Downregulation of ALDOB is associated with poor prognosis of patients with gastric cancer

Fructose-1, 6-Bisphosphate Aldolase B Suppresses Glycolysis and Tumor Progression of Gastric Cancer

OBJECTIVE

Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. However, mounting evidence indicates a link between the glycolysis and tumorigenesis, including gastric cancer.

METHODS

Our research identified 5508 differently expressed mRNAs in gastric cancer. Then, the genes highly associated with tumorigenesis were identified through weighted correlation network analysis (WGCNA). Bioinformatics analysis observed that these hub genes were significantly linked to the regulation of cell cycle, drug metabolism, and glycolysis. Among these hub genes, there is a critical gene involved in glycolysis regulation, namely fructose-bisphosphate B (ALDOB).

RESULTS

Analysis based on The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets revealed that ALDOB was significantly downregulated in GC compared with normal tissues. In addition, cell viability assay confirmed that ALDOB acted as a tumor suppressor. Finally, drug sensitivity analysis revealed that ALDOB increased the sensitivity of gastric cancer cells to most antitumor drugs, especially talazoparib, XAV939, and FTI-277. Our results showed that the expression of ALDOB was significantly lower in GC tissues than in normal tissues. And ALDOB significantly inhibited proliferation and migration, delayed glycolysis in GC cells. Consequently, our study suggests that ALDOB may be a potential target for the clinical treatment of gastric cancer.

Feature Engineering-Assisted Drug Repurposing on Disease-Drug Transcriptome Profiles in Gastric Cancer

Gastric cancer is one of the most common and deadly types of cancer in the world. To develop new biomarkers and drugs to diagnose and treat this cancer, it is necessary to identify the differences between the transcriptome profiles of gastric cancer and healthy individuals, identify critical genes associated with these differences, and make potential drug predictions based on these genes. In this study, using two gene expression datasets related to gastric cancer (GSE19826 and GSE79973), 200 genes that were ready for machine learning were selected, and their expression levels were analyzed. The best 100 genes for the model were chosen with the permutation feature importance method, and central genes, such as SCARB1, ETV3, SPATA17, FAM167A-AS1, and MTBP, which were shown to be associated with gastric cancer, were identified. Then, using the drug repurposing method with the Connectivity Map CLUE Query tools, potential drugs such as Forskolin, Gestrinone, Cediranib, Apicidine, and Everolimus, which showed a highly negative correlation with the expression levels of the selected genes, were identified. This study provides a method to develop new approaches to diagnosing and treating gastric cancer by comparing the transcriptome profiles of patients gastric cancer and performing a feature engineering-assisted drug repurposing analysis based on cancer data.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Functional Profiling of Soft Tissue Sarcoma Using Mechanistic Models

Soft tissue sarcoma is an umbrella term for a group of rare cancers that are difficult to treat. In addition to surgery, neoadjuvant chemotherapy has shown the potential to downstage tumors and prevent micrometastases. However, finding effective therapeutic targets remains a research challenge. Here, a previously developed computational approach called mechanistic models of signaling pathways has been employed to unravel the impact of observed changes at the gene expression level on the ultimate functional behavior of cells. In the context of such a mechanistic model, RNA-Seq counts sourced from the Recount3 resource, from The Cancer Genome Atlas (TCGA) Sarcoma project, and non-diseased sarcomagenic tissues from the Genotype-Tissue Expression (GTEx) project were utilized to investigate signal transduction activity through signaling pathways. This approach provides a precise view of the relationship between sarcoma patient survival and the signaling landscape in tumors and their environment. Despite the distinct regulatory alterations observed in each sarcoma subtype, this study identified 13 signaling circuits, or elementary sub-pathways triggering specific cell functions, present across all subtypes, belonging to eight signaling pathways, which served as predictors for patient survival. Additionally, nine signaling circuits from five signaling pathways that highlighted the modifications tumor samples underwent in comparison to normal tissues were found. These results describe the protective role of the immune system, suggesting an anti-tumorigenic effect in the tumor microenvironment, in the process of tumor cell detachment and migration, or the dysregulation of ion homeostasis. Also, the analysis of signaling circuit intermediary proteins suggests multiple strategies for therapy.

Dysregulation of core neurodevelopmental pathways-a common feature of cancers with perineural invasion

Background: High nerve density in tumors and metastasis via nerves (perineural invasion-PNI) have been reported extensively in solid tumors throughout the body including pancreatic, head and neck, gastric, prostate, breast, and colorectal cancers. Ablation of tumor nerves results in improved disease outcomes, suggesting that blocking nerve-tumor communication could be a novel treatment strategy. However, the molecular mechanisms underlying this remain poorly understood. Thus, the aim here was to identify molecular pathways underlying nerve-tumor crosstalk and to determine common molecular features between PNI-associated cancers. Results: Analysis of head and neck (HNSCC), pancreatic, and gastric (STAD) cancer Gene Expression Omnibus datasets was used to identify differentially expressed genes (DEGs). This revealed extracellular matrix components as highly dysregulated. To enrich for pathways associated with PNI, genes previously correlated with PNI in STAD and in 2 HNSCC studies where tumor samples were segregated by PNI status were analyzed. Neurodevelopmental genes were found to be enriched with PNI. In datasets where tumor samples were not segregated by PNI, neurodevelopmental pathways accounted for 12%-16% of the DEGs. Further dysregulation of axon guidance genes was common to all cancers analyzed. By examining paralog genes, a clear pattern emerged where at least one family member from several axon guidance pathways was affected in all cancers examined. Overall 17 different axon guidance gene families were disrupted, including the ephrin-Eph, semaphorin-neuropilin/plexin, and slit-robo pathways. These findings were validated using The Cancer Genome Atlas and cross-referenced to other cancers with a high incidence of PNI including colon, cholangiocarcinoma, prostate, and breast cancers. Survival analysis revealed that the expression levels of neurodevelopmental gene families impacted disease survival. Conclusion: These data highlight the importance of the tumor as a source of signals for neural tropism and neural plasticity as a common feature of cancer. The analysis supports the hypothesis that dysregulation of neurodevelopmental programs is a common feature associated with PNI. Furthermore, the data suggested that different cancers may have evolved to employ alternative genetic strategies to disrupt the same pathways. Overall, these findings provide potential druggable targets for novel therapies of cancer management and provide multi-cancer molecular biomarkers.

ALDOB plays a tumor-suppressive role by inhibiting AKT activation in gastric cancer

Enhanced aerobic glycolysis has been one of the cancer hallmarks. Cancerous cells develop certain alterations during glucose metabolism for supporting their infinite growth requirement and metastasis. Therefore, targeting metabolism, for instance, crucial glycolytic enzymes, will provide a novel therapeutic strategy to treat cancer. Aldolase B (ALDOB), as a glycolytic enzyme, plays a contentious role in cancers and can either act against the tumor or as an oncogenic enzyme. The precise role of ALDOB in gastric cancer (GC) and the endogenous process is elusive and needs further exploration. This investigation revealed that ALDOB expression was markedly decreased in GC tissues. Furthermore, ALDOB inhibition was notably linked with tumor size, depth of tumor invasion, lymph node metastasis (LNM), tumor node metastases (TNM) staging, and substandard prognosis of GC. The assessment of loss- and gain-of-function indicated that ALDOB inhibited the growth and the migrative ability of GC cells, suggesting its anti-tumor role. Mechanistic studies revealed that ALDOB modulates the AKT signaling pathway. The increase in growth and cells' ability to migrate stimulated by ALDOB inhibition was partially impaired in cells under the influence of AKT inhibitors. The overall data highlights a novel target, the ALDOB/AKT signaling axis for the treatment of GC.

Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.

