Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

Lipopolysaccharide-binding protein (LBP): a prognostic biomarker for gastric cancer linked to immune infiltration

BACKGROUND

Gastric cancer, characterized by rising global incidence and mortality, faces significant challenges due to the lack of effective early detection methods, delaying timely interventions and underscoring the need for novel biomarkers. Lipopolysaccharide-binding protein (LBP), implicated in cancers such as lung, colon, and cervical cancer, has emerged as a promising candidate. However, its specific roles and mechanisms in gastric cancer remain unclear, necessitating further investigation.

METHODS

This study utilized data from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the Human Protein Atlas (HPA) to assess LBP mRNA and protein expression levels in gastric cancer patients and explore their associations with clinical outcomes. Analytical techniques included volcano plots, protein-protein interaction networks, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and immune infiltration assessments. Furthermore, lentiviral vectors containing interference sequences targeting LBP were used to manipulate its expression in AGS and HGC-27 gastric cancer cell lines, enabling the analysis of gene knockdown effects on malignant behaviors. Western blotting (WB) was performed to validate the impact of LBP knockdown on the expression of key signaling pathway proteins.

RESULTS

Our pan-cancer comparative analysis across 33 cancer types revealed significant upregulation of LBP in gastric cancer, with diagnostic ROC curve analysis yielding an AUC of 0.765. Univariate and multivariate Cox regression analyses revealed that high LBP expression was inversely related to patient survival. Additionally, immune infiltration and functional enrichment analyses revealed the involvement of LBP in pathways crucial to cancer development, such as immune response modulation and lipid metabolism. LBP knockdown in gastric cancer cell lines reduced proliferation, migration, and invasion. WB confirmed decreased expression of P65, P-P65, STAT3, and P-STAT3 upon LBP knockdown.

CONCLUSION

LBP is intricately linked to gastric cancer pathogenesis; it influences cell proliferation, migration, and invasion, thereby representing a valuable prognostic and diagnostic biomarker. This study not only highlights the potential of LBP as a therapeutic target but also provides the groundwork for future investigations into its mechanistic pathways in gastric cancer.

CLINICAL TRIAL NUMBER

Not applicable. I would like to clarify that our research does not fall under clinical studies and therefore does not involve ethical concerns related to human or animal subjects. The cells used in this study are established cell lines purchased from a certified biotechnology company. All experimental procedures comply with standard research protocols and guidelines for cell line studies.

Unraveling the Core Components and Critical Targets of Houttuynia cordata Thunb. in Treating Non-small Cell Lung Cancer through Network Pharmacology and Multi-omics Analysis

OBJECTIVE

This study aimed to preliminary explore the molecular mechanisms of Houttuynia cordata Thunb. (H. cordata; Saururaceae) in treating non-small cell lung cancer (NSCLC), with the goal of screening drug potential targets for clinical drug development.

METHODS

This study employed a multi-omics and multi-source data integration approach to identify potential therapeutic targets of H. cordata against NSCLC from the TCMSP database, GEO database, BioGPS database, Metascape database, and others. Meanwhile, target localization was performed, and its possible mechanisms of action were predicted. Furthermore, dynamics simulations and molecular docking were used for verification. Multi-omics analysis was used to confirm the selected key genes' efficacy in treating NSCLC.

RESULTS

A total of 31 potential therapeutic targets, 8 key genes, and 5 core components of H. cordata against NSCLC were screened out. These potential therapeutic targets played a therapeutic role mainly by regulating lipid and atherosclerosis, the TNF signaling pathway, the IL-17 signaling pathway, and others. Molecular docking indicated a stable combination between MMP9 and quercetin. Finally, through multi-omics analysis, it was found that the expression of some key genes was closely related not only to the progression and prognosis of NSCLC but also to the level of immune infiltration.

CONCLUSION

Through comprehensive network pharmacology and multi-omics analysis, this study predicts that the core components of H. cordata play a role in treating NSCLC by regulating lipid and atherosclerosis, as well as the TNF signaling pathway. Among them, the anti-NSCLC activity of isoramanone is reported for the first time.

