Whole Genome Messenger RNA Profiling Identifies a Novel Signature to Predict Gastric Cancer Survival

Identification of gastric cancer subtypes based on disulfidptosis-related genes: GPC3 as a novel biomarker for prognosis prediction

Gastric cancer (GC) is the fourth most common cancer type. "Disulfidptosis," a distinct form of cell death, is initiated through aberrant intracellular disulfide metabolism. Here, we identified various GC subtypes based on disulfidptosis-related genes (DRGs) and constructed a risk score model to identify relevant genes to help predict patient prognosis and guide treatment. We downloaded RNA sequencing (RNA-seq) data from the TCGA-STAD database, performed a difference analysis, and combined the data with GSE84437 to successfully perform an unsupervised clustering analysis based on DRGs and differentially expressed genes (DEGs). Risk-scoring models were established by screening prognosis-related DEGs. The GC samples were segregated into high-risk (HR) and low-risk (LR) groups according to their risk scores. We then evaluated the genes screened with the model in terms of prognosis, tumor, and immune cell infiltration. The response of patients with GC to immunological therapy was assessed using tumor mutational burden, microsatellite instability, and tumor immune dysfunction and exclusion scores. Using unsupervised cluster analysis, we identified two DRG clusters and two gene clusters that differed in prognosis and tumor microenvironment. A six-gene model was developed for risk score assessment. The LR group demonstrated superior performance compared to the HR group in terms of immunity, exhibiting greater sensitivity to immunotherapy. Thereafter, we selected the model gene GPC3 for single-gene analysis and verified it by experimental validation. The results demonstrated that GPC3 can serve as a standalone biomarker with promising clinical applicability in the prognostic prediction and clinical management of GC.

Spatial transcriptomics reveal tumor microenvironment and SLCO2A1 correlated with tumor suppression in hypopharyngeal squamous cell carcinoma

BACKGROUND

Hypopharyngeal squamous cell carcinoma (HSCC) is a type of head and neck tumor with malignant behavior and poor prognosis. Spatial transcriptomics is a method that spatially analyzes gene expression patterns in tissues and has been used to discover tumor microenvironment and molecular markers in various tumors. However, there are no published reports on spatial transcriptomic analysis of HSCC.

METHODS

In this study, spatial transcriptomic analysis was performed on tumor tissues in situ, peritumoral tissues, and lymphatic metastatic tissues of four patients with HSCC. Morphological markers, including panCK, SMA, and CD45, were used to identify epithelial, fibroblast, and immune cells, respectively. By analyzing the expression of more than 18, 000 genes within the transcriptome of all ROIs, differentially expressed genes of three cell types in different tissues were identified, and differentially expressed signaling pathways and immune infiltration were analyzed.

RESULTS

The spatial distribution of cells suggests that fibroblast cells in tumor tissues may be involved in the genesis and development of tumors, and the immune infiltration of lymphatic tumor metastasis is lower than that of tumors in situ. For epithelial cells, SLCO2A1, which is a favorable prognosis marker in head and neck squamous cell carcinoma (HNSCC), was significantly down-regulated in tumor tissues and lymphatic metastatic tissues compared with adjacent normal tissues. For immune cells, KANK3, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with adjacent normal tissues. For fibroblast cells, AQP1, CLEC3B and SLCO2A1, which are favorable prognosis markers in HNSCC, were significantly down-regulated in tumor tissues compared with adjacent normal tissues. ITGA8, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with normal lymphatic tissues. CSRP1, DES, and SLCO2A1 positively correlate with immune infiltration in HNSCC. Moreover, SLCO2A1 overexpression suppressed Fadu cells proliferation and metastasis and significantly correlated with favorable survival overcome in HSCC.

CONCLUSIONS

We investigated tumor and fibroblast heterogeneity, as well as the immune microenvironment in HSCC by using spatial transcriptomics. SLCO2A1 may be a tumor suppressor gene and correlates with immune infiltration for HSCC and could serve as a potential target for its diagnosis and treatment.

Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East

Background: Colorectal cancer (CRC) displays a complex pattern of inheritance. It is postulated that much of the missing heritability of CRC is enriched in high-impact rare alleles, which might play a crucial role in the etiology and susceptibility of CRC. Methods: In this study, an exome-wide association analysis was performed in 146 patients with high-risk CRC in the Middle East and 1395 healthy controls. The aim was to identify rare germline variants in coding regions and their splicing sites associated with high-risk CRC in the Middle Eastern population. Results: Rare inactivating variants (RIVs) in APC had the strongest association with high-risk CRC (6/146 in cases vs. 1/1395 in controls, OR = 59.7, p = 5.13 × 10-12), whereas RIVs in RIMS1, an RAS superfamily member, were significantly associated with high-risk CRC (5/146 case vs. 2/1395 controls, OR = 24.7, p = 2.03 × 10-8). Rare damaging variants in 17 genes were associated with high-risk CRC at the exome-wide threshold (p < 2.5 × 10-6). Based on the sequence kernel association test, nonsynonymous variants in six genes (TNXB, TAP2, GPSM3, ADGRG4, TMEM229A, and ANKRD33B) had a significant association with high-risk CRC. RIVs in APC-the most common high-penetrance genetic factor-were associated with patients with high-risk CRC in the Middle East. Individuals who inherited APC RIVs had an approximate 60-fold increased risk of developing CRC and were likely to develop the disease earlier. Conclusions: We identified new potential CRC predisposition variants in other genes that could play a role in CRC inheritance. However, large collaborative studies are needed to confirm the association of these variants with high-risk CRC. These results provide information for counseling patients with high-risk CRC and their families in our population.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

FLRT2 suppresses bladder cancer progression through inducing ferroptosis

Bladder cancer is a common tumour worldwide and exhibits a poor prognosis. Fibronectin leucine rich transmembrane protein 2 (FLRT2) is associated with the regulation of multiple tumours; however, its function in human bladder cancer remain unclear. Herein, we found that FLRT2 level was reduced in human bladder cancer and that higher FLRT2 level predicted lower survival rate. FLRT2 overexpression inhibited, while FLRT2 silence facilitated tumour cell growth, migration and invasion. Mechanistic studies revealed that FLRT2 elevated acyl-CoA synthetase long-chain family member 4 (ACSL4) expression, increased lipid peroxidation and subsequently facilitated ferroptosis of human bladder cancer cells. In summary, we demonstrate that FLRT2 elevates ACSL4 expression to facilitate lipid peroxidation and subsequently triggers ferroptosis, thereby inhibiting the malignant phenotype of human bladder cancer cells. Overall, we identify FLRT2 as a tumour suppressor gene.

DCLK1 and its oncogenic functions: A promising therapeutic target for cancers

Doublecortin-like kinase 1 (DCLK1), a significant constituent of the protein kinase superfamily and the doublecortin family, has been recognized as a prooncogenic factor that exhibits a strong association with the malignant progression and clinical prognosis of various cancers. DCLK1 serves as a stem cell marker that governs tumorigenesis, tumor cell reprogramming, and epithelial-mesenchymal transition. Multiple studies have indicated the capable of DCLK1 in regulating the DNA damage response and facilitating DNA damage repair. Additionally, DCLK1 is involved in the regulation of the immune microenvironment and the promotion of tumor immune evasion. Recently, DCLK1 has emerged as a promising therapeutic target for a multitude of cancers. Several small-molecule inhibitors of DCLK1 have been identified. Nevertheless, the biological roles of DCLK1 are mainly ambiguous, particularly with the disparities between its α- and β-form transcripts in the malignant progression of cancers, which impedes the development of more precisely targeted drugs. This article focuses on tumor stem cells, tumor epithelial-mesenchymal transition, the DNA damage response, and the tumor microenvironment to provide a comprehensive overview of the association between DCLK1 and tumor malignant progression, address unsolved questions and current challenges, and project future directions for targeting DCLK1 for the diagnosis and treatment of cancers.

KRT13 and UPK1B for differential diagnosis between metastatic lung carcinoma from oral squamous cell carcinoma and lung squamous cell carcinoma

Oral squamous cell carcinomas unusually show distant metastasis to the lung after primary treatment, which can be difficult to differentiate from primary squamous cell carcinoma of the lung. While the location and number of tumor nodules is helpful in diagnosing cases, differential diagnosis may be difficult even with histopathological examination. Therefore, we attempted to identify molecules that can facilitate accurate differential diagnosis. First, we performed a comprehensive gene expression analysis using microarray data for OSCC-LM and LSCC, and searched for genes showing significantly different expression levels. We then identified KRT13, UPK1B, and nuclear receptor subfamily 0, group B, member 1 (NR0B1) as genes that were significantly upregulated in LSCC and quantified the expression levels of these genes by real-time quantitative RT-PCR. The expression of KRT13 and UPK1B proteins were then examined by immunohistochemical staining. While OSCC-LM showed no KRT13 and UPK1B expression, some tumor cells of LSCC showed KRT13 and UPK1B expression in 10 of 12 cases (83.3%). All LSCC cases were positive for at least one of these markers. Thus, KRT13 and UPK1B might contribute in differentiating OSCC-LM from LSCC.