Pan-cancer analysis of aldolase B gene as a novel prognostic biomarker for human cancers

Aldolase B (ALDOB) gene is essential for the process of glycolysis and differentially expressed in cancers. The aims of this study were to explore the potential role of ALDOB in pan-cancer, in order to deepen the research on the pathological mechanism of cancer. Hence, we used several online tools (TIMER2, GEPIA2, UALCAN, cBioPortal, and MXPRESS) and R language to identify the correlation between the ALDOB expression and survival analysis, genetic alteration, DNA methylation, and immune cell infiltration based on The Cancer Genome Atlas project. The results showed that ALDOB was lowly expressed in pan-cancer. Survival analysis revealed that low expression of ALDOB was markedly related with poor clinical prognosis, while the genetic alteration within ALDOB changed along with the difference of overall survival (OS) and disease-free survival (DFS) prognosis in several cancers. A possible relationship between DNA methylation and ALDOB expression for several tumors was found. Besides, ALDOB expression was confirmed to be associated with tumor immune cell infiltration, especially in breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), and testicular germ cell tumors (TGCT) cases. Further, the enrichment analysis demonstrated that metabolic pathway was closely related to ALDOB expression. Our results provide a comprehensive pan-cancer analysis and suggest ALDOB could act as a promising tumor predictive biomarker for human cancer.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein–protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein–protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients’ prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

Identification of significant genes associated with prognosis of gastric cancer by bioinformatics analysis

BACKGROUND

Gastric cancer (GC) ranks second in mortality among all malignant diseases worldwide. However, the cause and molecular mechanism underlying gastric cancer are not clear. Here, we used integrated bioinformatics to identify possible key genes and reveal the pathogenesis and prognosis of gastric cancer.

METHODS

The gene expression profiles of GSE118916, GSE79973, and GSE29272 were available from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between GC and normal gastric tissues were screened by R software and Venn diagram software. GO and KEGG pathway enrichment of DEGs was performed using the DAVID database. A protein-protein interaction (PPI) network was established by STRING and visualized using Cytoscape software. Then the influence of hub genes on expression and survival was assessed using TCGA database.

RESULTS

A total of 83 DEGs were found in the three datasets, including 41 up-regulated genes and 42 down-regulated genes. These DEGs were mainly enriched in extracellular matrix organization and cell adhesion. The enriched pathways obtained in the KEGG pathway analysis were extracellular matrix (ECM)-receptor interaction and focal adhesion. A PPI network of DEGs was analyzed using the Molecular Complex Detection (MCODE) app of Cytoscape. Four genes were considered hub genes, including COL5A1, FBN1, SPARC, and LUM. Among them, LUM was found to have a significantly worse prognosis based on TCGA database.

CONCLUSIONS

We screened DEGs associated with GC by integrated bioinformatics analysis and found one potential biomarker that may be involved in the progress of GC. This hub gene may serve as a guide for further molecular biological experiments.

Prognostic values of ALDOB expression and 18F-FDG PET/CT in hepatocellular carcinoma

Purpose

The glycolytic enzyme fructose 1,6-bisphosphate aldolase B (ALDOB) is aberrantly expressed and impacts the prognosis in hepatocellular carcinoma (HCC). Hepatic ALDOB loss leads to paradoxical upregulation of glucose metabolism, favoring hepatocellular carcinogenesis. Nevertheless, the relationship between ALDOB expression and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake, and their effects on HCC prognosis remain unclear. We evaluated whether ALDOB expression is associated with ¹⁸F-FDG uptake and their impacts on HCC prognosis prediction.

Methods

Changes in ALDOB expression levels and the prognostic values in HCC were analyzed using data from The Cancer Genome Atlas (TCGA) database. Ultimately, 34 patients with HCC who underwent ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) preoperatively were enrolled in this retrospective study. ALDOB expression was determined using immunohistochemistry (IHC) staining, and the maximum standardized uptake value (SUVmax) of HCC was calculated from the ¹⁸F-FDG PET/CT scans. The relationship between ALDOB expression and SUVmax was examined, and their impacts on overall survival were evaluated using Cox proportional hazards models and Kaplan-Meier survival analysis. ALDOB overexpression in HUH7 and 7721 cells was used to analyze its role in tumor metabolism.

Results

According to TCGA database, the ALDOB mRNA level was downregulated in HCC compared to normal tissue, and significantly shortened overall survival in HCC patients. ALDOB protein expression was similarly decreased in IHC findings in HCC than that in adjacent normal tissues (P<0.05) and was significantly associated with tumor size (P<0.001), high tumor-node-metastasis stage (P=0.022), and elevated SUVmax (P=0.009). ALDOB expression in HCC was inversely correlated with SUVmax (r=-0.454; P=0.012), and the optimal SUVmax cutoff value for predicting its expression was 4.15. Prognostically, low ALDOB expression or SUVmax ≥3.9 indicated shorter overall survival time in HCC. Moreover, COX regression analysis suggested high SUVmax as an independent prognostic risk factor for HCC (P=0.036). HCC patients with negative ALDOB expression and positive SUVmax (≥3.9) were correlated with worse prognosis. ALDOB overexpression in HCC cells significantly decreases ¹⁸F-FDG uptake and lactate production.

Conclusion

SUVmax in HCC patients is inversely correlated with ALDOB expression, and ¹⁸F-FDG PET/CT may be useful for ALDOB status prediction. The combined use of ALDOB expression and ¹⁸F-FDG PET/CT data can provide additional information on disease prognosis in HCC patients.

H19 may regulate the immune cell infiltration in carcinogenesis of gastric cancer through miR-378a-5p/SERPINH1 signaling

Background

Increasing studies have indicated that noncoding RNA (ncRNA)-mediated competing endogenous RNA (ceRNA) network serves as a significant role in cancer progression, but the underlying regulatory mechanisms of which in gastric cancer (GC) remain largely unclear.

Methods

Based on Gene Expression Omnibus and The Cancer Genome Atlas datasets, potential biomarkers for GC were screened and validated by machine learning. Then, upstream regulatory ncRNA of potential biomarkers was identified to construct a novel ceRNA network in GC through means of stepwise reverse prediction and validation. Ultimately, tumor immune cell infiltration analysis was performed based on the EPIC algorithm.

Results

A total of 188 differentially expressed genes (DEGs) were screened, and three candidate diagnostic biomarkers (FAP, PSAPL1, and SERPINH1) for GC were identified and validated. Subsequently, H19 and miR-378a-5p were identified as upstream regulatory ncRNAs that could potentially bind SERPINH1 in GC. Moreover, Immune infiltration analysis revealed that each component in the ceRNA network (H19/miR-378a-5p/SERPINH1) was significantly correlated with the infiltration abundances of diverse tumor-infiltrating immune cells.

Conclusions

H19 may regulate the immune cell infiltration in carcinogenesis of GC through miR-378a-5p/SERPINH1 signaling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02760-6.

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.

Comprehensive bioinformatic analysis reveals a cancer-associated fibroblast gene signature as a poor prognostic factor and potential therapeutic target in gastric cancer

BACKGROUND

Gastric cancer is one of the deadliest cancers, currently available therapies have limited success. Cancer-associated fibroblasts (CAFs) are pivotal cells in the stroma of gastric tumors posing a great risk for progression and chemoresistance. The poor prognostic signature for CAFs is not clear in gastric cancer, and drugs that target CAFs are lacking in the clinic. In this study, we aim to identify a poor prognostic gene signature for CAFs, targeting which may increase the therapeutic success in gastric cancer.

METHODS

We analyzed four GEO datasets with a network-based approach and validated key CAF markers in The Cancer Genome Atlas (TCGA) and The Asian Cancer Research Group (ACRG) cohorts. We implemented stepwise multivariate Cox regression guided by a pan-cancer analysis in TCGA to identify a poor prognostic gene signature for CAF infiltration in gastric cancer. Lastly, we conducted a database search for drugs targeting the signature genes.

RESULTS

Our study revealed the COL1A1, COL1A2, COL3A1, COL5A1, FN1, and SPARC as the key CAF markers in gastric cancer. Analysis of the TCGA and ACRG cohorts validated their upregulation and poor prognostic significance. The stepwise multivariate Cox regression elucidated COL1A1 and COL5A1, together with ITGA4, Emilin1, and TSPAN9 as poor prognostic signature genes for CAF infiltration. The search on drug databases revealed collagenase clostridium histolyticum, ocriplasmin, halofuginone, natalizumab, firategrast, and BIO-1211 as the potential drugs for further investigation.

CONCLUSIONS

Our study demonstrated the central role of extracellular matrix components secreted and remodeled by CAFs in gastric cancer. The gene signature we identified in this study carries high potential as a predictive tool for poor prognosis in gastric cancer patients. Elucidating the mechanisms by which the signature genes contribute to poor patient outcomes can lead to the discovery of more potent molecular-targeted agents and increase the therapeutic success in gastric cancer.