APOE expression in papillary thyroid carcinoma: Influencing tumor progression and macrophage polarization

BACKGROUND

As metastatic papillary thyroid carcinoma becomes increasingly challenging to treat, immunotherapy has emerged as a new research direction. Tumor-associated macrophages (TAMs) influence the occurrence, invasion, and metastasis of tumors. Apolipoprotein E (APOE) can regulate the polarization changes of macrophages and participate in the remodeling of the tumor microenvironment. However, the role of APOE in regulating the polarization and biological functions of TAMs in papillary thyroid carcinoma (PTC) remains unclear, as it acts as a dual biomarker.

METHODS

We probed APOE expression in PTC tissues using immunohistochemical staining. A cell co-culture model was established where different APOE-expressing K1 cells were co-cultured with THP-1-derived M0 macrophages. An in-depth analysis of macrophage polarization behavior was performed using real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, and western blotting. Subsequently, the impact of APOE-regulated macrophages on tumor cell behavior, especially proliferation, migration, and invasion, was evaluated utilizing IncuCyte ZOOM system, flow cytometry, colony formation, and scratch experiments. Finally, we used a xenograft model to confirm the effects of APOE on PTC tumorigenesis.

RESULTS

Tumor dimensions, stage, and lymphatic metastases were significantly associated with increased APOE expression in PTC tissues. K1 cells were markedly limited in their proliferation, migration, and invasion abilities when APOE expression was silenced, a process mediated by the PI3K/Akt/NF-κB signaling axis. Moreover, APOE is a key facilitator of the enhancement of the anti-inflammatory cytokines IL-10 and TGF-β1. In PTC cellular models, APOE contributed to the phenotypic shift of THP-1 derived macrophages towards an M2 phenotypic polarization, predominantly through the modulation of IL-10. Furthermore, in vivo studies involving athymic nude mice have demonstrated pivotal role of APOE in tumor progression and the induction of M2-like TAM polarization.

CONCLUSION

Our results elucidated that APOE could promote the shift of TAMs from M0-type to M2-type polarization by regulating inflammatory factors expressions in K1 cell through the PI3K/Akt/NF-κB pathway. These findings are crucial for understanding the molecular mechanisms underlying PTC pathogenesis and for developing immunological drugs to treat this disease.

CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling

OBJECTIVE

Gastric cancer (GC) is one of the most common malignancies worldwide and it is considered the fourth most common cause of cancer death. This study aimed to find critical genes/pathways in GC pathogenesis to be used as biomarkers or therapeutic targets.

METHODS

Differentially expressed genes were explored between human gastric cancerous and noncancerous tissues, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses were done. Hub genes were identified based on the protein-protein interaction network constructed in the STRING database with Cytoscape software. The hub genes were selected for further investigation using GEPIA2 and DrugBank databases.

RESULTS

Ten overexpressed hub genes in GC were identified in the current study, including FN1, TP53, IL-6, CXCL5, ELN, ADAMTS2, WISP1, MMP2, CTGF, and THBS1. The study demonstrated the PI3K-Akt pathway's central involvement in GC, with pronounced alterations in essential components. Survival analysis revealed significant correlations between CTGF, FN1, IL-6, THBS1, and WISP1 overexpression and reduced overall survival times in GC patients.

CONCLUSION

A mutual interplay emerged, where PI3K-Akt signaling could upregulate certain genes, forming feedback loops and intensifying cancer phenotypes. The interconnected overexpression of genes and the PI3K-Akt pathway fosters gastric tumorigenesis, suggesting therapeutic potential. DrugBank analysis identified limited FDA-approved drugs, advocating for further exploration while targeting these hub genes could reshape GC treatment. The identified genes could be novel diagnostic/prognostic biomarkers or potential therapeutic targets for GC, but further clinical validation is required.

Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells

Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8- conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.

