parameters (White blood cell, Eosinophil ratio, Neutrophil count, Eosinophil count, Monocyte ratio and Monocyte count) among patients and controls was analyzed.

RESULTS

In the overall results, rs2274908, rs4301615 and rs3815308 were correlated with KOA susceptibility. After gender stratification analysis, ITLN1 rs2274908 and XCL2 rs4301615 were related to an increased risk of KOA in males, and ITLN1 rs2274908 and DOT1L rs3815308 were related to an increased risk of KOA in females. After age stratification analysis, ITLN1 rs2274908 and XCL2 rs4301615 were correlated with an increased risk of KOA in people aged > 65 year old. After smoking stratification analysis and drinking stratification analysis, ITLN1 rs2274908, XCL2 rs4301615 and DOT1L rs3815308 were still associated with the risk of KOA.

CONCLUSION

In short, ITLN1 rs2274908, XCL2 rs4301615 and DOT1L rs3815308 were related to KOA risk in the Han population, which was helpful to clarify the pathogenesis of these sites in KOA.

Emerging molecular biomarkers in osteoarthritis pathology

Osteoarthritis (OA) is the most common form of arthritis resulting in joint discomfort and disability, culminating in decline in life quality. Attention has been drawn in recent years to disease-associated molecular biomarkers found in readily accessible biofluids due to low invasiveness of acquisition and their potential to detect early pathological molecular changes not observed with traditional imaging methodology. These biochemical markers of OA have been found in synovial fluid, blood, and urine. They include emerging molecular classes, such as metabolites and noncoding RNAs, as well as classical biomarkers, like inflammatory mediators and by-products of degradative processes involving articular cartilage. Although blood-based biomarkers tend to be most studied, the use of synovial fluid, a more isolated biofluid in the synovial joint, and urine as an excreted fluid containing OA biomarkers can offer valuable information on local and overall disease activity, respectively. Furthermore, larger clinical studies are required to determine relationships between biomarkers in different biofluids, and their impacts on patient measures of OA. This narrative review provides a concise overview of recent studies of OA using these four classes of biomarkers as potential biomarker for measuring disease incidence, staging, prognosis, and therapeutic intervention efficacy.

Identification of MATN3 as a Novel Prognostic Biomarker for Gastric Cancer through Comprehensive TCGA and GEO Data Mining

Gastric cancer (GC) is still a vital malignant cancer across the world with unsatisfactory prognostic results. Matrilin-3 (MATN3) is a member of the extracellular matrix (ECM) protein family. The present research intends to explore the expression level of MATN3 in patients with GC and to explore the prognosis significance of MATN3. In this study, we observed that the MATN3 expression was remarkably upregulated in GC samples in contrast to noncancer samples. Clinical analyses unveiled that high MATN3 expression was related to age, tumor status, and clinical stages. Survival analyses unveiled that patients with high MATN3 expression displayed a poorer overall survival and progression-free survival than those with low MATN3 expression. The AUC of the relevant ROC curve for 1 year, 3 years, and 5 years of survival is 0.571, 0.596, and 0.720, separately. Multivariate assays revealed that MATN3 expression and stage were independent predictors of poor prognosis of GC patients. A meta-analysis unveiled that high MATN3 expression was tightly associated with better overall survival. Overall, our data indicated that MATN3 may have a diagnostic and prognostic value for patients with advanced gastric cancer and assist to improve clinical outcomes for GC patients.