MiR-122-5p as a potential regulator of pulmonary vascular wall cell in idiopathic pulmonary arterial hypertension

MicroRNAs (miRNAs) are versatile regulators of pulmonary arterial remodeling in idiopathic pulmonary arterial hypertension (IPAH). We herein aimed to characterize miRNAs in peripheral blood mononuclear cell (PBMC) and plasma exosomes, and investigate specific miRNA expression in pulmonary artery cells and lung tissues in IPAH. A co-dysregulated miRNA was identified from the miRNA expression profiles of PBMC and plasma exosomes in IPAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed the potential function of differentially expressed miRNAs. Real-time quantitative reverse transcription polymerase chain reaction was used to validate the expression of specific miRNAs in hypoxia-induced pulmonary microvascular endothelial cells (PMECs), pulmonary artery smooth muscle cells (PASMCs), pericyte cells (PCs), and lung tissues of patients with IPAH and rats. Finally, the miRNA-mRNA mechanisms of miR-122-5p were predicted. MiR-122-5p was the only co-upregulated miRNA in PBMC and plasma exosomes in patients with IPAH. Functional analysis of differentially expressed miRNAs revealed associations with the GO terms "transcription, DNA-templated," "cytoplasm," and "metal ion binding" in both PBMC and plasma exosomes, KEGG pathway MAPK signaling in PBMC, and KEGG-pathway human papillomavirus infection in plasma exosomes. Hypoxic PMECs and PCs, lung tissue of patients with IPAH, and rats showed increased expression of miR-122-5p, but hypoxic PASMCs showed decreased expression. And miR-122-5p mimics and inhibitor affected cell proliferation. Finally, miR-122-5p was found to potentially target DLAT (in lung tissue) and RIMS1 (in PMECs) in IPAH. According to the dual-luciferase assay, miR-122-5p bound to DLAT or RIMS1. In studies, DLAT imbalance was associated with cell proliferation and migration, RIMS1 is differentially expressed in cancer and correlated with cancer prognosis. Our findings suggest that the miR-122-5p is involved in various biological functions in the adjacent vascular wall cells in IPAH.

Prognostic Values of Core Genes in Pilocytic Astrocytom

BACKGROUND

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis.

METHODS

The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves.

RESULTS

Compared with normal brain tissues (n = 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n = 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio  =  2.8, P  <  0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio =  3.2, P  <  0.01).

CONCLUSIONS

This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Aberrant methylation in neurofunctional gene serves as a hallmark of tumorigenesis and progression in colorectal cancer

BACKGROUND

DNA methylation is one of the most promising biomarkers in predicting the prognosis of colorectal cancer (CRC). We aimed to develop a DNA methylation biomarker that could evaluate the prognosis of CRC.

METHODS

A promising DNA methylation biomarker was developed by hypermethylated genes in cancer tissue that were identified from Illumina EPIC methylation arrays. A cohort comprising 30 pairs of snap-frozen tumor tissue and adjacent normal tissue was used for correlation analysis between the methylation and expression status of the marker. The other cohort comprising 254 formalin-fixed paraffin-embedded (FFPE) tumor tissue from 254 CRC patients was used for prognosis analysis.

RESULTS

Regulating synaptic membrane exocytosis 2 (RIMS2) was hypermethylated and lowly expressed in CRC comparing to adjacent normal tissue. Hypermethylation of RIMS2 in CRC was correlated with less frequent KRAS mutant and high differentiation. RIMS2 promoter methylation showed independent predictive value for survival outcome (P = 0.015, HR 1.992, 95% CI [(1.140-3.48)]), and a combination of RIMS2 methylation with KRAS status could predict prognosis better.

CONCLUSIONS

RIMS2 is frequently hypermethylated in CRC, which can silence the expression of RIMS2. RIMS2 methylation is a novel biomarker for predicting the prognosis of CRC.

