Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis

Finding a needle in a haystack: functional screening for novel targets in cancer immunology and immunotherapies

Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients. Immunotherapy, as a highly promising cancer treatment method, has been widely validated in the clinic, but it could only meet the needs of a small proportion of cancer patients. Finding new immunotherapy targets is the key to the future of tumor immunotherapy. Here, we revisit the application of functional screening in cancer immunology from different perspectives, from the selection of diverse in vitro and in vivo screening models to the screening of potential immune checkpoints and potentiating genes for CAR-T cells. The data will offer fresh therapeutic clues for cancer patients.

Identification and Validation of Four Serum Biomarkers With Optimal Diagnostic and Prognostic Potential for Gastric Cancer Based on Machine Learning Algorithms

BACKGROUND

Gastric cancer (GC) is considered a highly heterogeneous disease, and currently, a comprehensive approach encompassing molecular data from various biological levels is lacking.

METHODS

This study conducted different analyses, including the identification of differentially expressed genes (DEGs), weighted correlation networks (WGCNA), single-cell RNA sequencing (scRNA-seq), mRNA expression-based stemness index (mRNAsi), and multiCox analysis, utilizing data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Subsequently, the machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), combined with multiCox analysis were exploited to identify hub genes. These findings were then validated through the receiver operating characteristic (ROC) curve and Kaplan-Meier analysis, and were experimentally confirmed in GC samples by reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA).

RESULTS

Integrated analysis of TCGA and GEO databases, coupled with LASSO regression and RF algorithms, allowed us to identify 18 hub genes encoding differentially expressed secreted proteins in GC. The results of RT-PCR and bioinformatics analysis revealed four promising biomarkers with optimal diagnostic and prognostic potential. ROC analysis and Kaplan-Meier curves highlighted CHI3L1, FCGBP, VSIG2, and TFF2 as promising biomarkers for GC, offering superior modeling accuracy. These findings were further confirmed by RT-PCR and ELISA, affirming the clinical utility of these four biomarkers. Additionally, CIBERSORT analysis indicated a potential correlation between the four biomarkers and the infiltration of B memory cells and Treg cells.

CONCLUSION

This study unveiled four promising biomarkers present in the serum of patients with GC, which could serve as powerful indicators of GC and provide valuable insights for further research into GC pathogenesis.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

BACKGROUND

Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear.

METHODS

In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data.

RESULTS

We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated.

CONCLUSION

The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

CST2 promotes cell proliferation and regulates cell cycle by activating Wnt-β-catenin signalling pathway in serous ovarian cancer

BACKGROUND

Cystatin SA (CST2) plays multiple roles in different types of malignant tumours; however, its role in serous ovarian cancer (SOC) remains unclear. Therefore, we aimed to investigate the expression levels, survival outcomes, immune cell infiltration, proliferation, cell cycle, and underlying molecular mechanisms associated with the CST2 signature in SOC.

METHODS

The Cancer Genome Atlas database was used to acquire clinical information and CST2 expression profiles from patients with SOC. Wilcoxon rank-sum tests were used to compare CST2 expression levels between SOC and normal ovarian tissues. A prognostic assessment of CST2 was conducted using Cox regression analysis and the Kaplan-Meier method. Differentially expressed genes were identified using functional enrichment analysis. Immune cell infiltration was examined using a single-sample gene set enrichment analysis. Cell cycle characteristics and proliferation were assessed using a colony formation assay, flow cytometry, and a cell counting kit-8 assay. Western blots and quantitative reverse transcription PCR analyses were employed to examine CST2 expressions and related genes involved in the cell cycle and the Wnt-β-catenin signalling pathway.

RESULTS

Our findings revealed significant upregulation of CST2 in SOC, and elevated CST2 expression was correlated with advanced clinicopathological characteristics and unfavourable prognoses. Pathway enrichment analysis highlighted the association between the cell cycle and the Wnt signalling pathway. Moreover, increased CST2 levels were positively correlated with immune cell infiltration. Functionally, CST2 played vital roles in promoting cell proliferation, orchestrating the G1-to-S phase transition, and driving malignant SOC progression through activating the Wnt-β-catenin signalling pathway.

CONCLUSIONS

The elevated expression of CST2 may be related to the occurrence and progression of SOC by activating the Wnt-β-catenin pathway. Additionally, our findings suggest that CST2 is a promising novel biomarker with potential applications in therapeutic, prognostic, and diagnostic strategies for SOC.

Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein-protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan-Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell-cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

QIGTD: identifying critical genes in the evolution of lung adenocarcinoma with tensor decomposition

BACKGROUND

Identifying critical genes is important for understanding the pathogenesis of complex diseases. Traditional studies typically comparing the change of biomecules between normal and disease samples or detecting important vertices from a single static biomolecular network, which often overlook the dynamic changes that occur between different disease stages. However, investigating temporal changes in biomolecular networks and identifying critical genes is critical for understanding the occurrence and development of diseases.

METHODS

A novel method called Quantifying Importance of Genes with Tensor Decomposition (QIGTD) was proposed in this study. It first constructs a time series network by integrating both the intra and inter temporal network information, which preserving connections between networks at adjacent stages according to the local similarities. A tensor is employed to describe the connections of this time series network, and a 3-order tensor decomposition method was proposed to capture both the topological information of each network snapshot and the time series characteristics of the whole network. QIGTD is also a learning-free and efficient method that can be applied to datasets with a small number of samples.

RESULTS

The effectiveness of QIGTD was evaluated using lung adenocarcinoma (LUAD) datasets and three state-of-the-art methods: T-degree, T-closeness, and T-betweenness were employed as benchmark methods. Numerical experimental results demonstrate that QIGTD outperforms these methods in terms of the indices of both precision and mAP. Notably, out of the top 50 genes, 29 have been verified to be highly related to LUAD according to the DisGeNET Database, and 36 are significantly enriched in LUAD related Gene Ontology (GO) terms, including nuclear division, mitotic nuclear division, chromosome segregation, organelle fission, and mitotic sister chromatid segregation.

CONCLUSION

In conclusion, QIGTD effectively captures the temporal changes in gene networks and identifies critical genes. It provides a valuable tool for studying temporal dynamics in biological networks and can aid in understanding the underlying mechanisms of diseases such as LUAD.

Identification and validation of key genes in gastric cancer: insights from in silico analysis, clinical samples, and functional assays

INTRODUCTION

The underlying mechanisms of gastric cancer (GC) remain unknown. Therefore, in this study, we employed a comprehensive approach, combining computational and experimental methods, to identify potential key genes and unveil the underlying pathogenesis and prognosis of GC.

METHODS

Gene expression profiles from GEO databases (GSE118916, GSE79973, and GSE29272) were analyzed to identify DEGs between GC and normal tissues. A PPI network was constructed using STRING and Cytoscape, followed by hub gene identification with CytoHubba. Investigations included expression and promoter methylation analysis, survival modeling, mutational and miRNA analysis, gene enrichment, drug prediction, and in vitro assays for cellular behaviors.

RESULTS

A total of 83 DEGs were identified in the three datasets, comprising 41 up-regulated genes and 42 down-regulated genes. Utilizing the degree and MCC methods, we identified four hub genes that were hypomethylated and up-regulated: COL1A1, COL1A2, COL3A1, and FN1. Subsequent validation of their expression and promoter methylation on clinical GC samples through targeted bisulfite sequencing and RT-qPCR analysis further confirmed the hypomethylation and overexpression of these genes in local GC patients. Furthermore, it was observed that these hub genes regulate tumor proliferation and metastasis in in vivo and exhibited mutations in GC patients.

CONCLUSION

We found four potential diagnostic and prognostic biomarkers, including COL1A1, COL1A2, COL3A1, and FN1 that may be involved in the occurrence and progression of GC.