Computational Approach to Identify the Key Genes for Invasive Lobular Carcinoma (ILC) Diagnosis and Therapies

Invasive Lobular Carcinoma (ILC) is a common form of breast cancer that begins in milk-producing glands lobules and spreads to other parts of the breast. According to the American Cancer Society, about 10–15% of breast cancer cases are ILC. ILC risk rises with age. The number of deaths caused by this cancer each year can be decreased through early diagnosis and if accurate therapy is given. However, diagnosis of ILC is difficult due to its development pattern as it grows as single file strands and not as lumps. Treatments of ILC involve chemotherapy, hormonal therapy and radiation therapy. Drugs that are being used for ILC, are commonly used to treat all types of breast cancer and there are no specific drugs that target receptors of ILC are available. Microarray technology’s emergence helps in finding the differentially expressed genes (DEGs) in malignant cells. From the DEGs, highly interacting genes were identified using the online tool, string. Seven key genes were identified based on the interaction and they are FN1, CDKN2A, COL1A1, COL3A1, COL11A1, LEF1 and IL1B. Thus, the drugs targeting these biomarkers were identified by doing molecular docking using the tool Autodock and molecular dynamic (MD) simulation using the tool iMODs. The response of the identified drugs to the ILC cell line was compared with the control drugs by in silico pharmacogenomic analysis and it was found that the identified drugs have a good response to the ILC cell line.

Novel chromium (III)-based compound for inhibition of oxaliplatin-resistant colorectal cancer progression

Colorectal cancer (CRC) ranks as the third leading cause of cancer-related mortality worldwide. The current standard of care includes systemic chemotherapy with cytotoxic agents, offering palliative relief for severe CRC cases and serving as the primary therapy for metastatic recurrence. However, the development of chemoresistance poses a substantial obstacle in the realm of chemotherapy. This study delved into the potential of a novel chromium (III)-based compound, hexaacetotetraaquadihydroxochromium (III) diiron (III) nitrate, for CRC treatment. The therapeutic promise of this innovative chromium (III)-based compound was explored by utilizing LoVo colon cancer cells and an in-vivo mouse model of CRC. Various dosages of the compound were administered to LoVo parental cells and LoVo oxaliplatin-resistant cells. Findings unveiled that a concentration of 2000 μg/mL of the chromium (III) compound significantly inhibited mesenchymal transition and the migratory and invasive properties of LoVo oxaliplatin-resistant cells. This novel chromium (III)-based compound also demonstrated similar efficacy in other different CRC cell lines. The tumor growth was in the in-vivo mouse model was reduced by this compound. Moreover, the chromium (III)-based compound induced apoptosis by triggering the endoplasmic reticulum (ER) stress pathway in LoVo oxaliplatin-resistant cells. This study illuminates the capacity of the novel chromium (III)-based compound to impede the progression and growth of chemotherapy-resistant CRC. This discovery instills confidence in the potential of this compound as a therapeutic agent for CRC, even in the face of drug resistance. It holds the promise of serving as a valuable asset in the future treatment of chemotherapy-resistant CRC.

Development and verification of a newly established cuproptosis-associated lncRNA model for predicting overall survival in uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.

Methods

Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.

Results

By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.

Conclusions

We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.

DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.

High FN1 expression correlates with gastric cancer progression

OBJECTIVE

High stromal ratio of gastric cancer is associated with a poor prognosis. Fibronectin 1(FN1) is the main component of gastric cancer stroma. The focus of this research was to investigate the FN1 express pattern, the connection between FN1 expression, clinicopathological parameters, survival, and mismatch repair genes (MMR) or immune checkpoints in gastric cancer patients.

METHODS

Eighty-six paired stomach cancer tissues, neighboring normal tissues, and eight independent gastric cancer tissues were used to create 180 points tissue microarrays. The association between epithelial fibronectin (E-FN1), stromal fibronectin (S-FN1) expression, and clinical characteristics was analyzed using the chi-square test or Fisher's exact test, and the survival analysis curve was analyzed using the log-rank test, followed by univariate and multivariate Cox regression. The correlation between FN1 and MMR or immune checkpoints was analyzed by Spearman correlation.