CD109 on Dendritic Cells Regulates Airway Hyperreactivity and Eosinophilic Airway Inflammation

Asthma is a chronic airway inflammatory disease characterized by airway hyperreactivity (AHR) and eosinophilic airway inflammation. Dendritic cells (DCs) are essential for the development of asthma via presenting allergens, causing T-helper cell type 2 (Th2) skewing and eosinophil inflammation. Recent studies have revealed that CD109, a glycosylphosphatidylinositol-anchored glycoprotein, is involved in the pathogenesis of inflammatory diseases such as rheumatoid arthritis and psoriasis. However, no study has addressed the role of CD109 in asthma. This study sought to address the role of CD109 on DCs in the development of AHR and allergic inflammation. CD109-deficient mice (CD109^-/-) were sensitized with house dust mite or ovalbumin and compared with wild-type mice for induction of AHR and allergic inflammation. CD109-deficient mice had reduced AHR and eosinophilic inflammation together with lower Th2 cytokine expression compared with wild-type mice. Interestingly, CD109 expression was induced in lung conventional DC2s (cDC2s), but not lung cDC1s, upon allergic challenge. Lung cDC2s from CD109^-/- mice had a poor ability to induce cytokine production in ex vivo DC-T cell cocultures with high expression of RUNX3 (runt-related transcription factor 3), resulting in suppression of Th2 differentiation. Adoptive transfer of bone marrow-derived CD109^-/- DCs loaded with house dust mite failed to develop AHR and eosinophilic inflammation. Finally, administration of monoclonal anti-CD109 antibody reduced airway eosinophils and significantly decreased AHR. Our results suggest the involvement of CD109 in asthma pathogenesis. CD109 is a novel therapeutic target for asthma.

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.

METHODS

The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.

RESULTS

22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).

CONCLUSION

Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

Key Molecules of Fatty Acid Metabolism in Gastric Cancer

Fatty acid metabolism is closely linked to the progression of gastric cancer (GC), a very aggressive and life-threatening tumor. This study examines linked molecules, such as Sterol Regulatory Element-Binding Protein 1 (SREBP1), ATP Citrate Lyase (ACLY), Acetyl-CoA Synthases (ACSs), Acetyl-CoA Carboxylase (ACC), Fatty Acid Synthase (FASN), Stearoyl-CoA Desaturase 1 (SCD1), CD36, Fatty Acid Binding Proteins (FABPs), and Carnitine palmitoyltransferase 1 (CPT1), as well as their latest studies and findings in gastric cancer to unveil its core mechanism. The major enzymes of fatty acid de novo synthesis are ACLY, ACSs, ACC, FASN, and SCD1, while SREBP1 is the upstream molecule of fatty acid anabolism. Fatty acid absorption is mediated by CD36 and FABPs, and fatty acid catabolism is mediated by CPT1. If at all possible, we will discover novel links between fatty acid metabolism and a prospective gastric cancer target.

Identification of a seven-cell cycle signature predicting overall survival for gastric cancer

While genetic alterations in several regulators of the cell cycle have a significant impact on the gastric carcinogenesis process, the prognostic role of them remains to be further elucidated. The TCGA-STAD training set were downloaded and the mRNA expression matrix of cell cycle genes was extracted and corrected for further analysis after taking the intersection with GSE84437 dataset. Differentially expressed mRNAs were identified between tumor and normal tissue samples in TCGA-STAD. Univariate Cox regression analysis and lasso Cox regression model established a novel seven-gene cell cycle signature (including GADD45B, TFDP1, CDC6, CDC25A, CDC7, SMC1A and MCM3) for GC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. The signature was further validated in the GSE84437 dataset. In tissue microarray, CDC6 and MCM3 protein expression were significant differences by the immunohistochemistry-based H-score between tumor tissues and adjacent tissues, and CDC6 is an independent prognostic factor for GC. Interestingly, our GSEA revealed that low-risk patients were more related to cell cycle pathways and might benefit more from therapies targeting cell cycle. Our study identified a novel robust seven-gene cell cycle signature for GC prognosis prediction that may serve as a beneficial complement to clinicopathological staging. The signature might provide potential biomarkers for the application of cell cycle regulators to therapies and treatment response prediction.