Cystatin SA attenuates gastric cancer cells growth and increases sensitivity to oxaliplatin via PI3K/AKT signaling pathway

PURPOSE

Cystatin SA (CST2) belongs to the superfamily of cysteine protease inhibitors. Emerging research indicates that CST2 is often dysregulated across various cancers. Its role and molecular mechanisms in gastric cancer remain underexplored. This study aims to explore the expression and function of CST2 in gastric cancer.

METHODS

CST2 expression was analyzed and validated through Western blot. CST2 overexpression was induced by lentivirus in GC cells, and the correlation between CST2 expression levels and downstream signaling pathways was assessed. In addition, multiple assays, including cell proliferation, colony formation, wound-healing, and transwell migration/invasion, were considered to ascertain the influence of CST2 overexpression on gastric cancer. The cell cycle and apoptosis were detected by flow cytometry.

RESULTS

CST2 expression at the protein level was decreased to be reduced in both gastric cancer tissues and cell lines, and CST2 expression attenuate gastric cancer growth, an effect restricted to gastric cancer cells and absent in gastric epithelial GES-1 cells. Furthermore, CST2 was demonstrated to improve chemosensitivity to Oxaliplatin in gastric cancer cells through the PI3K/AKT signaling pathway.

CONCLUSION

These findings indicate that CST2 is downregulated at the protein level in gastric cancer tissues and cell lines. Additionally, CST2 was found to attenuate the growth of gastric cancer cells and to enhance sensitivity to Oxaliplatin through the PI3K/AKT signaling pathway, specific to gastric cancer cell lines. CST2 may serve as a tumor suppressor gene increasing sensitivity to Oxaliplatin in gastric cancer.

Role of carboxylesterase and arylacetamide deacetylase in drug metabolism, physiology, and pathology

Carboxylesterases (CES1 and CES2) and arylacetamide deacetylase (AADAC), which are expressed primarily in the liver and/or gastrointestinal tract, hydrolyze drugs containing ester and amide bonds in their chemical structure. These enzymes often catalyze the conversion of prodrugs, including the COVID-19 drugs remdesivir and molnupiravir, to their pharmacologically active forms. Information on the substrate specificity and inhibitory properties of these enzymes, which would be useful for drug development and toxicity avoidance, has accumulated. Recently,in vitroandin vivostudies have shown that these enzymes are involved not only in drug hydrolysis but also in lipid metabolism. CES1 and CES2 are capable of hydrolyzing triacylglycerol, and the deletion of their orthologous genes in mice has been associated with impaired lipid metabolism and hepatic steatosis. Adeno-associated virus-mediated human CES overexpression decreases hepatic triacylglycerol levels and increases fatty acid oxidation in mice. It has also been shown that overexpression of CES enzymes or AADAC in cultured cells suppresses the intracellular accumulation of triacylglycerol. Recent reports indicate that AADAC can be up- or downregulated in tumors of various organs, and its varied expression is associated with poor prognosis in patients with cancer. Thus, CES and AADAC not only determine drug efficacy and toxicity but are also involved in pathophysiology. This review summarizes recent findings on the roles of CES and AADAC in drug metabolism, physiology, and pathology.

COL8A1 Regulates Esophageal Squamous Carcinoma Proliferation and Invasion Through PI3K/AKT Pathway

PURPOSE

Esophageal squamous carcinoma (ESCC) is a gastrointestinal malignancy with a high mortality, but the tumorigenesis is still unclear, restricting the target therapy development of ESCC. We explored the role of COL8A1 in ESCC development.

METHODS

Tissue microarrays were used to investigate the expression level of COL8A1 in ESCC tissues. The association between COL8A1 and the overall survival of ESCC patients was assessed. The effect of differential COL8A1 expression on tumor growth was investigated by the xenograft model. The regulation of COL8A1 on tumor growth, migration, and invasion was studied by using ESCC cell lines. The signal transduction pathways involved in COL8A1 were bioinformatically profiled and validated.

RESULTS

The COL8A1 was significantly expressed in cancerous tissues and was associated with poor prognosis in patients with ESCC. In vivo, the tumor growth obviously declined after inhibition of the COL8A1 expression. The abilities of cell proliferation and invasion were both decreased when the expression of COL8A1 was knockdown in ESCC cell line. Furthermore, we found the inactivation of the PI3K/AKT pathway that was mediated by knockdown of COL8A1 in ESCC cells, which was reversed with COL8A1 overexpression, whereas the cell proliferation and invasion ability were restored.

CONCLUSIONS

This is the first report that COL8A1 promote ESCC progression, which hopefully will provide a theoretical basis for clinical targeting of ESCC.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

ASPORIN: A root of the matter in tumors and their host environment

Asporin (ASPN) has been identified as one of the members of the class I small leucine-rich proteoglycans (SLRPs) family in the extracellular matrix (ECM). It is involved in classic ensigns of cancers such as self-dependent growth, resistance to growth inhibitors, restricting apoptosis, cancer metastasis, and bone-related disorders. ASPN is different from other members of SLRPs, such as decorin (DCN) and biglycan (BGN), in a way that it contains a distinctive length of aspartate (D) residues in the amino (N) -terminal region. These D-repeats residues possess germline polymorphisms and are identified to be linked with cancer progression and osteoarthritis (OA). The polyaspartate stretch in the N-terminal region of the protein and its resemblance to DCN are the reasons it is called asporin. In this review, we comprehensively summarized and updated the dual role of ASPN in various malignancies, its structure in mice and humans, variants, mutations, cancer-associated signalings and functions, the relationship between ASPN and cancer-epithelial, stromal fibroblast crosstalk, immune cells and immunosuppression in cancer and other diseases. In cancer and other bone-related diseases, ASPN is identified to be regulating various signaling pathways such as TGFβ, Wnt/β-catenin, notch, hedgehog, EGFR, HER2, and CD44-mediated Rac1. These pathways promote cancer cell invasion, proliferation, and migration by mediating the epithelial-to-mesenchymal transition (EMT) process. Finally, we discussed mouse models mimicking ASPN in vivo function in cancers and the probability of therapeutic targeting of ASPN in cancer cells, fibrosis, and other bone-related diseases.

Identification and validation of functional roles for three MYC-associated genes in hepatocellular carcinoma

BACKGROUND

Aberrations in MYC underlie a large proportion of liver hepatocellular carcinoma (LIHC) cases; however, MYC is difficult to target because of its undruggable structure. We aimed to uncover MYC-associated molecular targets to provide new strategies for LIHC treatment.

METHODS

LIHC transcriptome datasets and clinical information were obtained from The Cancer Genome Atlas. A series of bioinformatics analyses were performed for 370 patients who were stratified based on the median MYC expression level (high-MYC group and low-MYC group). Correlation analysis was performed to determine relationships between the expression of key MYC-associated genes and prognosis, DNA promotor methylation, and immune cell infiltration. Gene ontology and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analyses were performed to elucidate the functions of these genes in LIHC. Their expression and functions in LIHC were further verified using transgenic mice overexpressing c-Myc under control of the hepatocyte-specific promoter (Alb-Cre).

RESULTS

AURKB, CCNB2, and CDKN3 were overexpressed in LIHC patients with high MYC expression and were associated with poor prognosis. Upregulation of these 3 genes was significantly correlated with hypomethylated promoter status, advanced T stage, metastasis, and immune cell infiltration in LIHC patients. Functional enrichment analyses indicated that these genes participate in the "p53 signaling pathway" and "cell cycle". Furthermore, RT-PCR and IHC analysis revealed that their mRNA and protein expression levels were upregulated in an Alb-Cre;cMYClsl/- mouse model. Drugs that target these 3 MYC-related genes were identified.

CONCLUSION

Taken together, our results identify biomarkers of potential utility for managing liver cancer therapy owing to their significance in tumorigenesis, proliferation, and tumor immunity.