Identification of hub pathways and drug candidates in gastric cancer through systems biology

Gastric cancer is the fourth cause of cancer death globally, and gastric adenocarcinoma is its most common type. Efforts for the treatment of gastric cancer have increased its median survival rate by only seven months. Due to the relatively low response of gastric cancer to surgery and adjuvant therapy, as well as the complex role of risk factors in its incidences, such as protein-pomp inhibitors (PPIs) and viral and bacterial infections, we aimed to study the pathological pathways involved in gastric cancer development and investigate possible medications by systems biology and bioinformatics tools. In this study, the protein-protein interaction network was analyzed based on microarray data, and possible effective compounds were discovered. Non-coding RNA versus coding RNA interaction network and gene-disease network were also reconstructed to better understand the underlying mechanisms. It was found that compounds such as amiloride, imatinib, omeprazole, troglitazone, pantoprazole, and fostamatinib might be effective in gastric cancer treatment. In a gene-disease network, it was indicated that diseases such as liver carcinoma, breast carcinoma, liver fibrosis, prostate cancer, ovarian carcinoma, and lung cancer were correlated with gastric adenocarcinoma through specific genes, including hgf, mt2a, mmp2, fbn1, col1a1, and col1a2. It was shown that signaling pathways such as cell cycle, cell division, and extracellular matrix organization were overexpressed, while digestion and ion transport pathways were underexpressed. Based on a multilevel systems biology analysis, hub genes in gastric adenocarcinoma showed participation in the pathways such as focal adhesion, platelet activation, gastric acid secretion, HPV infection, and cell cycle. PPIs are hypothesized to have a therapeutic effect on patients with gastric cancer. Fostamatinib seems a potential therapeutic drug in gastric cancer due to its inhibitory effect on two survival genes. However, these findings should be confirmed through experimental investigations.

Targeting Energy Metabolism in Cancer Treatment

Cancer is the second most common cause of death worldwide after cardiovascular diseases. The development of molecular and biochemical techniques has expanded the knowledge of changes occurring in specific metabolic pathways of cancer cells. Increased aerobic glycolysis, the promotion of anaplerotic responses, and especially the dependence of cells on glutamine and fatty acid metabolism have become subjects of study. Despite many cancer treatment strategies, many patients with neoplastic diseases cannot be completely cured due to the development of resistance in cancer cells to currently used therapeutic approaches. It is now becoming a priority to develop new treatment strategies that are highly effective and have few side effects. In this review, we present the current knowledge of the enzymes involved in the different steps of glycolysis, the Krebs cycle, and the pentose phosphate pathway, and possible targeted therapies. The review also focuses on presenting the differences between cancer cells and normal cells in terms of metabolic phenotype. Knowledge of cancer cell metabolism is constantly evolving, and further research is needed to develop new strategies for anti-cancer therapies.

Transcriptome analysis reveals SALL4 as a prognostic key gene in gastric adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) dominates 80-90% of gastric cancer (GC). Over the years, it has been realized that the identification of the genes responsible for gastric carcinogenesis is essential to understand the biomarker discovery.

METHODS

This study aims to identify candidate genes for biomarker discovery in STAD. RNA-Seq was performed on three paired tumor-normal and one unpaired tumor samples from four GC patients and investigated for differentially expressed genes (DEGs) using DESeq2. Gene set enrichment analysis were performed. The DEGs were compared with two STAD microarray datasets available on Gene Expression Omnibus (GEO) database. Survival study (OS) were performed using KM-Plotter on the common genes between all the datasets.

RESULTS

Totally, 148 DEGs were identified, wherein 55 genes were upregulated and 93 genes were downregulated with |log2foldchange| > 1 and Benjamini-Hochberg (BH) Adjusted P value < 0.01. Cell adhesion molecule (CAM) Pathway was found to be the most significant among the upregulated genes. Gastric acid secretion and mineral absorption pathways were the most significant pathways among the downregulated genes. Comparison with two GEO datasets followed by OS analysis revealed two upregulating genes, APOC1 and SALL4 with prognostic significance.

CONCLUSION

Upregulation of APOC1 is associated with marginal overall survival (OS) and SALL4 over-expression was associated with the poor OS using KM-Plotter during 5 years data period. Our study suggests that SALL4 could be a promising biomarker candidate in STAD.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

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.

Hypoxia-Related Gene-Based Signature Can Evaluate the Tumor Immune Microenvironment and Predict the Prognosis of Colon Adenocarcinoma Patients

Background

Colon adenocarcinoma (COAD) is a common gastrointestinal tumor and often occurs in the left colon with a poor prognosis. The progression of COAD is closely related to the tumor microenvironment, especially the hypoxia. Currently, few studies have reported the correlation between hypoxia-related genes and the prognosis of COAD patients. Furthermore, we constructed a prognostic model using four hypoxia-related genes to predict the prognosis of COAD patients.

Methods

The mRNA expression profiles and corresponding clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The string online analysis tool was used to construct a protein–protein interaction network (PPI) of hypoxia-related genes. Kaplan–Meier curve was used to analyze the relationship of hypoxia risk score and the overall survival of COAD patients, and the receiver operating characteristic (ROC) curve was used to assess the reliability.

Results

We screened out four hypoxia genes, including TKTL1 (transketolase like 1), SLC2A3 (solute carrier family 2 member 3), ALDOB (aldolase, fructose-bisphosphate B) and ENO3 (enolase 3), which were used to construct a hypoxia risk model to predict the overall survival of COAD patients. Besides, we also found that the hypoxia risk score was correlated with the immunosuppression of tumor microenvironment.

Conclusion

The model we constructed with four survival-related hypoxia genes, including TKTL1, SLC2A3, ALDOB and ENO3, could be used to predict the overall survival of COAD patients with high stability.

Microarray profile analysis identifies ETS1 as potential biomarker regulated by miR-23b and modulates TCF4 in gastric cancer

Background

Gastric cancer (GC), a common malignancy of the human digestive system, represents the second leading cause of cancer-related deaths worldwide. Early detection of GC has a significant impact on clinical outcomes. The aim of this study was to identify potential GC biomarkers.

Methods

In this study, we conducted a multi-step analysis of expression profiles in GC clinical samples downloaded from TCGA database to identify differentially expressed miRNAs (DEMs) and differentially expressed mRNAs (DEGs). Potential prognostic biomarkers from the available DEMs were then established using the Cox regression method. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the biological role of the predicted target genes of the miRNA biomarkers. Then, the prognostic DEM-mediated regulatory network was constructed based on transcription factor (TF)–miRNA–target interaction. Subsequently, the consensus genes were further determined based on the overlap between DEGs and these target genes of DEMs. Besides, expression profile, co-expression analysis, immunity, and prognostic values of these prognostic genes were also investigated to further explore the roles in the mechanism of GC tumorigenesis.

Results

We got five miRNAs, including miR-23b, miR-100, miR-143, miR-145, and miR-409, which are associated with the overall survival of GC patients. Subsequently, enrichment analysis of the target genes of the miRNA biomarkers shown that the GO biological process terms were mainly enriched in mRNA catabolic process, nuclear chromatin, and RNA binding. In addition, the KEGG pathways were significantly enriched in fatty acid metabolism, extracellular matrix (ECM) receptor interaction, and proteoglycans in cancer pathways. The transcriptional regulatory network consisting of 68 TFs, 4 DEMs, and 58 targets was constructed based on the interaction of TFs, miRNAs, and targets. The downstream gene ETS1 of miR-23b and TCF4 regulated by ETS1 were obtained by the regulatory network construction and co-expression analysis. High expression of ETS1 and TCF4 indicated poor prognosis in GC patients, particularly in the advanced stages. The expression of ETS1 and TCF4 was correlated with CD4⁺ T cells, CD8⁺ T cells, and B cells.

Conclusions

miR-23b, ETS1, and TCF4 were identified as the prognostic biomarkers. ETS1 and TCF4 had potential immune function in GC, which provided a theoretical basis for molecular-targeted combined immunotherapy in the future.