RESULTS

FN1 is mainly expressed in gastric cancer tissues, with low or no expression in adjacent normal tissues. In tumor tissues, FN1 is mostly distributed in the stroma. High E-FN1 expression was associated with a decreased overall survival (OS), while S-FN1 expression did not. High S-FN1 expression correlated with older age (P<0.001), higher pathological grade (P<0.001), pathological type (P<0.001), vessel/lymphatic invasion (P<0.001), advanced T stage (P=0.001), N stage (P=0.01), and worse TNM stage(P = 0.033). FN1 expression was not associated with MMR or immune checkpoints (MLH1, MSH2, MSH6, PDL1, PD1, PMS2, and CD8).

CONCLUSIONS

High E-FN1 expression predicted poor OS, while S-FN1 is associated with gastric cancer progression.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis

Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN (p = 8 × 10-12), THBS2 (p = 1.2 × 10-6), CTHRC1 (p = 1.1 × 10-4), SULF1 (p = 3.8 × 10-4), COL5A1 (p = 1.3 × 10-4), COL10A1 (p = 5.7 × 10-4), COL12A1 (p = 2 × 10-3) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (p-value range 4.82 × 10-7-1.63 × 10-13). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets.

Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.

Novel CircRNAs in Hub ceRNA Axis Regulate Gastric Cancer Prognosis and Microenvironment

Gastric cancer (GC) is one of the most prevalent malignancies with an unfavorable survival rate. Immunotherapy may contribute to a better prognosis. However, several phase III trials failed. Circular RNA (circRNA) is a novel type of non-coding RNA, plays a vital role in the progression of tumors. The expression and function of circRNA in the GC immune microenvironment remain obscure. In this study, we utilized a bioinformatic analysis to construct a circRNA/microRNA (miRNA)/messenger RNA (mRNA) network involved in the progression and prognosis of GC. CircRNA DYRK1A_017, circRNA FLNA_118, miR-6512-3p, miR-6270-5p, and VCAN were identified as the key molecules in the hub regulatory axis. Dysregulation of this axis contributed to the cancer-associated signaling pathways (epithelial-mesenchymal transition [EMT], Nuclear factor kappa β-Tumor necrosis factor-α (NFκβ-TNFα) signaling, and angiogenesis) and aberrant immune microenvironment (infiltration by tumor associated macrophage, regulatory T cell, and mast cell). More importantly, the immunosuppressive tumor microenvironment may reveal the mechanism of novel circRNAs in tumors and serve as the target of immunotherapy.

Identification of potential genes related to breast cancer brain metastasis in breast cancer patients

Brain metastases (BMs) usually develop in breast cancer (BC) patients. Thus, the molecular mechanisms of breast cancer brain metastasis (BCBM) are of great importance in designing therapeutic strategies to treat or prevent BCBM. The present study attempted to identify novel diagnostic and prognostic biomarkers of BCBM. Two datasets (GSE125989 and GSE100534) were obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) in cases of BC with and without brain metastasis (BM). A total of 146 overlapping DEGs, including 103 up-regulated and 43 down-regulated genes, were identified. Functional enrichment analysis showed that these DEGs were mainly enriched for functions including extracellular matrix (ECM) organization and collagen catabolic fibril organization. Using protein-protein interaction (PPI) and principal component analysis (PCA) analysis, we identified ten key genes, including LAMA4, COL1A1, COL5A2, COL3A1, COL4A1, COL5A1, COL5A3, COL6A3, COL6A2, and COL6A1. Additionally, COL5A1, COL4A1, COL1A1, COL6A1, COL6A2, and COL6A3 were significantly associated with the overall survival of BC patients. Furthermore, COL6A3, COL5A1, and COL4A1 were potentially correlated with BCBM in human epidermal growth factor 2 (HER2) expression. Additionally, the miR-29 family might participate in the process of metastasis by modulating the cancer microenvironment. Based on datasets in the GEO database, several DEGs have been identified as playing potentially important roles in BCBM in BC patients.