Hsa_circ_0043603 promotes the progression of esophageal squamous cell carcinoma by sponging miR-1178-3p and regulating AADAC expression

This study aims to investigate the regulatory impact of hsa_circ_0043,603, a circular RNA, on the progression of esophageal squamous cell carcinoma (ESCC), which ranks as the sixth leading cause of global mortality. We evaluated the expression, origin, and localization of hsa_circ_0043,603 in ESCC tumors using qRT-PCR, bioinformatics, and FISH analysis. Functional studies were conducted by manipulating the hsa_circ_0043,603 expression in Eca109 cells through overexpression and silencing plasmids. Additionally, xenografts derived from circ_0043,603-overexpressing Eca109 cells enabled us to investigate tumor growth, proliferation, and apoptosis. Through Starbase analysis, we identified miR-1178-3p as a target of circ_0043,603, which was validated using RIP and luciferase assays. Furthermore, we predicted arylacetamide deacetylase (AADAC) as a target of miR-1178-3p and examined its expression in ESCC tissues using Western blot. Lastly, we performed AADAC silencing and overexpression in Eca109 cells to study their impact on cellular phenotypic features, apoptosis, and their interaction with miR-1178-3p mimics and inhibitors. The low expression of hsa_circ_0043,603 in ESCC tissue was associated with poor prognosis. Overexpression of hsa_circ_0043,603 inhibited ESCC growth, invasion, migration, and proliferation, while promoting apoptosis in vitro and suppressing tumor growth in vivo. hsa_circ_0043,603 achieved these effects by targeting the oncogenic miR-1178-3p. Furthermore, AADAC was identified as a target of miR-1178-3p, and its reduced expression was confirmed in ESCC tissues. Overexpression of AADAC in Eca109 cells resulted in suppressed cell growth, proliferation, migration, and invasion by regulating miR-1178-3p. hsa_circ_0043,603 acts as a sponge for miR-1178-3p, leading to the regulation of AADAC expression and inhibition of ESCC development. These results suggest the potential of hsa_circ_0043,603 as a therapeutic and diagnostic target for ESCC.

Bioinformatics and pathway enrichment analysis identified hub genes and potential biomarker for gastric cancer prognosis

Introduction

Gastric cancer is one of the most common cancers in the world. This study aimed to identify genes, biomarkers, and metabolic pathways affecting gastric cancer using bioinformatic analysis and meta-analysis.

Methods

Datasets containing gene expression profiles of tumor lesions and adjacent non-tumor mucosa samples were downloaded. Common differentially expressed genes between data sets were selected to identify hub genes and further analysis. Gene Expression Profiling and Interactive Analyses (GEPIA) and the Kaplan-Meier method were used to further validate the expression level of genes and plot the overall survivalcurve, respectively.

Results and disscussion

KEGG pathway analysis showed that the most important pathway was enriched in ECM-receptor interaction. Hub genes includingCOL1A2, FN1, BGN, THBS2, COL5A2, COL6A3, SPARC and COL12A1 wereidentified. The top interactive miRNAs including miR-29a-3p, miR-101-3p,miR-183-5p, and miR-15a-5p targeted the most hub genes. The survival chart showed an increase in mortality in patients with gastric cancer, which shows the importance of the role of these genes in the development of the disease and can be considered candidate genes in the prevention and early diagnosis of gastric cancer.

Cell metabolism-based optimization strategy of CAR-T cell function in cancer therapy

Adoptive cell therapy (ACT) using chimeric antigen receptor (CAR)-modified T cells has revolutionized the field of immune-oncology, showing remarkable efficacy against hematological malignancies. However, its success in solid tumors is limited by factors such as easy recurrence and poor efficacy. The effector function and persistence of CAR-T cells are critical to the success of therapy and are modulated by metabolic and nutrient-sensing mechanisms. Moreover, the immunosuppressive tumor microenvironment (TME), characterized by acidity, hypoxia, nutrient depletion, and metabolite accumulation caused by the high metabolic demands of tumor cells, can lead to T cell "exhaustion" and compromise the efficacy of CAR-T cells. In this review, we outline the metabolic characteristics of T cells at different stages of differentiation and summarize how these metabolic programs may be disrupted in the TME. We also discuss potential metabolic approaches to improve the efficacy and persistence of CAR-T cells, providing a new strategy for the clinical application of CAR-T cell therapy.

Network and functional analyses of differentially expressed genes in gastric cancer provide new biomarkers associated with disease pathogenesis

BACKGROUND

Gastric cancer is a dominant source of cancer-related death around the globe and a serious threat to human health. However, there are very few practical diagnostic approaches and biomarkers for the treatment of this complex disease.

METHODS

This study aimed to evaluate the association between differentially expressed genes (DEGs), which may function as potential biomarkers, and the diagnosis and treatment of gastric cancer (GC). We constructed a protein-protein interaction network from DEGs followed by network clustering. Members of the two most extensive modules went under the enrichment analysis. We introduced a number of hub genes and gene families playing essential roles in oncogenic pathways and the pathogenesis of gastric cancer. Enriched terms for Biological Process were obtained from the "GO" repository.

RESULTS

A total of 307 DEGs were identified between GC and their corresponding normal adjacent tissue samples in GSE63089 datasets, including 261 upregulated and 261 downregulated genes. The top five hub genes in the PPI network were CDK1, CCNB1, CCNA2, CDC20, and PBK. They are involved in focal adhesion formation, extracellular matrix remodeling, cell migration, survival signals, and cell proliferation. No significant survival result was found for these hub genes.

CONCLUSIONS

Using comprehensive analysis and bioinformatics methods, important key pathways and pivotal genes related to GC progression were identified, potentially informing further studies and new therapeutic targets for GC treatment.

Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma

In recent studies, the tumourigenesis and development of endometrial carcinoma (EC) have been correlated significantly with redox. We aimed to develop and validate a redox-related prognostic model of patients with EC to predict the prognosis and the efficacy of immunotherapy. We downloaded gene expression profiles and clinical information of patients with EC from the Cancer Genome Atlas (TCGA) and the Gene Ontology (GO) dataset. We identified two key differentially expressed redox genes (CYBA and SMPD3) by univariate Cox regression and utilised them to calculate the risk score of all samples. Based on the median of risk scores, we composed low-and high-risk groups and performed correlation analysis with immune cell infiltration and immune checkpoints. Finally, we constructed a nomogram of the prognostic model based on clinical factors and the risk score. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. CYBA and SMPD3 were significantly related to the prognosis of patients with EC and used to construct a risk model. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low-and high-risk groups. The nomogram developed with clinical indicators and the risk scores was effective in predicting the prognosis of patients with EC. In this study, a prognostic model constructed based on two redox-related genes (CYBA and SMPD3) were proved to be independent prognostic factors of EC and associated with tumour immune microenvironment. The redox signature genes have the potential to predict the prognosis and the immunotherapy efficacy of patients with EC.

Mir-421 and mir-550a-1 are potential prognostic markers in esophageal adenocarcinoma

OBJECTIVE

To identify the prognostic indicators of esophageal adenocarcinoma (EAC) for future EAC diagnosis and treatment.

METHODS

The EAC dataset from The Cancer Genome Atlas was screened for differentially expressed microRNAs (miRNAs) and mRNAs associated with EAC. Weighted gene coexpression network analysis was performed to cluster miRNAs or mRNA with similar expression patterns to identify the miRNAs or mRNA that are highly associated with EAC. Prognostic miRNAs for overall survival (OS) were identified using Cox proportional-hazards regression analysis and least absolute shrinkage and selection operator based on survival duration and status. Two types of miRNAs were selected to develop a prognostic signature model for EAC using multiple Cox regression analysis. Furthermore, the signature was validated using internal validation sets 1 and 2. The receiver operating characteristic curve and concordance index were used to evaluate the accuracy of the signature and validation sets. The expression of miR-421, miR-550a-3p, and miR-550a-5p was assessed using quantitative polymerase chain reaction (qPCR). The proliferation, invasion, and migration of EAC cells were assessed using CCK8 and transwell assays. The OS of target mRNAs was assessed using Kaplan-Meier analysis. Functional enrichment analysis of the target mRNAs was performed using Metascape.