Large-scale human tissue analysis identifies Uroplakin 1b as a putative diagnostic marker in surgical pathology

Uroplakin 1B (Upk1b) stabilizes epithelial cells lining the bladder lumen to prevent rupturing during bladder distension. Little is known about Upk1b expression in other normal and malignant tissues. To comprehensively evaluate the potential diagnostic and prognostic utility of Upk1b expression analysis, a tissue microarray containing 14,061 samples from 127 different tumor types and subtypes and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. Upk1b immunostaining was found in 61 (48%) different tumor types including 50 (39%) with at least one moderately positive and 39 tumor types (31%) with at least one strongly positive tumor. Highest positivity rates were found in urothelial neoplasms (58-95%), Brenner tumors of the ovary (92%), epitheloid mesothelioma (87%), serous carcinoma of the ovary (58%) and the endometrium (53%) as well as in squamous cell carcinoma of the head and neck (18-37%), lung (39%) and esophagus (26%). In urothelial carcinoma, low Upk1b expression was linked to high grade and invasive tumor growth (p<0.0001 each) and nodal metastasis (p=0.0006). Our data suggest diagnostic applications of Upk1b immunohistochemistry in panels for the distinction of malignant mesothelioma from adenocarcinoma of the lung, urothelial carcinoma from prostatic adenocarcinoma in the bladder, or pancreatico-biliary and gastro-esophageal from colorectal adenocarcinoma.

DNA Repair and Replication-Related Gene Signature Based on Tumor Mutation Burden Reveals Prognostic and Immunotherapy Response in Gastric Cancer

The genomic variant features (mutations, deletions, structural variants, etc.) within gastric cancer impact its evolution and immunogenicity. The tumor has developed several coping strategies to respond to these changes by DNA repair and replication (DRR). However, the intrinsic relationship between the associated DRR-related genes and gastric cancer progression remained unknown. This study selected DRR-related genes with tumor mutation burden based on the TCGA (The Cancer Genome Atlas) database of gastric cancer transcriptome and mutation data. The prognosis model of seven genes (LAMA2, CREB3L3, SELP, ABCC9, CYP1B1, CDH2, and GAMT) was constructed by a univariate and LASSO regression analysis and divided into high-risk and low-risk groups with the median risk score. Survival analysis showed that overall survival (OS) was lower in the high-risk group than that in the low-risk group. Moreover, patients with gastric cancer in the high-risk group have worse survival in different subgroups, including age, gender, histological grade, and TNM stage. The nomogram that included risk scores for DRR-related genes could accurately foresee OS of patients with gastric cancer. Interestingly, the tumor mutation burden score was higher in the low-risk group than that in the high-risk group, and the risk score for DRR-related genes was negatively correlated with tumor mutation burden in gastric cancer. Next, we further combined the risk score and tumor mutation burden to evaluate the prognosis of gastric cancer patients. The low-risk cohort had a better prognosis than the high-risk cohort in the high tumor mutation burden subgroup. The number of mutation types in the high-risk group was lower than that in the low-risk group. In the immune microenvironment of gastric cancer, more naïve B cells, memory resting CD4+ T cells, Treg cells, monocytes cells, and resting mast cells were infiltrated in the high-risk group. At last, PD-L1 and IAP expressions were negatively correlated with the risk scores; patients with gastric cancer in the low-risk group showed better immunotherapy outcomes than those in the high-risk group. Overall, the DRR-related gene signature based on tumor mutation burden is a novel biomarker for prognostic and immunotherapy response in patients with gastric cancer.

Combining Algorithms to Find Signatures That Predict Risk in Early-Stage Stomach Cancer

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D-basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm looks for metagenes, which are sets of genes that are either overexpressed or underexpressed in the same patients. Whereas LUST runs unsupervised by clinical data, the D-basis algorithm uses implications and association rules to relate gene expression to clinical outcomes. The D-basis selects a small subset of the metagene (a signature) to predict survival. The two algorithms, LUST and D-basis, were combined and applied to mRNA expression and clinical data from The Cancer Genome Atlas (TCGA) for 203 stage 1 and 2 stomach cancer patients. Two small (four-gene) signatures effectively predict survival in early-stage stomach cancer patients. These signatures could be used as a guide for treatment. The first signature (DU4) consists of genes that are underexpressed on the long-survival/low-risk group: FLRT2, KCNB1, MYOC, and TNXB. The second signature consists of genes that are overexpressed on the short-survival/high-risk group: ASB5, SFRP1, SMYD1, and TACR2. Another nine-gene signature (REC9) predicts recurrence: BNC2, CCDC8, DPYSL3, MOXD1, MXRA8, PRELP, SCARF2, TAGLN, and ZNF423. Each patient is assigned a score that is a linear combination of the expression levels for the genes in the signature. Scores below a selected threshold predict low-risk/long survival, whereas high scores indicate a high risk of short survival. The metagenes associate with TCGA cluster C1. Both our signatures and cluster C1 identify tumors that are genomically silent, and have a low mutation load or mutation count. Furthermore, our signatures identify tumors that are predominantly in the WHO classification of poorly cohesive and the Lauren class of diffuse samples, which have a poor prognosis.

