Cross-Talk of Focal Adhesion-Related Gene Defines Prognosis and the Immune Microenvironment in Gastric Cancer

Data-independent acquisition (DIA) mass spectrometry reveals related proteins involved in the occurrence of early intestinal-type gastric cancer

Identifying proteins associated with the onset of early intestinal-type gastric cancer (EIGC) can yield valuable insights into the pathogenesis of this specific subtype of gastric cancer. Data-independent acquisition mass spectroscopy (DIA-MS) was utilized to identify the differential protein between 10 cases of EIGC and atrophic gastritis with intestinal metaplasia (NGC). The expressions of IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were verified by immunohistochemistry (IHC) in 20 EIGC samples, 17 gastric low-grade intraepithelial neoplasia (LGIN) samples, and 21 healthy controls. The prognostic values of the five genes were validated in the transcriptome data by survival analysis. A total of 4,028 proteins were identified using DIA-MS and a total of 177 differential proteins were screened with log2(fold change) > 1.5. Among them, 113 proteins were significantly up-regulated, and 64 proteins were significantly down-regulated in EIGC tissues. IHC results showed that proteins IPO4, TBL1XR1, p62/SQSTM1, PKP3, and CRTAP were highly expressed in the cytoplasm of EIGC and LGIN, which was consistent with the results of DIA-MS. Among them, p62/SQSTM1 may undergo nuclear-cytoplasmic transfer. The five protein-coding genes were associated with intestinal-type gastric cancer survival and exhibited differential expression across various disease stages. The study successfully identified differentially expressed proteins between EIGC and NGC, providing potential biomarkers and valuable insights into the mechanism underlying intestinal-type gastric cancer.

Single-cell and bulk RNA sequencing reveals Anoikis related genes to guide prognosis and immunotherapy in osteosarcoma

Anoikis resistance, a notable factor in osteosarcoma, plays a significant role in tumor invasion and metastasis. This study seeks to identify a distinct gene signature that is specifically associated with the anoikis subcluster in osteosarcoma. Clinical, single-cell, and transcriptional data from TARGET and GEO datasets were used to develop a gene signature for osteosarcoma based on the anoikis subcluster. Univariate Cox and LASSO regression analyses were employed. The signature's predictive value was evaluated using time-dependent ROC and Kaplan-Meier analyses. Functional enrichment analyses and drug sensitivity analyses were conducted. Validation of three modular genes was performed using RT-qPCR and Western blotting. Signature (ZNF583, CGNL1, CXCL13) was developed to predict overall survival in osteosarcoma patients, targeting the anoikis subcluster. The signature demonstrated good performance in external validation. Stratification based on the signature revealed significantly different prognoses. The signature was an independent prognostic factor. The low-risk group showed enhanced immune cell infiltration and improved immune function. Drug sensitivity analysis indicated efficacy of chemotherapy agents. Prognostic nomograms incorporating the signature provided greater predictive accuracy and clinical utility. Signatures related to the anoikis subcluster play a significant role in osteosarcoma progression. Incorporating these findings into clinical decision-making can improve osteosarcoma treatment and patient outcomes.

Overexpression of Pasteurella multocida OmpA induces transcriptional changes and its possible implications for the macrophage polarization

Pasteurella multocida (P. multocida) is a highly infectious, zoonotic pathogen. Outer membrane protein A (OmpA) is an important virulence component of the outer membrane of P. multocida. OmpA mediates bacterial biofilm formation, eukaryotic cell infection, and immunomodulation. It is unclear how OmpA affects the host immune response. We estimated the role of OmpA in the pathogenesis of P. multocida by investigating the effect of OmpA on the immune cell transcriptome. Changes in the transcriptome of rat alveolar macrophages (NR8383) upon overexpression of P. multocida OmpA were demonstrated. A model cell line for stable transcription of OmpA was constructed by infecting NR8383 cells with OmpA-expressing lentivirus. RNA was extracted from cells and sequenced on an Illumina HiSeq platform. Key gene analysis of genes in the RNA-seq dataset were performed using various bioinformatics methods, such as gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, Gene Set Enrichment Analysis, and Protein-Protein Interaction Analysis. Our findings revealed 1340 differentially expressed genes. Immune-related pathways that were significantly altered in rat alveolar macrophages under the effect of OmpA included focal adhesion, extracellular matrix and vascular endothelial growth factor signaling pathways, antigen processing and presentation, nucleotide oligomerization domain-like receptor and Toll-like receptor signaling pathways, and cytokine-cytokine receptor interaction. The key genes screened were Vegfa, Igf2r, Fabp5, P2rx1, C5ar1, Nedd4l, Gas6, Cxcl1, Pf4, Pdgfb, Thbs1, Col7a1, Vwf, Ccl9, and Arg1. Data of associated pathways and altered gene expression indicated that OmpA might cause the conversion of rat alveolar macrophages to M2-like. The related pathways and key genes can serve as a reference for OmpA of P. multitocida and host interaction mechanism studies.