RESULTS

The prognostic signature and validation sets comprising mir-421 and mir-550a-1 had favorable predictive power in OS. Compared with the patients with EAC in the high-expression group, those assigned to the low-expression group displayed increased OS according to survival analysis. Differential and qPCR analysis showed that miR-421, miR-550a-3p, and miR-550a-5p were highly expressed in the EAC tissues and cell lines. Moreover, the downregulation of miR-421 and miR-550a-3p with inhibitor markedly suppressed the proliferation, invasion, and migration in OE33 cells compared with the negative control. A total of 20 target mRNAs of three miRNAs were predicted, among which seven target mRNAs-ASAP3, BCL2L2, LMF1, PPM1L, PTPN21, SLC18A2, and NR3C2-had prognostic value; PRKACB, PDCD4, RPS6KA5, and BCL2L2 were enriched in the miRNA cancer pathway.

CONCLUSION

Prognostic indicators of EAC may be useful in future EAC diagnosis and treatment.

COL8A1 enhances the invasion/metastasis in MDA-MB-231 cells via the induction of IL1B and MMP1 expression

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with a high probability of metastasis and a lack of specific targets and targeted therapeutics. Previously, we have reported that COL8A1, which is highly expressed in the mesenchymal stem-like (MSL) subtype of TNBC, facilitates TNBC growth via FAK/Src activation. Furthermore, we have found that COL8A1 enhances the invasion and metastasis of MDA-MB-231 cells, classified into MSL. However, the mechanism of invasion and metastasis by COL8A1 remains unclear. Here, we investigated the biological function of COL8A1 on the invasion and metastasis of MDA-MB-231 cells.

METHODS

The invasion and metastasis of MDA-MB-231 cells were evaluated using three-dimensional (3D) culture methods and xenograft mouse models. DNA microarray analysis examined the gene expression in COL8A1-overexpressing MDA-MB-231 cells and control cells. Gene expression was verified using RT-qPCR.

RESULTS

COL8A1-deficient cells showed little or no metastasis, whereas forced expression of COL8A1 in MDA-MB-231 cells, the MSL subtype of TNBC cell lines, significantly promoted distant metastasis after tumor resection. As with in vivo, 3D invasion assay revealed that COL8A1 increased the invasion capacity of MDA-MB-231 and Hs578T cells, classified into the MSL subtype of TNBC. DNA microarray analysis for COL8A1-overexpressing cells indicated that COL8A1 induces interleukin 1B (IL1B) and matrix metalloproteinase-1 (MMP1) expression, both of which are correlated with COL8A1 expression in the mesenchymal subtypes of TNBC, and the Kaplan-Meier plotter provided evidence that the prognosis in the MSL subtype was strongly associated with both gene expressions and COL8A1 expression. Pharmacological inhibitor treatment showed that COL8A1 regulated IL1B and MMP1 expression through a different pathway. Moreover, the knockdown of each gene expression reduced the invasion capacity of COL8A1-overexpressing MDA-MB-231 and Hs578T cells.

CONCLUSION

Our findings indicate that COL8A1-induced IL1B and MMP1 enhanced the invasion and metastasis of the MSL subtype of TNBC. Considering our previous findings that COL8A1 promotes tumor growth, COL8A1 may be a prognostic and practical therapeutic target in TNBC.

Unusual Suspects: Bone and Cartilage ECM Proteins as Carcinoma Facilitators

The extracellular matrix (ECM) is the complex three-dimensional network of fibrous proteins and proteoglycans that constitutes an essential part of every tissue to provide support for normal tissue homeostasis. Tissue specificity of the ECM in its topology and structure supports unique biochemical and mechanical properties of each organ. Cancers, like normal tissues, require the ECM to maintain multiple processes governing tumor development, progression and spread. A large body of experimental and clinical evidence has now accumulated to demonstrate essential roles of numerous ECM components in all cancer types. Latest findings also suggest that multiple tumor types express, and use to their advantage, atypical ECM components that are not found in the cancer tissue of origin. However, the understanding of cancer-specific expression patterns of these ECM proteins and their exact roles in selected tumor types is still sketchy. In this review, we summarize the latest data on the aberrant expression of bone and cartilage ECM proteins in epithelial cancers and their specific functions in the pathogenesis of carcinomas and discuss future directions in exploring the utility of this selective group of ECM components as future drug targets.

Evaluation of CEP55, SERPINE1 and SMPD3 genes and proteins as diagnostic and prognostic biomarkers in gastric carcinoma in Egyptian patients

Background

Gastric carcinoma (GC) is a fatal disease. Detection of new biomarkers that can be utilized in the early diagnosis of GC is a pressing need. This present study assessed centrosomal protein-55 (CEP55)’ serpin family E member 1 (SERPINE1) and sphingomyelin phosphodiesterase 3 (SMPD3) genes and proteins in gastric adenocarcinoma with different tumor progression features. Thirty surgically resected gastric tissue samples from thirty patients suffered from gastric cancers were obtained. The gastric tissue samples were divided into tumorous (with different stages and grades) and adjacent non-tumorous samples. CEP55, SERPINE1 and SMPD3 genes were assessed by quantitative qRT-PCR, and their proteins were assessed by ELISA in the gastric tissue samples.

Results

As regards SERPINE1, CEP55 genes and proteins, results revealed significant elevations in the GC samples (p < 0.0001). On the contrary, SMPD3 gene and protein revealed significant decreases as compared to non-tumorous samples. The studied genes and proteins showed highly significant specificity and sensitivity in the early detection of GC. SERPINE1 gene and protein revealed highly significant increases and positive correlations, while SMPD3 gene and protein revealed highly significant decreases and negative correlations as the tumor progresses.

Conclusion

CEP55, SERPINE1 and SMPD3 genes and proteins could be used as useful biomarkers for the early detection of GC. SERPINE1 and SMPD3 genes and proteins might be used as risk and protective prognostic factors in GC, respectively.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

OBJECTIVE

The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis.

METHODS

GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression.

RESULTS

We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

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.

AADAC promotes therapeutic activity of cisplatin and imatinib against ovarian cancer cells

OBJECTIVE

To explore how AADAC functions in the malignant progression of ovarian cancer, and the effect of AADAC on drug therapeutic activity against ovarian cancer cells.

METHODS

AADAC level in tumor and normal samples from TCGA-OV dataset and its survival significance were analyzed by bioinformatics methods. Signaling pathway enrichment analysis for the high- and low-AADAC patients was achieved by using GSEA software. AADAC expression in the cell lines with different treatments was evaluated via qRT-PCR. Cell proliferative ability was assessed via MTT assay Cell migratory and invasive abilities were evaluated via transwell assay. Angiogenesis assay was performed to examine the angiogenetic ability.

RESULTS

AADAC was upregulated in ovarian cancer tissues, and patients with high expression of AADAC had favorable survival conditions compared to the low AADAC expression ones. Overexpression of AADAC inhibited the malignant progression of ovarian cancer cells. Both cisplatin and imatinib suppressed cancer cell malignant progression, while overexpressed AADAC synergistically enhanced such inhibition.

CONCLUSIONS

The study demonstrated that AADAC could somehow suppress the malignant progression of ovarian cancer, especially at the cellular level. In addition, synergic tumor-inhibitory effects between AADAC and the anti-cancer drugs were identified. All the above results proposed a novel idea and candidate biomarker for ovarian cancer therapy.

Interplay of Helicobacter pylori, fibroblasts, and cancer cells induces fibroblast activation and serpin E1 expression by cancer cells to promote gastric tumorigenesis

Background

Helicobacter pylori (H. pylori) can disrupt the tight junctions between gastric epithelial cells and penetrate the intercellular spaces acting on epithelial cells, normal fibroblasts (NFs), and cancer-associated fibroblasts (CAFs), but their interaction in gastric cancer tumorigenesis and progression remains unclear.