Revisiting social MPE: an integration of molecular pathological epidemiology and social science in the new era of precision medicine

INTRODUCTION

Molecular pathological epidemiology (MPE) is an integrative transdisciplinary area examining the relationships between various exposures and pathogenic signatures of diseases. In line with the accelerating advancements in MPE, social science and its health-related interdisciplinary areas have also developed rapidly. Accumulating evidence indicates the pathological role of social-demographic factors. We therefore initially proposed social MPE in 2015, which aims to elucidate etiological roles of social-demographic factors and address health inequalities globally. With the ubiquity of molecular diagnosis, there are ample opportunities for researchers to utilize and develop the social MPE framework.

AREAS COVERED

Molecular subtypes of breast cancer have been investigated rigorously for understanding its etiologies rooted from social factors. Emerging evidence indicates pathogenic heterogeneity of neurological disorders such as Alzheimer's disease. Presenting specific patterns of social-demographic factors across different molecular subtypes should be promising for advancing the screening, prevention, and treatment strategies of those heterogeneous diseases. This article rigorously reviewed literatures investigating differences of race/ethnicity and socioeconomic status across molecular subtypes of breast cancer and Alzheimer's disease to date.

EXPERT OPINION

With advancements of the multi-omics technologies, we foresee a blooming of social MPE studies, which can address health disparities, advance personalized molecular medicine, and enhance public health.

Tetraspanins: Novel Molecular Regulators of Gastric Cancer

Gastric cancer is the fourth and fifth most common cancer worldwide in men and women, respectively. However, patients with an advanced stage of gastric cancer still have a poor prognosis and low overall survival rate. The tetraspanins belong to a protein superfamily with four hydrophobic transmembrane domains and 33 mammalian tetraspanins are ubiquitously distributed in various cells and tissues. They interact with other membrane proteins to form tetraspanin-enriched microdomains and serve a variety of functions including cell adhesion, invasion, motility, cell fusion, virus infection, and signal transduction. In this review, we summarize multiple utilities of tetraspanins in the progression of gastric cancer and the underlying molecular mechanisms. In general, the expression of TSPAN8, CD151, TSPAN1, and TSPAN4 is increased in gastric cancer tissues and enhance the proliferation and invasion of gastric cancer cells, while CD81, CD82, TSPAN5, TSPAN9, and TSPAN21 are downregulated and suppress gastric cancer cell growth. In terms of cell motility regulation, CD9, CD63 and CD82 are metastasis suppressors and the expression level is inversely associated with lymph node metastasis. We also review the clinicopathological significance of tetraspanins in gastric cancer including therapeutic targets, the development of drug resistance and prognosis prediction. Finally, we discuss the potential clinical value and current limitations of tetraspanins in gastric cancer treatments, and provide some guidance for future research.

DNA methylation signatures associated with prognosis of gastric cancer

Background

Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.

Methods

Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.

Results

We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed.

Conclusions

We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08389-0.

SLC6A3 as a potential circulating biomarker for gastric cancer detection and progression monitoring

BACKGROUND

Gastric cancer is a malignant tumor originating from the gastric mucosal epithelium, with no obvious symptoms at the early stage. The dopamine transporter gene (SLC6A3) is involved in the metabolism of dopamine and catecholamine and is a potential gene for Parkinson's disease and alcoholism. But the role of SLC6A3 in gastric cancer is still unknown. The aim of our study is to investigate the potential diagnostic value of SLC6A3 on gastric cancer.

METHODS

Quantitative real-time PCR (RT-qPCR) was used to detect the expression of SLC6A3 in clinical samples and cells. A total of 246 samples were enrolled in this study (26 pairs of tissue samples; Serum of 113 patients with gastric cancer, 51 polyps patients and 56 healthy controls). The diagnostic value of SLC6A3 was evaluated by the ROC curve and analyzed the changes of SLC6A3 expression before and after surgery. The prognostic value, interacting proteins and related pathways of SLC6A3 were evaluated by TCGA analysis in UALCAN database (http://ualcan.path.uab.edu/).