Establishment of a prognostic model of ten transcription factors in gastric cancer

Transcription factors (TFs) play an important role in tumors. We integrated and analyzed 13 GPL570 platform gastric cancer (GC) microarrays, identified 10 independent prognostic TFs, and constructed a GC prognostic model. Using GSE26942 as the verification set, the Kaplan-Meier curve showed that the signature distinguish the survival rate of GC patients (P < 0.01), and the AUC values are 0.746 and 0.630, respectively. Compared with the clinicopathological characteristics, the signature is an independent prognostic factor (P < 0.05). A nomogram was established based on the model, and the five-year calibration curve verified that the prediction of the nomogram was almost consistent with the actual survival rate, C-index of 0.747 indicated a moderate prognostic ability. The analysis of target genes of 10 TFs showed that they are closely related to the progression of GC. External database and rt-PCR showed that the RNA and protein expression of TFs are consistent with our analysis.

α-Actinin1 promotes tumorigenesis and epithelial-mesenchymal transition of gastric cancer via the AKT/GSK3β/β-Catenin pathway

α-Actinin1 (ACTN1), an actin cross-linking protein, is implicated in cytokinesis, cell adhesion, and cell migration. In addition, it is involved in the tumorigenesis and development of certain cancers, such as breast cancer. We explored the function of ACTN1 in gastric cancer (GC), which has largely remained unclear. High-throughput sequencing and public microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) revealed the upregulation of ACTN1 in gastric cancer with a poor prognosis. These results were further verified by western blotting (WB), Real-Time Quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry. We constructed loss and gain of function gastric cancer cells, which revealed the effect of ACTN1 over-expression on promoting GC cell proliferation, invasion, migration, and inhibited apoptosis. Mechanistic studies revealed that ACTN1 regulates the epithelial-mesenchymal transition (EMT) and tumorigenesis of gastric cancer via the AKT/GSK3β/β-catenin pathway, confirmed by the inhibitor of AKT MK2206. Altogether, these results demonstrated that ACTN1 could be a promising candidate for gastric cancer treatment.

Glucoside xylosyltransferase 2 as a diagnostic and prognostic marker in gastric cancer via comprehensive analysis

To investigate the potential role of GXYLT2 (glucoside xylosyltransferase 2) in gastric cancer (GC), the TCGA (The Cancer Genome Atlas) database and Gene Expression Omnibus (GEO) dataset were used to evaluate GXYLT2 mRNA expression, and the standardized mean difference and diagnostic value were comprehensively assessed. Survival analysis and univariate/multivariate cox regression analysis were performed to evaluate the prognostic value of GXYLT2 in GC patients. The correlation between GXYLT2 and tumor immune cells was identified by using the CIBERSORT algorithm. The results showed that GXYLT2 expression level was significantly increased in GC tissues. GXYLT2 expression was significantly correlated with the grade, stage, and invasion depth of gastric cancer. Overall survival was reduced in the high GXYLT2 expression group. Univariate and multivariate Cox regression analyses showed that GXYLT2 was a reliable prognostic factor. GSEA showed that GXYLT2 might participate in the development of GC through tumor-related pathways. The expression of GXYLT2 was positively correlated with 5 tumor-infiltrating immune cells (resting dendritic cells, m2 macrophages, monocytes, active NK cells and resting mast cells), and was negatively correlated with 6 tumor-infiltrating immune cells (plasma cells, activated memory CD4 T cells, resting NK cells, activated dendritic cells, and activated neutrophils and mast cells). Through cell experiment verification, GXYLT2 expression level in gastric cancer cells was found to be high, which verified the results from the bioinformatics analysis. Furthermore, immunohistochemical staining results also showed that GC tissues had positive GXYLT2 expression. In summary, GXYLT2 might be a potential diagnostic and prognostic biomarker for gastric cancer.

Identification and evaluation of fructose-bisphosphate aldolase B as a potential diagnostic biomarker in choledochal cysts patients: a quantitative proteomic analysis

BACKGROUND

Choledochal cyst (CC) is a congenital bile duct malformation, with a higher incidence in minors. Patients with CCs are at risk of pancreatitis and ascending cholangitis. The main forms of treatments aim to avoid any possible hepatic, pancreatic, or biliary complications. Since early diagnosis is of great importance for CC treatment and prognosis, this investigation was designed to screen and identify potential biomarkers from the serum samples of CC patients for CC early diagnosis.

METHODS

Quantitative label free proteomic analysis was used to identify differentially expressed proteins in serum samples from CC patients and normal healthy children. The expression levels of biomarker candidates were further confirmed using quantitative polymerase chain reaction (Q-PCR), Western blot analysis, and immunohistochemistry in the choledochal tissues.

RESULTS

The quantitative label free proteomic analysis identified 47 differentially expressed proteins in the serum samples from the CC patients and the normal children, including 14 up-regulated proteins and 33 down-regulated proteins. The expression profile of eight biomarker candidates in CC patients, namely, insulin-like growth factor binding protein 2 (IGFBP2), tropomyosin (TPM3), fructose-bisphosphate aldolase B (ALDOB), fumarylacetoacetate hydrolase (FAH), superoxide dismutase 3 (SOD3), secreted protein acidic and cysteine rich (SPARC), apolipoprotein E (APOE), and retinol binding protein 4 (RBP4), were selected for further examination in choledochal tissues, showing that ALDOB was significantly increased.

CONCLUSIONS

The results demonstrated that the ALDOB protein increased significantly in choledochal tissues and the serum samples of CC patients, which may serve as an effective predictor for early diagnosis of CC.

Molecular Characterization of Advanced Colorectal Cancer Using Serum Proteomics and Metabolomics

Colorectal cancer (CRC) is a growing public health concern due to its high mortality rate. Currently, there is a lack of valid diagnostic biomarkers and few therapeutic strategies are available for CRC treatment, especially for advanced CRC whose underlying pathogenic mechanisms remain poorly understood. In the present study, we investigated the serum samples from 20 patients with stage III or IV advanced CRC using data-independent acquisition (DIA)-based proteomics and ultra-performance liquid chromatography coupled to time-of-flight tandem mass spectrometry (UPLC-TOF-MS/MS) metabolomics techniques. Overall, 551 proteins and 719 metabolites were identified. Hierarchical clustering analysis revealed that the serum proteomes of advanced CRC are more diversified than the metabolomes. Ten biochemical pathways associated with cancer cell metabolism were enriched in the detected proteins and metabolites, including glycolysis/gluconeogenesis, biosynthesis of amino acids, glutathione metabolism, and arachidonic acid metabolism, etc. A protein-protein interaction network in advanced CRC serum was constructed with 80 proteins and 21 related metabolites. Correlation analysis revealed conserved roles of lipids and lipid-like molecules in a regulatory network of advanced CRC. Three metabolites (hydroquinone, leucenol and sphingomyelin) and two proteins (coagulation factor XIII A chain and plasma kallikrein) were selected to be potential biomarkers for advanced CRC, which are positively and significantly correlated with CEA and/or CA 19-9. Altogether, the results expanded our understanding of the physiopathology of advanced CRC and discovered novel potential biomarkers for further validation and application to improve the diagnosis and monitoring of advanced CRC.

Development and Validation of a Hypoxia-Related Signature for Predicting Survival Outcomes in Patients With Bladder Cancer

Objectives

This study aimed to develop and validate a hypoxia signature for predicting survival outcomes in patients with bladder cancer.

Methods

We downloaded the RNA sequence and the clinicopathologic data of the patients with bladder cancer from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository?facetTab=files) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. Hypoxia genes were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Differentially expressed hypoxia-related genes were screened by univariate Cox regression analysis and Lasso regression analysis. Then, the selected genes constituted the hypoxia signature and were included in multivariate Cox regression to generate the risk scores. After that, we evaluate the predictive performance of this signature by multiple receiver operating characteristic (ROC) curves. The CIBERSORT tool was applied to investigate the relationship between the hypoxia signature and the immune cell infiltration, and the maftool was used to summarize and analyze the mutational data. Gene-set enrichment analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in both risk groups. Furthermore, we developed a model and presented it with a nomogram to predict survival outcomes in patients with bladder cancer.