Methods

Primary CAFs and NFs were isolated from paired gastric cancer tissues and adjacent normal tissues and identified by immunofluorescence staining and western blot analysis for FSP-1, α-SMA, FAP, and vimentin expression. RNA-sequencing was used to compare the transcriptomes between CAFs and NFs. The expressions of FAP, lumican, and α-SMA, human cytokine array, and Transwell assay were used to assess the transformation of NFs to CAFs. CCK-8 assay, colony formation, flow cytometry, Transwell assay, and nude mouse xenograft model were used to determine the effects of Serpin E1 on cell proliferation and metastasis in vitro and in vivo. Finally, Serpin E1 and/or FAP expression was measured in H. pylori-infected gerbil gastric mucosa and human gastric cancer tissues.

Results

Gastric CAFs are inflammatory CAFs with α-SMA^lowFAP^highlumican^high. The interplay of H. pylori, fibroblasts, and cancer cells promotes the transition of NFs to CAFs by inducing cytokine release, especially Serpin E1. Long-term H. pylori infection and CAFs induce Serpin E1 expression in gerbil gastric tissues and human gastric cancer cells. Serpin E1 overexpression enhances the growth, migration, invasion of gastric cancer cells in vitro, and xenograft tumor growth in nude mice via inducing angiogenesis. Serpin E1 and FAP were highly expressed in cancer cells and CAFs of gastric cancer tissues, respectively, and a good correlation was observed between their expression. Higher Serpin E1 expression is negatively associated with the overall survival of patients with gastric cancer.

Conclusions

The interplay of H. pylori, fibroblasts, and cancer cells induced Serpin E1 expression to promote the activation of NFs to CAFs and gastric carcinogenesis. Targeting Serpin E1 will provide a promising therapeutic strategy for gastric cancer by disrupting the interaction between H. pylori, CAFs, and gastric cancer cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03537-x.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

A Single-Center Follow-Up Study of Low-Grade Gastric Intraepithelial Neoplasia and the Screening of Key Genes of Precancerous Lesions

Objective

The study aimed to summarize the morphological characteristics of low-grade gastric intraepithelial neoplasia (LGIN) and explore its outcomes and risk factors. Additionally, it aimed to screen the core different expression genes (DEGs) of high-grade gastric intraepithelial neoplasia (HGIN) using bioinformatics methods to identify biomarkers for early gastric cancer outcomes.

Methods

The clinical and pathological data of 449 patients with LGIN in the endoscopy center of the Second Hospital of Hebei Medical University from June 2013 to September 2018 were collected for retrospective analysis. The GSE130823 and GSE55696 data sets were selected from the Gene Expression Omnibus database, and the GEO2R tool was used to screen DEGs in HGIN and chronic gastritis tissue types. A DEG functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery. The STRING database was utilized to create a protein–protein interaction network, and the CytoHubba plug-in was used to screen the key genes of HGIN.

Results

The incidence of LGIN increased with age, and most of the patients were aged between 45–59 years (P = 0.048). Lesions were found mainly in the cardia, mostly in people aged 60 (P < 0.05). Progression occurred in 42 of 449 patients, with a 9.4% rate of cancer development. Foci larger than 10 mm, ulcerative lesions, and an Helicobacter pylori-positive result were factors affecting the outcome of LGIN (P < 0.05). Seven core genes of HGIN were screened, including MYC, SOX2, CDX2, TBX3, KRT7, CDKN2A, and MUC5AC.

Conclusion

The patients with LGIN reflected the potential for developing cancer. A magnifying gastroscope can contribute to the detection of early gastric cancer. Additionally, the MYC, CDX2, and TBX3 genes may act as specific biomarkers of HGIN.

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Background

Prostate cancer (PCa) is one of the highest frequent malignant tumors with very complicated pathogenesis. Genes of neurodegenerative diseases can influence tumor progression. But its role in the progression of PCa remains unclear. The purpose of the present academic work was to identify significant genes with poor outcome and their underlying mechanism.

Methods

The GSE70768, GSE88808, and GSE134051 datasets were downloaded to screen the differentially expressed genes (DEGs). The DEG screening criteria were as follows: P < 0.05 and differential fold change |logFC| ≥ 1. The common DEGs (co-DEGs) of the three datasets were obtained by the Robust Rank Aggregation (RRA) method. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed using R software. Protein-protein interaction (PPI) network analysis was performed for co-DEGs using STRING to screen critical genes. Differential expression and prognosis of key genes were analyzed by the online tool Gene Expression Profiling Interactive Analysis 2 (GEPIA2). The intersection gene between key genes and neurodegenerative genes was identified by constructing a Venn diagram.

Results

A total of 263 co-DEGs were identified from the three datasets. GO analysis showed that co-DEGs were mainly involved in muscle contraction and blood circulation regulation. The top ten key genes were ACTG2, APOE, F5, CALD1, MYH11, MYL9, MYLK, TPM1, TPM2, and CALM1. GEPIA2 analysis showed that APOE, MYH11, and MYLK differ dramatically between tumor and normal tissues. These key genes are related to disease-free survival (DFS) in PCa. APOE was the intersection gene between key genes and Alzheimer-related genes.

Conclusion

The neurodegenerative gene APOE may be a potential prognostic and diagnostic biomarker for PCa.

Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Methods

For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway.

Results

We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072.

Conclusion

To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC.

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

Background

This study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.

Methodology

Tumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.

Results

Based on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.

Conclusion

A prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.

Impact of AADAC gene expression on prognosis in patients with Borrmann type III advanced gastric cancer

Background

The prognosis of Borrmann type III advanced gastric cancer (AGC) is known to vary significantly among patients. This study aimed to determine which differentially expressed genes (DEGs) are directly related to the survival time of Borrmann type III AGC patients and to construct a prognostic model.

Methods

We selected 25 patients with Borrmann type III AGC who underwent radical gastrectomy. According to the difference in overall survival (OS), the patients were divided into group A (OS<1 year, n=11) and group B (OS>3 years, n=14). DEGs related to survival time in patients with Borrmann type III AGC were determined by mRNA sequencing. The prognosis and functional differences of DEGs in different populations were determined by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The expression of mRNA and protein in cell lines was detected by quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) and Western blot (WB). Immunohistochemical (IHC) staining was used to detect protein expression in the paraffin-embedded tissues of 152 patients with Borrmann type III AGC who underwent radical gastrectomy. After survival analysis, nomograms were constructed to predict the prognosis of patients with Borrmann type III AGC.

Results

Arylacetamide deacetylase (AADAC) is a survival-related DEG in patients with Borrmann type III AGC. The higher the expression level of its mRNA and protein is, the better the prognosis of patients. Bioinformatics analysis found that AADAC showed significant differences in prognosis and function in European and American populations and Asian populations. In addition, the mRNA and protein expression levels of AADAC were high in differentiated gastric cancer (GC) cells. We also found that AADAC was an independent prognostic factor for patients with Borrmann type III AGC, and its high expression was significantly correlated with better OS and disease-free survival (DFS). Nomogram models of AADAC expression level combined with clinicopathological features can be used to predict the OS and DFS of Borrmann type III AGC.

Conclusion

AADAC can be used as a biomarker to predict the prognosis of Borrmann type III AGC and has the potential to become a new therapeutic target for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09594-1.

Bioinformatic analysis of cancer-associated fibroblast related gene signature as a predictive model in clinical outcomes and immune characteristics of gastric cancer

Background

Gastric cancer (GC) has a high incidence and high mortality rate among Asian countries, and distinguishing predictive prognosis biomarkers for GC are essential. Cancer-associated fibroblasts (CAFs) play a significant role in the progression, immune evasion, and therapeutic resistance of GC. Therefore, CAF-associated genes might have huge potential as prognostic biomarkers for predicting tumor progression and survival rate in GC pateints.