RESULTS

The expression level of SLC6A3 in gastric cancer was significantly higher than that in controls. Further, the proportion under the ROC curve (AUC) for SLC6A3, CEA and CA19-9 was 0.818 (95 % confidence interval [CI]: 0.754 to 0.883, P < 0.001), and the expression level of SLC6A3 in the serum of patients with gastric cancer decreased significantly after surgery (P < 0.001). Bioinformatic enrichment analysis of SLC6A3 displayed the relevant metabolic pathways involved in its interacting proteins.

CONCLUSION

SLC6A3 is involved in the occurrence and development of gastric cancer and can be used as a potential diagnostic indicator for gastric cancer.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.

A universal molecular prognostic score for gastrointestinal tumors

Colorectal and gastric cancers are a leading cause of cancer deaths in developed countries. Precise estimation of prognosis is important with regard to clinical decision making for individuals with such cancers. We here comprehensively compiled a complete atlas of prognostic genes based on an integrated meta-analysis of one of the largest assembled colorectal cancer cohorts. A simple yet robust machine learning approach was then applied to establish a universal molecular prognostic score (mPS_colon) that relies on the expression status of only 16 genes and which was validated with independent data sets. This score was found to be an independent prognostic indicator in multivariate models including cancer stage, to be valid independent of tumor characteristics or patient ethnicity, and to be also applicable to gastric cancer. We conclude that mPS_colon is a universal prognostic classifier for patients with gastrointestinal cancers and that it should prove informative for optimization of personalized therapy for such patients.

Recent advances in studies of SLCO2A1 as a key regulator of the delivery of prostaglandins to their sites of action

Solute carrier organic anion transporter family member 2A1 (SLCO2A1, also known as PGT, OATP2A1, PHOAR2, or SLC21A2) is a plasma membrane transporter consisting of 12 transmembrane domains. It is ubiquitously expressed in tissues, and mediates the membrane transport of prostaglandins (PGs, mainly PGE₂, PGF_2α, PGD₂) and thromboxanes (e.g., TxB₂). SLCO2A1-mediated transport is electrogenic and is facilitated by an outwardly directed gradient of lactate. PGs imported by SLCO2A1 are rapidly oxidized by cytoplasmic 15-hydroxyprostaglandin dehydrogenase (15-PGDH, encoded by HPGD). Accumulated evidence suggests that SLCO2A1 plays critical roles in many physiological processes in mammals, and it is considered a potential pharmacological target for diabetic foot ulcer treatment, antipyresis, and non-hormonal contraception. Furthermore, whole-exome analyses suggest that recessive inheritance of SLCO2A1 mutations is associated with two refractory diseases, primary hypertrophic osteoarthropathy (PHO) and chronic enteropathy associated with SLCO2A1 (CEAS). Intriguingly, SLCO2A1 is also a key component of the Maxi-Cl channel, which regulates fluxes of inorganic and organic anions, including ATP. Further study of the bimodal function of SLCO2A1 as a transporter and ion channel is expected to throw new light on the complex pathology of human diseases. Here, we review and summarize recent information on the molecular functions of SLCO2A1, and we discuss its pathophysiological significance.

Analysis of retrotransposon subfamily DNA methylation reveals novel early epigenetic changes in chronic lymphocytic leukemia

Retrotransposons such as LINE-1 and Alu comprise >25% of the human genome. While global hypomethylation of these elements has been widely reported in solid tumours, their epigenetic dysregulation is yet to be characterised in chronic lymphocytic leukemia (CLL), and there has been scant consideration of their evolutionary history that mediates sensitivity to hypomethylation. Here, we developed an approach for locus- and evolutionary subfamily-specific analysis of retrotransposons using the Illumina Infinium Human Methylation 450K microarray platform, which we applied to publicly-available datasets from CLL and other haematological malignancies. We identified 9,797 microarray probes mapping to 117 LINE-1 subfamilies and 13,130 mapping to 37 Alu subfamilies. Of these, 10,782 were differentially methylated (PFDR<0.05) in CLL patients (n=139) compared with healthy individuals (n=14), with enrichment at enhancers (P=0.002). Differential methylation was associated with evolutionary age of LINE-1 (r²=0.31, P=0.003) and Alu (r²=0.74, P=0.002) elements, with greater hypomethylation of older subfamilies (L1M, AluJ). Locus-specific hypomethylation was associated with differential expression of proximal genes, including DCLK2, HK1, ILRUN, TANK, TBCD, TNFRSF1B and TXNRD2, with higher expression of DCLK2 and TNFRSF1B associated with reduced patient survival. Hypomethylation at nine loci was highly frequent in CLL (>90% patients) but not observed in healthy individuals or other leukaemias, and was detectable in blood samples taken prior to CLL diagnosis in 9 of 82 individuals from the Melbourne Collaborative Cohort Study. Our results demonstrate differential methylation of retrotransposons in CLL by their evolutionary heritage that modulates expression of proximal genes.