Results

Eight genes (AKAP12, ALDOB, CASP6, DTNA, HS3ST1, JUN, KDELR3, and STC1) were included in the hypoxia signature. The patients with higher risk scores showed worse overall survival time than the ones with lower risk scores in the training set (TCGA) and two external validation sets (GSE13507 and GSE32548). Immune infiltration analysis showed that two types of immune cells (M0 and M1 macrophages) had a significant infiltration in the high-risk group. Tumor mutation burden (TMB) analysis showed that the risk scores between the wild types and the mutation types of TP53, MUC16, RB1, and FGFR3 were significantly different. Gene-Set Enrichment Analysis (GSEA) showed that immune or cancer-associated pathways belonged to the high-risk groups and metabolism-related signal pathways were enriched into the low-risk group. Finally, we constructed a predictive model with risk score, age, and stage and validated its performance in GEO datasets.

Conclusion

We successfully constructed and validated a novel hypoxia signature in bladder cancer, which could accurately predict patients’ prognosis.

Identification of the hub genes and prognostic indicators of gastric cancer and correlation of indicators with tumor-infiltrating immune cell levels

Aims: To identify the hub genes and prognostic indicators of gastric cancer (GC) and determine the correlation between prognostic indicators and the tumor-infiltrating immune cell levels so as to provide useful information for future GC diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) stomach adenocarcinoma dataset and two microarray datasets were used to screen the overlapping differentially expressed genes (DEGs) between normal gastric and GC tissue samples. Hub genes were screened via protein-protein interaction networks and module analysis of the overlapping DEGs. Their expression was validated at the cell level and tissue level using the ONCOMINE database. The prognostic indicators of overall survival (OS) and disease-free survival was identified by Cox proportional hazards regression analysis based on tumor grade and cancer stage. The expression of hub genes was validated at the cell level. The correlation of prognostic indicators with the tumor-infiltrating immune cell levels was analyzed using Tumor IMmune Estimation Resource. Results: Ten hub genes, namely CDC6, CDC20, BUB1B, TOP2A, CDK1, AURKA, CCNA2, CCNB1, MAD2L1, and KIF11, were screened and their upregulation in the GC tissue was verified. Three prognostic factors, namely LUM, VCAN, and EFNA4, were identified; their expression was higher in GC cells than in normal cells. LUM, VCAN, and EFNA4 were correlated with tumor-infiltrating immune cell levels in GC. Significance: The identified hub genes and prognostic indicators of GC could be useful indicators for future GC diagnosis and treatment.

Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets

Gastric cancer (GC) is a kind of malignancy with a high mortality and recurrence. An effective prediction model based on ideal biomarkers to assess prognosis could benefit patients for optimization of treatment. Bioinformatics has played an increasingly important role in the study of cancer diseases. Therefore, this study started with bioinformatics to establish a reliable prognostic model of gastric cancer. The gene expression data and clinical data of GC tissues and normal tissues were obtained from the Gene Expression Omnibus (GEO), Genotype‐Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) profile database. We finally identified a four gene signature and constructed a prognostic model. The results of internal and external validation showed that the model is highly reliable. In addition, we also constructed a nomogram based on the model, which was verified by a calibration curve to show its predicted accuracy. Comprehensive analysis indicated that the four genes in the model are related to the occurrence and development of tumors, perhaps they are potential targets for tumor treatment. Generally, this prognostic model can bring potential benefits to patients with gastric cancer.

Gene expression barcode values reveal a potential link between Parkinson's disease and gastric cancer

Gastric cancer is a disease that develops from the lining of the stomach, whereas Parkinson’s disease is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. Although these two diseases seem to be distinct from each other, increasing evidence suggests that they might be linked. To explore the linkage between these two diseases, differentially expressed genes between the diseased people and their normal controls were identified using the barcode algorithm. This algorithm transforms actual gene expression values into barcode values comprised of 1’s (expressed genes) and 0’s (silenced genes). Once the overlapped differentially expressed genes were identified, their biological relevance was investigated. Thus, using the gene expression profiles and bioinformatics methods, we demonstrate that Parkinson’s disease and gastric cancer are indeed linked. This research may serve as a pilot study, and it will stimulate more research to investigate the relationship between gastric cancer and Parkinson’s disease from the perspective of gene profiles and their functions.

Identification of Potential Biomarkers Associated with Prognosis in Gastric Cancer via Bioinformatics Analysis

Background

Gastric cancer (GC) is one of the leading causes of cancer-related mortality worldwide. We aimed to identify differentially expressed genes (DEGs) and their potential mechanisms associated with the prognosis of GC patients.

Material/Methods

This study was based on gene profiling information for 37 paired samples of GC and adjacent normal tissues from the GSE118916, GSE79973, and GSE19826 datasets in the Gene Expression Omnibus database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to investigate the biological role of the DEGs. The protein–protein interaction (PPI) network was constructed by Cytoscape, and the Kaplan-Meier plotter was used for prognostic analysis.

Results

We identified 119 DEGs, including 21 upregulated and 98 downregulated genes, in GC. The 21 upregulated genes were mainly enriched in extracellular matrix-receptor interaction, focal adhesion, and transforming growth factor-β signaling, while the 98 downregulated genes were significantly associated with gastric acid secretion, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. Thirty hub DEGs were obtained for further analysis. Twenty-five of the 30 hub DEGs were significantly associated with the prognosis of GC, and 21 of the 25 hub DEGs showed consistent expression trends within the 3 profile datasets. KEGG reanalysis of these 21 hub DEGs showed that COL1A1, COL1A2, COL2A1, COL11A1, THBS2, and SPP1 were mainly enriched in the extracellular matrix-receptor interaction pathways.

Conclusions

We identified 6 genes that were significantly related to the prognosis of GC patients. These genes and pathways could serve as potential prognostic markers and be used to develop treatments for GC patients.

Monocarboxylate transporter 9 (MCT9) is down-regulated in renal cell carcinoma

BACKGROUND

The renal cell carcinoma (RCC) incidences are continuously increasing, however, their proper characterization remains difficult. Mammalian kidneys require large amounts of energy, and monocarboxylate transporter (MCT) gene family is responsible for the transport of monocarboxylic compounds across plasma membranes.

OBJECTIVE

A total of 14 MCT members have been identified in humans, which show highly distinct substrate affinities and tissue distributions. To understand the yet-uncharacterized renal cancer-specific role of MCTs, we identified MCT members that are differentially regulated during the renal tumor progression.

METHODS

We examined the expression level of MCT members in renal cell tumors and their relationship with survival rate of patients using a public database. Quantitative RT-PCR and northern blotting were performed to validate the expression of MCTs. Anti-MCT9 antiserum was raised in rabbit and used to examine MCT9 expression in normal and tumor tissue arrays. Effect of MCT9 overexpression on cell proliferation was measured using renal cancer cell lines.

RESULTS

MCT9 was found to be abundantly and exclusively expressed in human kidney cells, and was highly downregulated in renal cancers. Kaplan-Meier plotter analysis revealed an increased survival rate of MCT9 high-expressing RCC patients. MCT9 proteins were detected in normal kidney tissue sections and their overexpression clearly attenuated renal cell proliferation.

CONCLUSIONS

MCT9 was identified as a novel highly downregulated gene in renal cell cancer, and its overexpression clearly attenuated RCC cell proliferation. Thus, functional analysis of MCT9 may help in deciphering a yet-undiscovered kidney-specific energy metabolism during renal tumor progression.

Selective treatment pressure in colon cancer drives the molecular profile of resistant circulating tumor cell clones

The characterization of circulating tumor cells (CTCs) holds promises for precision medicine because these cells are an important clinical indicator of treatment efficacy. We established the first and still only nine permanent colon CTC lines from peripheral blood samples of a patient with metastatic colon cancer collected at different time points during treatment and cancer progression. The study objectives were (i) to compare the gene expression profiles of these CTC lines, and (ii) to determine the main features acquired during treatment. The number of upregulated genes was higher in the CTC lines obtained after treatment, indicating that they acquired properties to escape treatment pressure. Among these upregulated genes, some are involved in the mTOR and PI3K/AKT signaling pathways. Moreover, cytidine deaminase expression was significantly increased in the CTC lines obtained after failure of the first- and second-line 5-fluorouracile-based treatments, suggesting that these CTCs can eliminate this specific drug and resist to therapy. Several enzymes involved in xenobiotic metabolism also were upregulated after treatment, suggesting the activation of detoxification mechanisms in response to chemotherapy. Finally, the significant higher expression of aldolase B in four of the six CTC lines obtained after treatment withdrawal and cancer progression indicated that these clones originated from liver metastases. In conclusion, these CTC lines generated at different time points during treatment of metastatic colon cancer in a single patient are characterized by the deregulation of different genes that promote (i) drug resistance, (ii) xenobiotic and energy metabolism, and (iii) stem cell properties and plasticity.