COL5A2 is a prognostic-related biomarker and correlated with immune infiltrates in gastric cancer based on transcriptomics and single-cell RNA sequencing

BACKGROUND

There is still a therapeutic challenge in treating gastric cancer (GC) due to its high incidence and poor prognosis. Collagen type V alpha 2 (COL5A2) is increased in various cancers, yet it remains unclear how it contributes to the prognosis and immunity of GC.

METHODS

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to download transcriptome profiling (TCGA-STAD; GSE84437), single-cell RNA sequencing (scRNA-seq) data (GSE167297) and clinical information. COL5A2 expression and its relationship with clinicopathological factors were analyzed. We conducted survival analysis and Cox regression analysis to evaluate the prognosis and independent factors of GC. Co-expressed analysis was also performed. To identify the underlying mechanism, we conducted analyses of differentially expressed genes (DEGs) and functional enrichment. The correlations between COL5A2 expression and immune cell infiltration levels and immune infiltrate gene marker sets were further explored. Additionally, we analyzed the association of COL5A2 expression with immunological checkpoint molecules. Furthermore, the relationship between COL5A2 expression and immunotherapy sensitivity was also investigated.

RESULTS

COL5A2 expression was elevated in GC. More than this, the scRNA-seq analysis revealed that COL5A2 expression had a spatial gradient. The upregulated COL5A2 was associated with worse overall survival. A significant correlation was found between COL5A2 overexpression and age, T classification and clinical stage in GC. COL5A2 was found to be an independent factor for the unfortunate outcome in Cox regression analysis. The co-expressed genes of COL5A2 were associated with tumor stage or poor survival. Enrichment analysis revealed that the DEGs were mainly associated with extracellular matrix (ECM)-related processes, PI3K-AKT signaling pathway, and focal adhesion. GSEA analyses revealed that COL5A2 was associated with tumor progression-related pathways. Meanwhile, COL5A2 expression was correlated with tumor-infiltrating immune cells. Moreover, immunophenoscore (IPS) analysis and PRJEB25780 cohorts showed that patients with low COL5A2 expression were highly sensitive to immunotherapy.

CONCLUSIONS

COL5A2 might act as a prognostic biomarker of GC prognosis and immune infiltration and may provide a therapeutic intervention strategy.

NGS Data Repurposing Allows Detection of tRNA Fragments as Gastric Cancer Biomarkers in Patient-Derived Extracellular Vesicles

Transfer RNA fragments (tRFs) have gene silencing effects similarly to miRNAs, can be sorted into extracellular vesicles (EVs) and are emerging as potential circulating biomarkers for cancer diagnoses. We aimed at analyzing the expression of tRFs in gastric cancer (GC) and understanding their potential as biomarkers. We explored miRNA datasets from gastric tumors and normal adjacent tissues (NATs) from TCGA repository, as well as proprietary 3D-cultured GC cell lines and corresponding EVs, in order to identify differentially represented tRFs using MINTmap and R/Bioconductor packages. Selected tRFs were validated in patient-derived EVs. We found 613 Differentially Expressed (DE)-tRFs in the TCGA dataset, of which 19 were concomitantly upregulated in TCGA gastric tumors and present in 3D cells and EVs, but barely expressed in NATs. Moreover, 20 tRFs were expressed in 3D cells and EVs and downregulated in TCGA gastric tumors. Of these 39 DE-tRFs, 9 tRFs were also detected in patient-derived EVs. Interestingly, the targets of these 9 tRFs affect neutrophil activation and degranulation, cadherin binding, focal adhesion and the cell-substrate junction, highlighting these pathways as major targets of EV-mediated crosstalk with the tumor microenvironment. Furthermore, as they are present in four distinct GC datasets and can be detected even in low quality patient-derived EV samples, they hold promise as GC biomarkers. By repurposing already available NGS data, we could identify and cross-validate a set of tRFs holding potential as GC diagnosis biomarkers.