Methods

A sum of 1,134 GC patients from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE62254, and GSE84437 datasets as well as GC cohorts from Xijing hospital were included. Firstly, we performed univariate Cox regression analysis to identify CAF-associated prognostic genes. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to develop a CAF gene signature (CAFGS) in the TCGA-STAD training cohort. CAFGS’s predictive performance was examined in both the training and validation cohorts, and the relationship between CAFGS and the tumor microenvironment (TME) was investigated by ssGSEA, CIBERSORT, TIMER, and ESTIMATE. Finally, a nomogram of CAFGS was established.

Results

Ten CAF-associated genes (ANGPTL4, CPNE8, CST2, HTR1F, IL1RAP, NR1D1, NTAN1, OLFML2B, TMEM259, and VTN) were identified to develop CAFGS. A high CAFGS score represented a worse outcome for GC patients in four cohorts, and a strong correlation was found between CAFGS and the infiltration of immune cells. We showed that CAFs contribute to immune evasion and unfavorable prognoses of GC patients by promoting the formation of an immunosuppressive microenvironment, and a high level of CAF infiltration may attenuate the efficacy of immunotherapy. The nomogram based on CAFGS showed reasonable predictive ability and may deliver great clinical net benefits.

Conclusions

We established a CAFGS model with 10 CAF-associated genes that had a great predictive value for GC prognosis and survival rate evaluation. This study could provide a novel insight for investigating the role of CAFs in GC.

A comprehensive analysis of potential gastric cancer prognostic biomarker ITGBL1 associated with immune infiltration and epithelial-mesenchymal transition

Background

Integrin, beta-like 1 (ITGBL1) is involved in a variety of human malignancies. However, the information on the involvement of ITGBL1 in gastric carcinoma (GC) is limited. Hence, this study aimed further to explore the functions and mechanisms of ITGBL1 in GC.

Methods

First, multiple bioinformatics databases, including Oncomine, Tumor Immune Estimation Resource, UALCAN, and Kaplan–Meier Plotter, were used to predict the expression level and prognostic value of ITGBL1, as well as its association with immune infiltration and epithelial–mesenchymal transition (EMT) in GC. Quantitative reverse transcription–polymerase chain reaction and immunohistochemical analysis were used to detect the expression of ITGBL1 in both GC tissues and cells. Then, targeted silencing of ITGBL1 in GC cells was further used to examine the biological functions of ITGBL1.

Results

These databases revealed that ITGBL1 was overexpressed and affected the overall survival in GC. Besides, the expression of ITGBL1 positively correlated with immune-infiltrating cells and EMT-related markers. Subsequently, molecular biology experiments verified these predictions. In GC tissues and cells, ITGBL1 was notably overexpressed. Loss-of-function studies showed that the knockdown of ITGBL1 significantly suppressed migration and invasion but promoted apoptosis in MGC803 GC cells. Furthermore, the inhibition of ITGBL1 resulted in remarkably increased protein expression levels of cadherin 1, while the expression of Vimentin, Snail, and transforming growth factor-β1 was downregulated, indicating the initiation and progression of GC caused by ITGBL1 partly via inducing EMT.

Conclusions

To sum up, the findings indicated that ITGBL1 acted as a valuable oncogenic factor in GC.

In Silico Identification of Hub Genes as Observing Biomarkers for Gastric Cancer Metastasis

Perception of hub genes engaged in metastatic gastric cancer (mGC) promotes novel ways to diagnose and treat the illness. The goal of this investigation is to recognize the hub genes and reveal its molecular mechanism. In order to explore the potential facts for gastric cancer, the expression profiles of two different datasets were used (GSE161533 and GSE54129). The genes were confirmed to be part of the PPI network for gastric cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. Responsible hub genes were identified. Data from Kaplan-Meier plotter confirmed the predictive value of these distinct genes in various stages of gastric malignancy. Upregulated and downregulated genes were identified to utilize for further analysis. Positive regulation by a host of viral process, positive regulation of granulocyte differentiation, negative regulation of histone H3-K9 methylation were found in DEGs analysis. In addition, five KEGG pathways were identified as an essential enhancer that include nucleotide excision repair; base excision repair; DNA replication; homologous recombination; and complement and coagulation cascades. POLE, BUB1B, POLD4, C3, BLM, CCT7, PRPF31, APEX1, PSMA7, and CDC45 were chosen as hub genes after combining the PPI results. Our study recommends that BUB1B, CCT7, APEX1, PSMA7, and CDC45 might be potential biomarkers for gastric cancer. These biomarkers are upregulated genes. Therefore, suppression of these genes will increase the survival rate in gastric cancer patients.

Development of a Model System to Study Expression Profile of RAC2 Gene in Breast Cancer MDA-MB-231 Cell Line

The RAC2 gene encoding GTPases involve cellular signaling of actin polymerization, cell migration, and formation of the phagocytic NADPH oxidase complex. Oncogenic mutations in the RAC2 gene have been identified in various cancers, and extensive research is in progress to delineate its signaling pathways and identify potential therapeutic targets in breast cancers. This paper explored developing a bioinformatics model system to understand the RAC2 gene expression pattern concerning estrogenic receptor status in breast cancers. We have used the MDA-MB-231 breast cancer cell line to identify RAC2 gene expression. To simplify the development of model system with one dataset, we retrieved the microarray dataset GSE27515 from the Gene Expression Omnibus (GEO) for the differential gene expression analysis. Then, network analysis, pathway enrichment analysis, volcano plot, ORA, and the up/downregulated genes were used to highlight genes involved in signaling network pathways. We observed that the RAC2 gene is upregulated in the GSM679722, GSM676923, and GSM679724 downregulated in the samples GSM676925, GSM676926, and GSM676927 from the GEO dataset. Our observation found that the RAC2 gene is upregulated in the estrogen receptor (ER) negative breast cancers and downregulated in ER-positive breast cancer, involving pathways such as focal adhesion, MAPK signaling, axon guidance, and VEGF signaling pathway.

Exploring the Multicomponent Synergy Mechanism of Yinzhihuang Granule in Inhibiting Inflammation-Cancer Transformation of Hepar Based on Integrated Bioinformatics and Network Pharmacology

Background

The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application.

Methods

To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1.

Results

The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway.

Conclusion

The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.

Identification of prognostic signature with seven LncRNAs for papillary thyroid carcinoma

PURPOSE

With the increasing incidence of thyroid cancer (TC), the prognostic risk assessment of thyroid cancer has been becoming more and more important. The aim of this study was to screen TC-related biomarkers and identify key multi-long non coding RNA (lncRNA) signature for prognostic risk assessment of papillary TC.

MATERIAL AND METHODS

The lncRNAs differentially expressed between TC tissue and adjacent normal tissue was identified by R language. Bioinformatics analysis was applied to screen the lncRNAs significantly associated with prognosis in TC patients and build the multi-lncRNA signature. The lncRNAs were annotated by co-expression and enrichment analysis to demonstrate the underlying mechanism of their effect on prognosis.

RESULTS

285 up-regulated and 174 down-regulated differently expressed lncRNAs were identified. Based on seven signature lncRNAs (AL591846.2, AC253536.3, AC004112.1, LINC00900, AC008555.1, TNRC6C-AS1, LINC01736) a prognostic risk assessment model was built. The model can segregate the patients into the high-risk and low-risk groups (P value <0.0001, CI: 0.02∼0.14). ROC analysis revealed that the area under the curve reached 0.86, indicating that this model had an excellent sensitivity and specificity. Also, the model could act as an independent prognostic indication (HR ​= ​2.90, P value ​= ​0.0094 with multivariate analysis). Annotation results further supported and enriched our understanding of the seven signature lncRNAs. Importantly, expression levels of three of the seven lncRNAs were confirmed in Gene Expression Omnibus (GEO) data.

CONCLUSIONS

This study has provided a promising method for the prognostic risk assessment in patients with TC.

Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients

BACKGROUND

Accumulating studies have demonstrated that lncRNAs play vital roles in the prognosis of gastric cancer (GC); however, the prognostic value of N6-methyladenosine-related lncRNAs has not been fully reported in GC. This study aimed to construct and validate an m6A-related lncRNA pair signature (m6A-LPS) for predicting the prognosis of GC patients.

METHODS

GC cohort primary data were downloaded from The Cancer Genome Atlas. We analysed the coexpression of m6A regulators and lncRNAs to identify m6A-related lncRNAs. Based on cyclical single pairing along with a 0-or-1 matrix and least absolute shrinkage and selection operator-penalized regression analyses, we constructed a novel prognostic signature of m6A-related lncRNA pairs with no dependence upon specific lncRNA expression levels. All patients were divided into high-risk and low-risk group based on the median risk score. The predictive reliability was evaluated in the testing dataset and whole dataset with receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis was used to identify potential pathways.

RESULTS

Fourteen m6A-related lncRNA pairs consisting of 25 unique lncRNAs were used to construct the m6A-LPS. Kaplan-Meier analysis showed that the high-risk group had poor prognosis. The area under the curve for 5-year overall survival was 0.906, 0.827, and 0.882 in the training dataset, testing dataset, and whole dataset, respectively, meaning that the m6A-LPS was highly accurate in predicting GC patient prognosis. The m6A-LPS served as an independent prognostic factor for GC patients after adjusting for other clinical factors (p < 0.05). The m6A-LPS had more accuracy and a higher ROC value than other prognostic models for GC. Functional analysis revealed that high-risk group samples mainly showed enrichment of extracellular matrix receptor interactions and focal adhesion. Moreover, N-cadherin and vimentin, known biomarkers of epithelial-mesenchymal transition, were highly expressed in high-risk group samples. The immune infiltration analysis showed that resting dendritic cells, monocytes, and resting memory CD4 T cells were significantly positively related to the risk score. Thus, m6A-LPS reflected the infiltration of several types of immune cells.

CONCLUSIONS

The signature established by pairing m6A-related lncRNAs regardless of expression levels showed high and independent clinical prediction value in GC patients.

CENPA is one of the potential key genes associated with the proliferation and prognosis of ovarian cancer based on integrated bioinformatics analysis and regulated by MYBL2

Background

Ovarian cancer (OV) is a highly lethal disease, and the fifth leading cause of all cancer-related deaths in women. The study aimed to identify potential key genes associated with the proliferation and prognosis of OV.

Methods

Differentially expressed genes (DEGs) between ovarian cancer and normal tissues were screened by the robust rank aggregation (RRA) method. The expression of CENPA and MYBL2 were examined in SKOV3 and A2780 ovarian cancer cell lines and tumor tissues by qRT-PCR and western blot. Small RNA interference assays, plasmid overexpression assays and EdU assays were used to validate the proliferative effect of the MYBL2-CENPA axis in ovarian cancer cell lines. The ChIP assay was used to verify the direct regulation of MYBL2 on CENPA.

Results

133 up-regulated genes and 158 down-regulated genes were identified, and the up-regulated genes mainly enrichment in cell cycle. The three up-regulated gene with DNA separation (CENPA, CENPF and CEP55) might be tightly correlated with proliferation and prognosis of OV. Knockdown CENPA expression inhibited the proliferation of A2780 and SKOV3 cells After the knockout of MYBL2, the expression of CENPA significantly decreased. MYBL2 directly binds to the promoter region of CENPA.

Conclusions

The MYBL2-CENPA pathway plays an important role in the proliferation of ovarian cancer cells, suggesting that this pathway may be a potential target for the treatment of ovarian cancer.

Identification of Key Pathways and Establishment of a Seven-Gene Prognostic Signature in Cervical Cancer

Cervical cancer (CC) remains high morbidity and mortality. We aimed to identify critical pathways underlying cervical carcinogenesis and establish a prognostic signature. Six datasets from the gene expression omnibus (GEO) database were used to screen the differentially expressed genes (DEGs) between CC and normal tissues. We used the unions of the DEGs to perform functional analysis. The 108 overlapped DEGs were analyzed to determine a prognostic signature by Cox regression and Lasso analysis based on The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) and Immune Cell Abundance Identifier (ImmuCellAI) were used to determine the relationships between the signature and biological functions. The PI3K-Akt signaling pathway, the Ras signaling pathway, and the viral carcinogenesis pathway may be critical for CC development. We identified seven genes (PLOD2, DSG2, SPP1, CXCL8, MCM5, HLTF, and KLF4) to construct a risk score formula. Survival analysis showed that the high-risk group indicated a worse prognosis than the low-risk group (p < 0.0001). The AUC of the prognostic signature was 0.7449, 0.7641, and 0.8146 at 1, 3, and 5 years. We also identified that the signature is an independent prognostic factor. GSEA showed five pathways were relevant to the signature, such as the adherens junction pathway. The signature also affected the abundances of various types of immune cells, such as B cell, CD4+ T cell, and CD8+ T cell. Further, we found that SPP1 was co-expressed with HK3, CD163, CCL3, CLEC5A, MMP8, TREM1, OLR1, and TREM2. The results of Gene Ontology analysis showed that SPP1 and its co-expressed related proteins mainly affected metabolic process, multicellular organismal process, cell communication, cell proliferation, protein binding, and transporter activity. In conclusion, the present study explored the key pathways for CC development and the seven-gene signature can effectively make the prognosis evaluation of CC patients.

Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

Forecasting Gastric Cancer Diagnosis, Prognosis, and Drug Repurposing with Novel Gene Expression Signatures

Gastric cancer (GC) is a prevalent disease worldwide with high mortality and poor treatment success. Early diagnosis of GC and forecasting of its prognosis with the use of biomarkers are directly relevant to achieve both personalized/precision medicine and innovation in cancer therapeutics. Gene expression signatures offer one of the promising avenues of research in this regard, as well as guiding drug repurposing analyses in cancers. Using publicly accessible gene expression datasets from the Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), we report here original findings on co-expressed gene modules that are differentially expressed between 133 GC samples and 46 normal tissues, and thus hold potential for novel diagnostic candidates for GC. Furthermore, we found two co-expressed gene modules were significantly associated with poor survival outcomes revealed by survival analysis of the RNA-Seq TCGA datasets. We identified STAT6 (signal transducer and activator of transcription 6) as a key regulator of the identified gene modules. Finally, potential therapeutic drugs that may target and reverse the expression of the identified altered gene modules examined for drug repurposing analyses and the unraveled compounds were further investigated in the literature by the text mining method. Accordingly, we found several repurposed drug candidates, including Trichostatin A, Vorinostat, Parthenolide, Panobinostat, Brefeldin A, Belinostat, and Danusertib. Through text mining analysis and literature search validation, Belinostat and Danusertib were suggested as possible novel drug candidates for GC treatment. These findings collectively inform multiple aspects of GC medical management, including its precision diagnosis, forecasting of possible outcomes, and drug repurposing for innovation in GC medicines in the future.

Exploring potential mechanisms of Suhexiang Pill against COVID-19 based on network pharmacology and molecular docking

BACKGROUND

The traditional Chinese medicine prescription Suhexiang Pill (SHXP), a classic prescription for the treatment of plague, has been recommended in the 2019 Guideline for coronavirus disease 2019 (COVID-19) diagnosis and treatment of a severe type of COVID-19. However, the bioactive compounds and underlying mechanisms of SHXP for COVID-19 prevention and treatment have not yet been elucidated. This study investigates the mechanisms of SHXP in the treatment of COVID-19 based on network pharmacology and molecular docking.

METHODS

First, the bioactive ingredients and corresponding target genes of the SHXP were screened from the traditional Chinese medicine systems pharmacology database and analysis platform database. Then, we compiled COVID-19 disease targets from the GeneCards gene database and literature search. Subsequently, we constructed the core compound-target network, the protein-protein interaction network of the intersection of compound targets and disease targets, the drug-core compound-hub gene-pathway network, module analysis, and hub gene search by the Cytoscape software. The Metascape database and R language software were applied to analyze gene ontology biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Finally, AutoDock software was used for molecular docking of hub genes and core compounds.