GATA3 somatic mutations are associated with clinicopathological features and expression profile in TCGA breast cancer patients

The effect of somatic mutations and the gene expression profiles on the prognosis is well documented in cancer research. This study was conducted to evaluate the association of GATA3 somatic mutations with tumor features, survival, and expression profiles in breast cancer. Clinicopathological information was compared between TCGA-BRCA patients with GATA3-mutant and non-mutant tumors in all patients as well as in ER-positive subgroup. Cox-regression method was used to evaluate the association of the GATA3 mutation status with overall survival time. Differential gene expression, functional annotation, and protein-protein interaction analyses were performed using edgeR, Metascape, DAVID, STRING and CytoNCA. GATA3-mutant and non-mutant samples had significantly different clinicopathological features (p < 0.05). While GATA3 mutation status was not associated with the overall survival in the entire cohort (p_adj = 0.52), the GATA3-wild type ER-positive cases had a better prognosis than mutant ones (p_adj = 0.04). GATA3 expression was higher in tumors than normal tissues. Several pathways were different between mutant and non-mutant groups (p < 0.05). Interleukin-6 was found as the highest scored gene in both comparisons (normal vs. mutant and normal vs. non-mutant groups) in the entire patient and in the ER-positive subgroup, suggesting the association of IL6 with breast tumorigenesis. These findings suggest that GATA3 mutations can be associated with several tumor characteristics and influence the pattern of gene expression. However, GATA3 mutation status seems to be a prognostic factor for the disease only in ER-positive patients.

Meta-Analysis Reveals the Prognostic Relevance of Nuclear and Membrane-Associated Bile Acid Receptors in Gastric Cancer

INTRODUCTION

Bile acids (BAs) arising from duodenogastric reflux are known to facilitate gastric cancer (GC) development. Although BAs traditionally contribute to carcinogenesis through direct cellular cytotoxicity, increasing evidence implicates nuclear and membrane BA receptors (BARs) as additional factors influencing cancer risk. Indeed, some BARs are already linked with GC, but conflicting evidence and lack of information regarding other endogenous BARs warrant further investigation. In this study, we meta-analyzed multiple data sets to identify clinically relevant relationships between BAR expression and prognosis, clinicopathology, and activity in GC.

METHODS

We collected transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas to analyze associations between BAR expression and GC prognosis, subtype, and clinicopathology. We also used Ingenuity Pathway Analysis to assess and predict functions, upstream regulators, and downstream mediators of membrane and nuclear BARs in GC.

RESULTS

BARs showed differential distribution in GC; membrane BARs (G protein-coupled BAR 1, sphingosine-1-phosphate receptor 2, and cholinergic receptor muscarinic 2) were enriched in diffuse-, genome-stable, and mesenchymal-type tumors, whereas nuclear BARs (pregnane-X-receptor, constitutive androstane receptor, and farnesoid-X-receptor) were enriched in chromosome instability and metabolic subtypes. High expression of all membrane but not nuclear BARs was associated with poor prognosis and unfavorable GC clinicopathologic features. Similarly, expression patterns of membrane but not nuclear BARs varied geographically, aligning with Helicobacter pylori infection and GC mortality rates. Finally, GC-related oncogenes, namely transforming growth factor β1, were associated with membrane BARs, whereas many metabolic-associated genes were associated with nuclear BARs.

DISCUSSION

Through transcriptomic meta-analysis, we identified distinct expression profiles between nuclear and membrane BARs that demonstrate prognostic relevance and warrant further investigation.

Digging deeper through glucose metabolism and its regulators in cancer and metastasis

Glucose metabolism enzymes and transporters play major role in cancer development and metastasis. In this study, we discuss glucose metabolism, transporters, receptors, hormones, oncogenes and tumor suppressors which interact with glucose metabolism and we try to discuss their major role in cancer development and cancer metabolism. We try to highlight the. Metabolic changes in cancer and metastasis upregulation of glycolysis is observed in many primary and metastatic cancers and aerobic glycolysis is the most favorable mechanism for glucose metabolism in cancer cells, and it is a kind of evolutionary change. The question that is posed at this juncture is: Can we use aerobic glycolysis phenotype and enzymes beyond this mechanism in estimating cancer prognosis and metastasis? Lactate is a metabolite of glucose metabolism and it is a key player in cancer and metastasis in both normoxic and hypoxic condition so lactate dehydrogenase can be a good prognostic biomarker. Furthermore, monocarboxylic transporter which is the main lactate transporter can be good target in therapeutic studies. Glycolysis enzymes are valuable enzymes in cancer and metastasis diagnosis and can be used as therapeutic targets in cancer treatment. Designing a diagnostic and prognostic profile for cancer metastasis seems to be possible base on glycolysis enzymes and glucose transporters. Also, glucose metabolism enzymes and agents can give us a clear vision in estimating cancer metastasis. We can promote a panel of genes that detect genetic changes in glucose metabolism agents to diagnose cancer metastasis.

Analysis of the expression, function, prognosis and co-expression genes of DDX20 in gastric cancer

DDX20 (DEAD-box polypeptide 20) is implicated in many cellular processes involving alteration of RNA secondary structure. The role of DDX20 in gastric cancer is still unknown. In the research, the expression of DDX20 and the functional roles of DDX20 in gastric cancer were detected. The increased DDX20 expression in gastric cancer tissue compared with normal gastric tissue was observed. Functional experiments indicated that DDX20 promoted gastric cancer MGC-803 and AGS cells growth, migration, and invasion in vitro. Surprisingly, survival analysis showed that high expression of DDX20 is a favorable prognostic factor for patients with gastric cancer. In addition, enrichment analysis revealed that there is a positive correlation between DDX20 expression and T cell activation in gastric cancer. but not in normal gastric tissues. Furthermore, we found that DDX20 expression level has significant positive correlations with activated CD8 + T cells and activated CD4 + T cells in gastric cancer. Therefore, we hypothesize that the prognostic role of DDX20 in gastric cancer patients may be due to patients with high DDX20 expression contained better immune activation. Taken together, these findings suggest that DDX20 can promote the progression of gastric cancer in vitro and its prognostic value in gastric cancer may be related to many factors, including immune activation.

A methylation-based mRNA signature predicts survival in patients with gastric cancer

Background

Evidence suggests that altered DNA methylation plays a causative role in the occurrence, progression and prognosis of gastric cancer (GC). Thus, methylated-differentially expressed genes (MDEGs) could potentially serve as biomarkers and therapeutic targets in GC.

Methods

Four genomics profiling datasets were used to identify MDEGs. Gene Ontology enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis were used to explore the biological roles of MDEGs in GC. Univariate Cox and LASSO analysis were used to identify survival-related MDEGs and to construct a MDEGs-based signature. The prognostic performance was evaluated in two independent cohorts.

Results

We identified a total of 255 MDEGs, including 192 hypermethylation-low expression and 63 Hypomethylation-high expression genes. The univariate Cox regression analysis showed that 83 MDEGs were associated with overall survival. Further we constructed an eight-MDEGs signature that was independent predictive of prognosis in the training cohort. By applying the eight-MDEGs signature, patients in the training cohort could be categorized into high-risk or low-risk subgroup with significantly different overall survival (HR = 2.62, 95% CI 1.71–4.02, P < 0.0001). The prognostic value of the eight-MDEGs signature was confirmed in another independent GEO cohort (HR = 1.35, 95% CI 1.03–1.78, P = 0.0302) and TCGA-GC cohort (HR = 1.85, 95% CI 1.16–2.94, P = 0.0084). Multivariate cox regression analysis proved the eight-MDEGs signature was an independent prognostic factor for GC.

Conclusion

We have thus established an innovative eight-MDEGs signature that is predictive of overall survival and could be a potentially useful guide for personalized treatment of GC patients.

Comprehensive analysis reveals CTHRC1, SERPINE1, VCAN and UPK1B as the novel prognostic markers in gastric cancer

Background

Gastric cancer (GC) is one of the most common malignant diseases worldwide, the incidence and mortality for GC is still high, thus it is urgently important to identify the effective and reliable biomarkers to evaluate GC and the underlying molecular events.