Monocarboxylate Transporter-2 Expression Restricts Tumor Growth in a Murine Model of Lung Cancer: A Multi-Omic Analysis

Monocarboxylate transporter 2 (MCT2) is a major high-affinity pyruvate transporter encoded by the SLC16A7 gene, and is associated with glucose metabolism and cancer. Changes in the gut microbiota and host immune system are associated with many diseases, including cancer. Using conditionally expressed MCT2 in mice and the TC1 lung carcinoma model, we examined the effects of MCT2 on lung cancer tumor growth and local invasion, while also evaluating potential effects on fecal microbiome, plasma metabolome, and bulk RNA-sequencing of tumor macrophages. Conditional MCT2 mice were generated in our laboratory using MCT2^loxP mouse intercrossed with mCre-Tg mouse to generate MCT2^loxP/loxP; Cre⁺ mouse (MCT2 KO). Male MCT2 KO mice (8 weeks old) were treated with tamoxifen (0.18 mg/g BW) KO or vehicle (CO), and then injected with mouse lung carcinoma TC1 cells (10 × 10⁵/mouse) in the left flank. Body weight, tumor size and weight, and local tumor invasion were assessed. Fecal DNA samples were extracted using PowerFecal kits and bacterial 16S rRNA amplicons were also performed. Fecal and plasma samples were used for GC−MS Polar, as well as non-targeted UHPLC-MS/MS, and tumor-associated macrophages (TAMs) were subjected to bulk RNAseq. Tamoxifen-treated MCT2 KO mice showed significantly higher tumor weight and size, as well as evidence of local invasion beyond the capsule compared with the controls. PCoA and hierarchical clustering analyses of the fecal and plasma metabolomics, as well as microbiota, revealed a distinct separation between the two groups. KO TAMs showed distinct metabolic pathways including the Acetyl-coA metabolic process, activation of immune response, b-cell activation and differentiation, cAMP-mediated signaling, glucose and glutamate processes, and T-cell differentiation and response to oxidative stress. Multi-Omic approaches reveal a substantial role for MCT2 in the host response to TC1 lung carcinoma that may involve alterations in the gut and systemic metabolome, along with TAM-related metabolic pathway. These findings provide initial opportunities for potential delineation of oncometabolic immunomodulatory therapeutic approaches.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.

Analysis of Gastric Cancer Transcriptome Allows the Identification of Histotype Specific Molecular Signatures With Prognostic Potential

Gastric cancer is the fifth most common malignancy but the third leading cause of cancer-associated mortality worldwide. Therapy for gastric cancer remain largely suboptimal making the identification of novel therapeutic targets an urgent medical need. In the present study we have carried out a high-throughput sequencing of transcriptome expression in patients with gastric cancers. Twenty-four patients, among a series of 53, who underwent an attempt of curative surgery for gastric cancers in a single center, were enrolled. Patients were sub-grouped according to their histopathology into diffuse and intestinal types, and the transcriptome of the two subgroups assessed by RNAseq analysis and compared to the normal gastric mucosa. The results of this investigation demonstrated that the two histopathology phenotypes express two different patterns of gene expression. A total of 2,064 transcripts were differentially expressed between neoplastic and non-neoplastic tissues: 772 were specific for the intestinal type and 407 for the diffuse type. Only 885 transcripts were simultaneously differentially expressed by both tumors. The per pathway analysis demonstrated an enrichment of extracellular matrix and immune dysfunction in the intestinal type including CXCR2, CXCR1, FPR2, CARD14, EFNA2, AQ9, TRIP13, KLK11 and GHRL. At the univariate analysis reduced levels AQP9 was found to be a negative predictor of 4 years survival. In the diffuse type low levels CXCR2 and high levels of CARD14 mRNA were negative predictors of 4 years survival. In summary, we have identified a group of genes differentially regulated in the intestinal and diffuse histotypes of gastric cancers with AQP9, CARD14 and CXCR2 impacting on patients' prognosis, although CXCR2 is the only factor independently impacting overall survival.

Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.