RESULTS

A total of 326 compounds, 2450 target genes of SHXP, and 251 genes related to COVID-19 were collected, among which there were 6 hub genes of SHXP associated with the treatment of COVID-19, namely interleukin 6, interleukin 10, vascular endothelial growth factor A, signal transducer and activator of transcription 3 (STAT3), tumor necrosis factor (TNF), and epidermal growth factor. Functional enrichment analysis suggested that the effect of SHXP against COVID-19 is mediated by synergistic regulation of several biological signaling pathways, including Janus kinase/ STAT3, phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt), T cell receptor, TNF, Nuclear factor kappa-B, Toll-like receptor, interleukin 17, Chemokine, and hypoxia-inducible factor 1 signaling pathways. SHXP may play a vital role in the treatment of COVID-19 by suppressing the inflammatory storm, regulating immune function, and resisting viral invasion. Furthermore, the molecular docking results showed an excellent binding affinity between the core compounds and the hub genes.

CONCLUSION

This study preliminarily predicted the potential therapeutic targets, signaling pathways, and molecular mechanisms of SHXP in the treatment of severe COVID-19, which include the moderate immune system, relieves the "cytokine storm," and anti-viral entry into cells.

COL1A1 Gene Expression in Hepatitis B Virus (HBV) Related Hepatocellular Carcinoma (HCC) Egyptian's Patients

Introduction:

Collagens are the most abundant proteins in the human body, accounting for one-third of total proteins. Over the last few years, accumulated evidence have indicated that some collagens are differentially expressed in cancer. The aim of the study was to assess COL1A1 gene expression as a novel marker for the progression of hepatitis B cirrhosis into hepatocellular carcinoma.

Methods:

This cohort study included 348 subjects and was conducted between May 2018 and June 2019. Subjects were divided into 4 groups: group1 included HBV positive hepatocellular carcinoma patients “HCC” (n= 87), group II included HBV positive patients with liver cirrhosis “LC” (n = 87), group III included chronic hepatitis B patients with neither HCC nor cirrhosis “ C-HBV” (n = 87) and group IV consisted of healthy volunteers as controls (n = 87). Fasting venous blood samples (10 ml) were collected from each participant in this study and were used for assessment of aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, albumin and alfa-fetoprotein (AFP). Another portion of blood was collected in 2 vacutainer tubes containing EDTA, one for Complete blood count and the other for gene expression of COL1A1.

Results:

The gene expression of collagen was 6.9 ± 8.8 in group 1 (HBV positive hepatocellular carcinoma patients) and this was a significant increase in comparison with the other groups. In group 2 (HBV positive patients with liver cirrhosis), the gene expression (collagen) was 3.7±1.5 and it was significantly increased when compared with group 4 (healthy volunteers).

Conclusion:

COL1A1 gene expression can be used as an indicator of the progression of hepatitis B cirrhosis into hepatocellular carcinoma.

Signature of arylacetamide deacetylase expression is associated with prognosis and immune infiltration in ovarian cancer

Objective

The role of the protein-coding gene arylacetamide deacetylase (AADAC) in the prognostication of ovarian cancer remains uncertain. We aimed to identify and validate its prognostic value using integrated bioinformatics analyses.

Methods

Gene expression profiles of RNA-sequencing and microarray data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus. Univariate and multivariate Cox regression models were used to evaluate the prognostic value of gene expression. The predictive accuracy of the gene signature model was evaluated using a time-dependent receiver operating characteristic (ROC) curve. In addition, the correlation between immune infiltration and AADAC was identified. A nomogram of the gene signature with clinical parameters was constructed to estimate the clinical application of the signature for survival prediction in patients with ovarian cancer.

Results

Univariate and multivariate Cox regression analyses in the training and validation cohorts indicated that a high AADAC expression signature was significantly and independently correlated with better survival outcomes in ovarian cancer. AADAC upregulation positively correlated with the infiltration of CD4+ memory T cells. Immunological signature gene sets were significantly enriched in CD4+ T cell regulation pathways. The area under the curve of the time-dependent ROC for overall survival indicated that the constructed nomogram had a moderate predictive ability for prognostic prediction in ovarian cancer.

Conclusion

AADAC expression signature significantly and independently correlated with the survival outcome and CD4+ memory T cell infiltration in ovarian cancer, indicating its potential applicability in the prediction of prognosis and immunotherapy efficacy.

Identifying Diagnostic and Prognostic Biomarkers and Candidate Therapeutic Drugs of Gastric Cancer Based on Transcriptomics and Single-Cell Sequencing

Background and Objective: Gastric cancer (GC) is an important health burden and the prognosis of GC is poor. We aimed to explore new diagnostic and prognostic indicators as well as potential therapeutic targets for GC in the current study.

Methods: We screened the overlapped differentially expressed genes (DEGs) from GSE54129 and TCGA STAD datasets. Protein-protein interaction network analysis recognized the hub genes among the DEGs. The roles of these genes in diagnosis, prognosis, and their relationship with immune infiltrates and drug sensitivity of GC were analyzed using R studio. Finally, the clinically significant hub genes were verified using single-cell RNA sequencing (scRNA-seq) data.

Results: A total of 222 overlapping genes were screened, which were enriched in extracellular matrix-related pathways. Further, 17 hub genes were identified, and our findings demonstrated that BGN, COMP, COL5A2, and SPARC might be important diagnostic and prognostic indicators of GC, which were also correlated with immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and sensitivity of therapeutic drugs. The scRNA-seq results further confirmed that all four hub genes were highly expressed in GC.

Conclusion: Based on transcriptomics and single-cell sequencing, we identified four diagnostic and prognostic biomarkers of GC, including BGN, COMP, COL5A2, and SPARC, which can help predict drug sensitivity for GC as well.

Bioinformatics Analysis of Hub Genes and Potential Therapeutic Agents Associated with Gastric Cancer

Purpose

The current treatment methods available for advanced gastric cancer are not very promising. Hence, it is important to explore novel biomarkers and potential therapeutic agents to treat gastric cancer (GC). This study aimed to identify hub genes associated with GC prognosis and explore potential drugs for its treatment.

Materials and Methods

Three gene expression data of GC and normal tissues were downloaded from the Gene Expression Omnibus (GEO) and processed to identify the differentially expressed genes (DEGs). We conducted a comprehensive analysis of DEGs, including functional enrichment analysis, construction of protein-protein interaction (PPI) network, identification of hub genes, survival analysis and expression verification of hub genes. Finally, we constructed the network of miRNA-mRNA, and predicted the drugs that might be effective for GC treatment.

Results

A total of 340 DEGs, including 94 up-regulated and 246 down-regulated genes, were identified. Among the up-regulated DEGs, the enrichment terms were primarily related to tumorigenesis and tumor progression, extracellular matrix organization, and collagen catabolic process. Additionally, 10 hub genes (FN1, COL3A1, COL1A2, BGN, THBS2, COL5A2, THBS1, COL5A1, SPARC, and COL4A1) were identified, out of which 7 genes were significantly associated with poor overall survival (OS) in GC. The expression levels of these 7 hub genes were verified using real-time PCR, immunohistochemistry, and the GEPIA2 (Gene Expression Profiling Interactive Analysis) server. A regulatory network of miRNA-mRNA was also constructed, and the top 4 interactive miRNAs (hsa-miR-29b-3p, hsa-miR-140-3p, hsa-miR-29a-3p, and hsa-miR-29c-3p) that targeted the most hub genes were identified. Finally, fourteen small molecules were predicted to be effective in treating GC.

Conclusion

The identification of the hub genes, miRNA-mRNA network, and potential candidate drugs associated with GC provides new insights into the molecular mechanisms and treatment of GC.

High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.