Methods

The study integrated four Gene Expression Omnibus (GEO) profile datasets and The Cancer Genome Atlas (TCGA) dataset to screen differentially expressed genes (DEGs), screened key genes by performing the Kaplan-Meier analysis, univariate and multivariate-cox analysis. Further analysis were performed to evaluate and validate the prognostic value of the key genes based on TCGA database and online websites. In addition, mechanism analysis of the key genes was performed thought biological processes and KEGG pathway analysis.

Results

In the study, 192 DEGs (92 up-regulated and 100 down-regulated) were identified from the GEO and TCGA datasets. Next, gene ontology (GO) for DEGs focused primarily on cell adhesion, extracellular region and extracellular matrix structural constituent. Then four significant key genes were screened by performed the Kaplan-Meier analysis, univariate and multivariate-cox analysis. By using Kaplan-Meier plotter and OncoLnc, the expression level was associated with a worse prognosis. In addition, the area under curve (AUC) for time-dependent receiver operating characteristic (ROC) indicated a moderate diagnostic value. Furthermore, the expression of collagen triple helix repeat containing 1 (CTHRC1), serpin family E member 1 (SERPINE1), Versican (VCAN) was associated with tumor size, Uroplakin 1B (UPK1B) expression was associated with distant metastasis. Finally, multiple biological processes and signaling pathway associated with key genes revealed the underlying mechanism in GC.

Conclusions

Taken together, CTHRC1, SERPINE1, VCAN, UPK1B were novel potential prognostic molecular markers for GC, which acted as oncogene to promote the development of GC.

MUC16 C-terminal binding with ALDOC disrupts the ability of ALDOC to sense glucose and promotes gallbladder carcinoma growth

The MUC16 C-terminal (MUC16c) level is associated with tumor serum CA-125 levels, however, the roles remain unclear in gallbladder carcinoma (GBC). In this study, we found that MUC16c promoted glucose uptake and glycolysis for GBC cell proliferation. Mass spectrometry analysis suggested that MUC16c could combine with aldolase. The ALDOC mRNA and protein are overexpressed in GBC tumors. The IHC results also showed the consistent up-regulation of. ALDOC and MUC16c level in GBC tumor tissues than in peritumor tissues. We determined that MUC16c combining with ALDOC promoted ALDOC protein stability and disrupted the ability of ALDOC sensing glucose deficiency, which activated AMPK pathway and increased GBC cell proliferation. ALDOC knockdown significantly inhibited the glucose uptake and glycolysis induced by MUC16c. Our study established important roles of MUC16c promoting GBC cell glycolysis and proliferation and revealed the underlying mechanism of CA-125-related heavy tumor metabolic burden in GBC.

Identifying multiple collagen gene family members as potential gastric cancer biomarkers using integrated bioinformatics analysis

Background

Gastric cancer is one of the most common malignant cancers worldwide. Despite substantial developments in therapeutic strategies, the five-year survival rate remains low. Therefore, novel biomarkers and therapeutic targets involved in the progression of gastric tumors need to be identified.

Methods

We obtained the mRNA microarray datasets GSE65801, GSE54129 and GSE79973 from the Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to analyze DEG pathways and functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape to obtain the protein-protein interaction (PPI) network. Next, we validated the hub gene expression levels using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA), and conducted stage expression and survival analysis.

Results

From the three microarray datasets, we identified nine major hub genes: COL1A1, COL1A2, COL3A1, COL5A2, COL4A1, FN1, COL5A1, COL4A2, and COL6A3.

Conclusion

Our study identified COL1A1 and COL1A2 as potential gastric cancer prognostic biomarkers.

Could CTSK and COL4A2 be specific biomarkers of poor prognosis for patients with gastric cancer in Asia?-a microarray analysis based on regional population

Background

In the purpose of identifying reliable biomarkers for evaluating prognosis, monitoring recurrence and exploring new therapeutic targets, it is quite necessary to screen for the genetic changes and potential molecular mechanisms of the occurrence and development of gastric cancer (GC) from the aspects of race and region.

Methods

Target datasets were retrieved from Gene Expression Omnibus (GEO) database with "gastric cancer" as the key word, and corresponding data was downloaded. The differentially expressed genes (DEGs) were obtained by using limma R package, and the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for DEGs were analyzed in Enirchr database. Protein-protein interaction (PPI) network and molecular module were also constructed through STRING database and Cytoscape software. Survival analyses were completed for DEGs in GEO and Kaplan-Meier plotter database via cross validation. Finally, the correlation between gene expression and the infiltration cell levels in tumor microenvironment (TME) was explored based on the tumor immune estimation resource (TIMER) database.

Results

Five GC-related microarray datasets were selected and used for differential analysis, and 222 DEGs were identified. GO analyses of DEGs were mainly involved in cell metabolism and the formation of extracellular matrix (ECM). The top enriched pathways of DEGs were protein digestion and absorption, ECM-receptor interaction, focal adhesion (FA), PI3K-Akt signaling pathway. Survival analyses of DEGs revealed that the expression levels of CTSK and COL4A2 were significantly associated with poor prognosis of GC patients in Asian. Specifically, the high expression of CTSK had a closely related to the infiltration level of inflammatory cell in TME.

Conclusions

CTSK and COL4A2 could play a critical role in the pathogenesis of GC and act as the promising prognostic biomarkers. CTSK could induce the formation of immunosuppressive TME and promote the immune escape of GC cells.

MicroRNA and mRNA analysis of angiotensin II-induced renal artery endothelial cell dysfunction

Continuous activation of angiotensin II (Ang II) induces renal vascular endothelial dysfunction, inflammation and oxidative stress, all of which may contribute to renal damage. MicroRNAs (miRs/miRNAs) play a crucial regulatory role in the pathogenesis of hypertensive nephropathy (HN). The present study aimed to assess the differential expression profiles of potential candidate genes involved in Ang II-induced rat renal artery endothelial cell (RRAEC) dysfunction and explore their possible functions. In the present study, the changes in energy metabolism and autophagy function in RRAECs were evaluated using the Seahorse XF Glycolysis Stress Test and dansylcadaverine/transmission electron microscopy following exposure to Ang II. Subsequently, mRNA-miRNA sequencing experiments were performed to determine the differential expression profiles of mRNAs and miRNAs. Integrated bioinformatics analysis was applied to further explore the molecular mechanisms of Ang II on endothelial injury induced by Ang II. The present data supported the notion that Ang II upregulated glycolysis levels and promoted autophagy activation in RRAECs. The sequencing data demonstrated that 443 mRNAs and 58 miRNAs were differentially expressed (DE) in response to Ang II exposure, where 66 mRNAs and 55 miRNAs were upregulated, while 377 mRNAs and 3 miRNAs were downregulated (fold change >1.5 or <0.67; P<0.05). Functional analysis indicated that DE mRNA and DE miRNA target genes were mainly associated with cell metabolism (metabolic pathways), differentiation (Th1 and Th2 cell differentiation), autophagy (autophagy-animal and autophagy-other) and repair (RNA-repair). To the best of the authors' knowledge, this is the first report on mRNA-miRNA integrated profiles of Ang II-induced RRAECs. The present results provided evidence suggesting that the miRNA-mediated effect on the ‘mTOR signaling pathway’ might play a role in Ang II-induced RRAEC injury by driving glycolysis and autophagy activation. Targeting miRNAs and their associated pathways may provide valuable insight into the clinical management of HN and may improve patient outcome.