ECM2, a prognostic biomarker for lower grade glioma, serves as a potential novel target for immunotherapy

Extracellular matrix protein 2 (ECM2), which regulates cell proliferation and differentiation, has recently been reported as a prognostic indicator for multiple cancers, but its value in lower grade glioma (LGG) remains unknown. In this study, LGG transcriptomic data of 503 cases in The Cancer Genome Atlas (TCGA) database and 403 cases in The Chinese Glioma Genome Atlas (CGGA) database were collected to analyze ECM2 expression patterns and the relationship with clinical characteristics, prognosis, enriched signaling pathways, and immune-related markers. In addition, a total of 12 laboratory samples were used for experimental validation. Wilcoxon or Kruskal-Wallis tests demonstrated highly expressed ECM2 in LGG was positively associated with malignant histological features and molecular features such as recurrent LGG and isocitrate dehydrogenase (IDH) wild-type. Also, Kaplan-Meier (KM) curves proved high ECM2 expression could predict shorter overall survival in LGG patients, as multivariate analysis and meta-analysis claimed ECM2 was a deleterious factor for LGG prognosis. In addition, the enrichment of immune-related pathways for ECM2, for instance JAK-STAT pathway, was obtained by Gene Set Enrichment Analysis (GSEA) analysis. Furthermore, positive relationships between ECM2 expression with immune cells infiltration and cancer-associated fibroblasts (CAFs), iconic markers (CD163), and immune checkpoints (CD274, encoding PD-L1) were proved by Pearson correlation analysis. Finally, laboratory experiments of RT-qPCR and immunohistochemistry showed high expression of ECM2, as well as CD163 and PD-L1 in LGG samples. This study identifies ECM2, for the first time, as a subtype marker and prognostic indicator for LGG. ECM2 could also provide a reliable guarantee for further personalized therapy, synergizing with tumor immunity, to break through the current limitations and thus reinvigorating immunotherapy for LGG. AVAILABILITY OF DATA AND MATERIALS: Raw data from all public databases involved in this study are stored in the online repository (chengMD2022/ECM2 (github.com)).

A telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer

BACKGROUND

Telomeres are involved in the development and progression of gastric cancer (GC). However, the association of telomerase regulation-related lncRNAs with prognosis and immunotherapy responsiveness in gastric cancer is unclear.

METHODS

This study systematically evaluated the relationship between lncRNAs co-expressed with 67 telomerase regulatory genes and gastric cancer prognosis. The risk scores of the samples were calculated based on telomerase regulation-related lncRNAs with prognostic value, and the samples were classified into high-/low-risk groups. The prognostic value of risk groups was then evaluated, a GC prognostic prediction model based on risk groups and clinical characteristics was established, and the prediction accuracy of the model was clarified by receiving operating characteristic (ROC) curves and calibration curves. Finally, the value of risk grouping in GC immunotherapy sensitivity was predicted by comparing MSI status and tumor mutation load between the high- and low-risk groups.

RESULTS

We identified 13 lncRNAs with prognostic value co-expressed with telomerase regulatory genes and observed that the prognosis of the low-risk group was significantly better than that of the high-risk group. Meanwhile, a GC overall survival (OS) prediction model based on risk grouping and clinical characteristics was developed, and ROC curves and calibration curves confirmed the good predictive ability of the model. In addition, the low-risk group exhibited a higher tumor mutation load and MSI-H, suggesting a possible benefit of immunotherapy.

CONCLUSION

We found that telomerase regulation-related lncRNAs have prognostic value in GC patients and contribute to the exploration of more effective immunotherapeutic strategies.

Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma

Background

Glycolysis and cholesterol synthesis are crucial in cancer metabolic reprogramming. The aim of this study was to identify a glycolysis and cholesterol synthesis-related genes (GCSRGs) signature for effective prognostic assessments of osteosarcoma patients.

Methods

Gene expression data and clinical information were obtained from GSE21257 and TARGET-OS datasets. Consistent clustering method was used to identify the GCSRGs-related subtypes. Univariate Cox regression and LASSO Cox regression analyses were used to construct the GCSRGs signature. The ssGSEA method was used to analyze the differences in immune cells infiltration. The pRRophetic R package was utilized to assess the drug sensitivity of different groups. Western blotting, cell viability assay, scratch assay and Transwell assay were used to perform cytological validation.

Results

Through bioinformatics analysis, patients diagnosed with osteosarcoma were classified into one of 4 subtypes (quiescent, glycolysis, cholesterol, and mixed subtypes), which differed significantly in terms of prognosis and tumor microenvironment. Weighted gene co-expression network analysis revealed that the modules strongly correlated with glycolysis and cholesterol synthesis were the midnight blue and the yellow modules, respectively. Both univariate and LASSO Cox regression analyses were conducted on screened module genes to identify 5 GCSRGs (RPS28, MCAM, EN1, TRAM2, and VEGFA) constituting a prognostic signature for osteosarcoma patients. The signature was an effective prognostic predictor, independent of clinical characteristics, as verified further via Kaplan-Meier analysis, ROC curve analysis, univariate and multivariate Cox regression analysis. Additionally, GCSRGs signature had strong correlation with drug sensitivity, immune checkpoints and immune cells infiltration. In cytological experiments, we selected TRAM2 as a representative gene to validate the validity of GCSRGs signature, which found that TRAM2 promoted the progression of osteosarcoma cells. Finally, at the pan-cancer level, TRAM2 had been correlated with overall survival, progression free survival, disease specific survival, tumor mutational burden, microsatellite instability, immune checkpoints and immune cells infiltration.

Conclusion

Therefore, we constructed a GCSRGs signature that efficiently predicted osteosarcoma patient prognosis and guided therapy.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Identification of circRNA Biomarker for Gastric Cancer through Integrated Analysis

Gastric cancer (GC) is one of the most common malignant tumors and ranks third in cancer mortality globally. Although, a lot of advancements have been made in diagnosis and treatment of gastric cancer, there is still lack of ideal biomarker for the diagnosis and treatment of gastric cancer. Due to the poor prognosis, the survival rate is not improved much. Circular RNAs (circRNAs) are single-stranded RNAs with a covalently closed loop structure that don't have the 5'-3' polarity and a 3' polyA tail. Because of their circular structure, circRNAs are more stable than linear RNAs. Previous studies have found that circRNAs are involved in several biological processes like cell cycle, proliferation, apoptosis, autophagy, migration and invasion in different cancers, and participate in some molecular mechanisms including sponging microRNAs (miRNAs), protein translation and binding to RNA-binding proteins. Several studies have reported that circRNAs play crucial role in the occurrence and development of different types of cancers. Although, some studies have reported several circRNAs in gastric cancer, more studies are needed in searching new biomarkers for gastric cancer diagnosis and treatment. Here, we investigated potential circRNA biomarkers for GC using next-generation sequencing (NGS) data collected from 5 paired GC samples. A total of 45,783 circRNAs were identified in all samples and among them 478 were differentially expressed (DE). The gene ontology (GO) analysis of the host genes of the DE circRNAs showed that some genes were enriched in several important biological processes, molecular functions and cellular components. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that some host genes were enriched in several GC related pathways. The circRNA-miRNA-gene interaction network analysis showed that two circRNAs circCEACAM5 and circCOL1A1 were interacted with gastric cancer related miRNAs, and their host genes were also the important therapeutic and prognostic biomarkers for GC. The experimental results also validated that these two circRNAs were DE in GC compared to adjacent normal tissues. Overall, our findings suggest that these two circRNAs circCEACAM5 and circCOL1A1 might be the potential biomarkers for the diagnosis and treatment of GC.