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

PURPOSE

The exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).

METHODS

The mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.

RESULTS

5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139-0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.

CONCLUSION

We construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.

Identification of biomarkers predicting the chemotherapeutic outcomes of capecitabine and oxaliplatin in patients with gastric cancer

The capecitabine and oxaliplatin (CapeOX) regimen is a commonly used adjuvant chemotherapeutic regimen for gastric cancer (GC). However, some patients exhibit a poor chemotherapy response due to genetic differences among individuals. Therefore, finding an effective sensitization strategy for CapeOX is important in the treatment of GC. The present study aimed to investigate the predictive biomarkers of the CapeOX chemotherapeutic outcomes for patients with GC. A total of 30 differentially expressed genes (DEGs) were identified using the gene expression profiles from The Cancer Genome Atlas capecitabine and oxaliplatin treatment GC cases and seven key DEGs [uroplakin-1b (UPK1B), fatty acid-binding protein, heart (FABP3), cystatin-M, caspase-5 (CASP5), corticosteroid 11-β-dehydrogenase isozyme 2, cytochrome P450 4X1 (CYP4X1) and epidermal growth factor receptor kinase substrate 8-like protein 3] were associated with survival. Gene validation was performed in clinical samples divided into recurrence and nonrecurrence groups. Patients with high or low expression of UPK1B, FABP3, CASP5 and CYP4X1 had markedly different overall survival rates. A model was established and the area under the curve of the receiver operating characteristic reached 0.875 (0.793–0.957), indicating that the model had good sensitivity and specificity.

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

Gastric cancer (GC) is the second leading cause of cancer mortality and remains the fourth common cancer worldwide. The effective and feasible methods for predicting the possible outcomes for GC patients are still lacking. While genetic profiling might be suitable in some way, the application of gene expression signatures has been show to be a robust tool. Here, by performing a comprehensive search in PubMed, we provided an up-to-date summary of 39 prognostic gene signatures for GC patients, and described the processing procedure of the selection, calculation and construction of gene signature. We also reviewed current web tools including PROGgene and SurvExpress that can be used to analyze the prognostic value of multiple genes for GC. This review will aid in comprehensive understanding of the current prognostic gene signatures to accurately predict the outcome of GC patients, and may guide the future clinical management when the reliability of these signatures is validated in clinics.

Development of a novel gene signature in patients without Helicobacter pylori infection gastric cancer

Gastric cancer (GC) is one of the most fatal common cancers in worldwide. Helicobacter pylori (H. pylori) infection is closely related to the development of GC, although the mechanism is still unclear. In our study, we aim to develop a robust messenger RNA (mRNA) signature associated with H. pylori (-) GC that can sensitively and efficiently predict the prognostic. The RNA-seq expression profile and corresponding clinical data of 598 gastric cancer samples and 63 normal samples obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Using gene set enrichment analysis H. pylori (+) GC and H. pylori (-) GC patients and normal samples to select certain genes for further analysis. Using univariate and multivariate Cox regression model to establish a gene signature for predicting the overall survival (OS). Finally, we identified G2/M related seven-mRNA signature (TGFB1, EGF, MKI67, ILF3, INCENP, TNPO2, and CHAF1A) closely related to the prognosis of patients with H. pylori (-) GC. The seven-mRNA signature was identified to act as an independent prognostic biomarker by stratified analysis and multivariate Cox regression analysis. It was also validated on two test groups from TCGA and GSE15460 and shown that patients with high-risk scores based on the expression of the seven mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < .0001). In this study, we developed a seven-mRNA signature related to G2/M checkpoint from H. pylori (-) GCs that as an independent biomarker potentially with a good performance in predicting OS and might be valuable for the clinical management for patients with GC.