Identification of Hub Genes and Pathways in Gastric Adenocarcinoma Based on Bioinformatics Analysis

BACKGROUND Gastric adenocarcinoma accounts for 95% of all gastric malignant tumors. The purpose of this research was to identify differentially expressed genes (DEGs) of gastric adenocarcinoma by use of bioinformatics methods. MATERIAL AND METHODS The gene microarray datasets of GSE103236, GSE79973, and GSE29998 were imported from the GEO database, containing 70 gastric adenocarcinoma samples and 68 matched normal samples. Gene ontology (GO) and KEGG analysis were applied to screened DEGs; Cytoscape software was used for constructing protein-protein interaction (PPI) networks and to perform module analysis of the DEGs. UALCAN was used for prognostic analysis. RESULTS We identified 2909 upregulated DEGs (uDEGs) and 7106 downregulated DEGs (dDEGs) of gastric adenocarcinoma. The GO analysis showed uDEGs were enriched in skeletal system development, cell adhesion, and biological adhesion. KEGG pathway analysis showed uDEGs were enriched in ECM-receptor interaction, focal adhesion, and Cytokine-cytokine receptor interaction. The top 10 hub genes - COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 - were distinguished from the PPI network. These 10 hub genes were shown to be significantly upregulated in gastric adenocarcinoma tissues in GEPIA. Prognostic analysis of the 10 hub genes via UALCAN showed that the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduced the survival time of gastric adenocarcinoma patients. Module analysis revealed that gastric adenocarcinoma was related to 2 pathways: including focal adhesion signaling and ECM-receptor interaction. CONCLUSIONS This research distinguished hub genes and relevant signal pathways, which contributes to our understanding of the molecular mechanisms, and could be used as diagnostic indicators and therapeutic biomarkers for gastric adenocarcinoma.

MFAP2 is overexpressed in gastric cancer and promotes motility via the MFAP2/integrin α5β1/FAK/ERK pathway

Gastric cancer (GC) is one of the most common malignancies and its prognosis is extremely poor. This study identifies a novel oncogene, microfibrillar-associated protein 2 (MFAP2) in GC. With integrative reanalysis of transcriptomic data, we found MFAP2 as a GC prognosis-related gene. And the aberrant expression of MFAP2 was explored in GC samples. Subsequent experiments indicated that silencing and exogenous MFAP2 could affect motility of cancer cells. The inhibition of silencing MFAP2 could be rescued by another FAK activator, fibronectin. This process is probably through affecting the activation of focal adhesion process via modulating ITGB1 and ITGA5. MFAP2 regulated integrin expression through ERK1/2 activation. Silencing MFAP2 by shRNA inhibited tumorigenicity and metastasis in nude mice. We also revealed that MFAP2 is a novel target of microRNA-29, and miR-29/MFAP2/integrin α5β1/FAK/ERK1/2 could be an important oncogenic pathway in GC progression. In conclusion, our data identified MFAP2 as a novel oncogene in GC and revealed that miR-29/MFAP2/integrin α5β1/FAK/ERK1/2 could be an important oncogenic pathway in GC progression.

High expression of fibronectin 1 indicates poor prognosis in gastric cancer

Fibronectin 1 (FN1) is involved in the occurrence and development of various tumors and is upregulated in multiple cancer types. FN1 has been demonstrated to promote cell proliferation and migration in gastric cancer cell lines. However, the relationship between the expression of FN1 and clinicopathological factors and prognosis is not clear in gastric cancer (GC). The aim of the present study was to investigate the association between FN1 expression and clinicopathology and prognosis of gastric cancer. In this study, 17 publicly available GC cohorts (n=2,376) with gene expression data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine databases were tested. In addition, FN1 protein expression was validated by immunohistochemistry in a separate cohort (n=190). The meta-analysis results demonstrated an increase in FN1 expression at the protein and mRNA level in GC tissues, and the FN1 gene was highly expressed at the mRNA level in the advanced T stage (T2 + T3 + T4) group compared with that in the early T stage (T1) group. In addition, the expression of epithelial FN1 at the protein level was positively correlated with tumor size. FN1 expression at the protein and mRNA level was a predictor of poor prognosis following radical resection of GC. In conclusion, the expression of FN1 in GC tissues is upregulated compared with adjacent normal tissues, and it is a potential biomarker of poor prognosis in patients with GC.

Downregulation of NUSAP1 suppresses cell proliferation, migration, and invasion via inhibiting mTORC1 signalling pathway in gastric cancer

Gastric cancer (GC) is one of the most common causes of cancer-related death worldwide, and outstanding biomarkers for therapeutic targets or predicting GC survival are still lacking. Increasing evidence indicated that nucleolar and spindle associated protein 1 (NUSAP1) involved in regulating the progression of various cancers; however, its specific role in GC remained unclear. In this study, we found that NUSAP1 was upregulated in the GC tissues and cell lines via analysing data from The Cancer Genome Atlas (TCGA), gene expression omnibus (GEO), qRT-PCR, and western blot assays. Patients with high NUSAP1 expression levels showed shorter free-progression survival (FPS), larger tumour size, and higher lymphatic metastasis rate compared with those with low NUSAP1 expression. Further functional experiments revealed knockdown of NUSAP1 could inhibit the growth, migration, and invasion of GC cells in vitro and vivo. Additionally, silencing NUSAP1 induced G0/G1 phase arrest, apoptosis, and suppressed the epithelial-mesenchymal transition (EMT) process. Finally, we performed gene set enrichment analysis (GSEA) and observed NUSAP1 was positive with mTORC1 signalling pathway, which was verified by the subsequent immunoblotting. In conclusion, our findings suggested that NUSAP1 contributed to GC progression and may act as a potential therapeutic target for GC. SIGNIFICANCE OF THE STUDY: Our results firstly illuminated that NUSAP1 expression was significantly upregulated in GC tissues and predicted poor FPS. Silencing it could attenuate GC progression via inhibiting mTORC1 signalling pathway. Hence, NUSAP1 may act as a promising therapy target for GC.

Identification of the lncRNA-miRNA-mRNA network associated with gastric cancer via integrated bioinformatics analysis

The aim of the present study was to investigate the long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulatory network in gastric cancer (GC) using bioinformatics analysis. Two mRNA gene expression profiles, GSE79973 and GSE54129, and two miRNA expression profiles, GSE93415 and GSE78091, were downloaded from the Gene Expression Omnibus database. The differentially expressed mRNAs (DEMs) and the differentially expressed miRNAs (DEMis) were merged separately. Gene ontology and pathway enrichment analysis were conducted using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was then constructed and the 10 top hub genes in the network were analyzed using the Search Tool for the Retrieval of Interacting Genes. The lncRNA-miRNA-mRNA networks were visualized using Cytoscape software. As a result, 158 shared DEMs (40 upregulated and 118 downregulated) were identified from two mRNA datasets. A total of 30 upregulated miRNAs and 1 downregulated miRNA functioned as DEMis. The PPI network consisted of 129 nodes and 572 interactions. The 10 top hub genes were selected by degree using Cytohubba, including Jun proto-oncogene, mitogen-activated protein kinase (MAPK)3, transforming growth factor-β1, Fos proto-oncogene, AP-1 transcription factor subunit, interleukin (IL)-8, MAPK1, RELA proto-oncogene nuclear factor-κB subunit, interferon regulatory factor 7, ubiquitin like modifier and vascular endothelial growth factor A. In the lncRNA-miRNA-mRNA network, a total of 1,215 regulatory associations were constructed using Cytoscape. In conclusion, the present study provides a novel perspective of the molecular mechanisms underlying GC by identifying the lncRNA-miRNA-mRNA regulatory network via bioinformatics analysis.

Papillary Renal Cell Carcinomas Rewire Glutathione Metabolism and Are Deficient in Both Anabolic Glucose Synthesis and Oxidative Phosphorylation

Papillary renal cell carcinoma (pRCC) is a malignant kidney cancer with a prevalence of 7–20% of all renal tumors. Proteome and metabolome profiles of 19 pRCC and patient-matched healthy kidney controls were used to elucidate the regulation of metabolic pathways and the underlying molecular mechanisms. Glutathione (GSH), a main reactive oxygen species (ROS) scavenger, was highly increased and can be regarded as a new hallmark in this malignancy. Isotope tracing of pRCC derived cell lines revealed an increased de novo synthesis rate of GSH, based on glutamine consumption. Furthermore, profound downregulation of gluconeogenesis and oxidative phosphorylation was observed at the protein level. In contrast, analysis of the The Cancer Genome Atlas (TCGA) papillary RCC cohort revealed no significant change in transcripts encoding oxidative phosphorylation compared to normal kidney tissue, highlighting the importance of proteomic profiling. The molecular characteristics of pRCC are increased GSH synthesis to cope with ROS stress, deficient anabolic glucose synthesis, and compromised oxidative phosphorylation, which could potentially be exploited in innovative anti-cancer strategies.