Identification of key pathways and genes in colorectal cancer using bioinformatics analysis

TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers

Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.

The impact of SLC10A3 on prognosis and immune microenvironment in colorectal adenocarcinoma

BACKGROUND

SLC10A3, a gene upregulated in pan-cancer, lacks full understanding regarding its prognostic implications and association with immune infiltration in colorectal cancer (CRC). This study comprehensively analyzed SLC10A3 in CRC, evaluating its prognostic significance and influence on the tumor's immune microenvironment.

METHODS

Transcriptomic data from TCGA were obtained to compare SLC10A3 expression in both colorectal cancer (CRC) and normal tissues. Prognostic value was assessed for overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). DNA methylation patterns of SLC10A3 and correlation with DNA mismatch repair (MMR) were explored. Genetic alterations in SLC10A3 were scrutinized. The study also delved into the influence of SLC10A3 on the immune microenvironment of CRC, including immune cell infiltration and chemokines. Involvement of cancer-associated fibroblasts (CAFs) was explored. Methylation status of specific CpG islands in the SLC10A3 gene correlated with CRC patient prognosis. CRC tissue microarray was performed to verify the expression of SLC10A3 and its relationship with prognosis.

RESULTS

The research revealed that SLC10A3 is significantly upregulated in CRC and holds promise as a potential diagnostic marker. Elevated SLC10A3 expression was linked to poorer OS, DSS, and PFI. Methylation patterns of SLC10A3 displayed prognostic relevance, and genetic alterations in the gene were identified. SLC10A3 was shown to impact the immune microenvironment, with significant correlations observed between its expression and various immune cell types, chemokines, and markers associated with CAFs. Furthermore, an inverse relationship between SLC10A3 and MMR molecules was established. Methylation status of specific CpG islands within the SLC10A3 gene was associated with CRC patient prognosis. Tissue microarray showed that SLC10A3 was highly expressed in CRC and significantly correlated with poor prognosis.

CONCLUSION

The study underscores the importance of elevated SLC10A3 in CRC, associating it with decreased survival and immune infiltration, proposing it as a diagnostic biomarker and appealing immunotherapy target, given its significant overexpression and influence on the immune microenvironment and prognosis through methylation patterns.

Hederagenin Upregulates PTPN1 Expression in Aβ-Stimulated Neuronal Cells, Exerting Anti-Oxidative Stress and Anti-Apoptotic Activities

Alzheimer's disease (AD) is a prevalently neurodegenerative disease characterized by neuronal damage which is associated with amyloid-β (Aβ) accumulation. Hederagenin is a triterpenoid saponin, exerting anti-apoptotic, anti-oxidative, anti-inflammatory, anti-tumoral, and neuroprotective activities. However, its role in AD progression is still obscure. The aim of this study was to explore the influences of hederagenin on Aβ-caused neuronal injury in vitro. Neuronal cells were treated with Aβ25-35 (Aβ) to establish a cellular model of AD. Cell viability was assessed using cell counting kit-8 (CCK-8). Oxidative stress was evaluated by detecting reactive oxygen species (ROS) generation and superoxide dismutase (SOD) activity. Apoptosis was investigated using TUNEL staining and caspase-3 activity assays. Protein tyrosine phosphatase nonreceptor type 1 (PTPN1) was screened by bioinformatics analysis. Protein levels of PTPN1 and protein kinase B (Akt) were measured by western blotting. Hederagenin (2.5, 5, and 10 μM) alone did not affect viability of neuronal cells, but relieved Aβ-induced viability reduction. Hederagenin mitigated Aβ-induced increase in ROS accumulation and decrease in SOD activity. Hederagenin attenuated Aβ-induced increase in apoptotic rate and caspase-3 activity. PTPN1 was screened as a target of hederagenin against AD by bioinformatics analysis. Hederagenin treatment resisted Aβ-induced decrease in PTPN1 mRNA and protein levels in neuronal cells. PTPN1 silencing attenuated the suppressive functions of hederagenin in Aβ-stimulated oxidative stress and apoptosis. Hederagenin mitigated Aβ-induced Akt signaling inactivation by upregulating PTPN1 expression. In conclusion, hederagenin attenuates oxidative stress and apoptosis in neuronal cells stimulated with Aβ by promoting PTPN1/Akt signaling activation.

Identification of autophagy and angiogenesis modulators in colorectal cancer based on bioinformatics analysis

Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer-related death worldwide. The purpose of this study was to discover novel molecular pathways and potential prognosis biomarkers. To achieve this, we acquired five microarray datasets from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes between CRC and adjacent normal tissue samples and further validated them using The Cancer Genome Atlas (TCGA) database. Using various analytical approaches, including the construction of a competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as survival analysis, we identified key genes and pathways associated with the diagnosis and prognosis of CRC. We obtained a total of 185 differentially expressed genes, comprising 17 lncRNAs, 30 miRNAs, and 138 mRNAs. The ceRNA network consisted of 17 lncRNAs, 25 miRNAs, and 7 mRNAs. Among the 7 mRNAs involved in the ceRNA network, SLC7A5 and KRT80 were found to be upregulated, while ADIPOQ, CCBE1, KCNB1, CADM2, and CHRDL1 were downregulated in CRC. Further analysis revealed that ADIPOQ and SLC7A5 are involved in the AMPK and mTOR signaling pathway, respectively. In addition, survival analysis demonstrated a statistically significant relationship between ADIPOQ, SLC7A5, and overall survival rates in CRC patients. In conclusion, our findings suggest that downregulation of ADIPOQ and upregulation of SLC7A5 in tumor cells lead to increased mTORC1 activity, reduced autophagy, enhanced angiogenesis, and ultimately contribute to cancer progression and decreased survival in CRC patients.

Identification of genes critical for inducing ulcerative colitis and exploring their tumorigenic potential in human colorectal carcinoma

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease leading to continuous mucosal inflammation in the rectum extending proximally towards the colon. Chronic and/or recurrent UC is one of the critical predisposing mediators of the oncogenesis of human colorectal carcinoma (CRC). Perturbations of the differential expression of the UC-critical genes exert an intense impact on the neoplastic transformation of the affected tissue(s). Herein, a comprehensive exploration of the UC-critical genes from the transcriptomic profiles of UC patients was conducted to study the differential expression, functional enrichment, genomic alterations, signal transduction pathways, and immune infiltration level encountered by these genes concerning the oncogenesis of CRC. The study reveals that WFDC2, TTLL12, THRA, and EPHB3 play crucial roles as UC-CRC critical genes and are positively correlated with the molecular transformation of UC to CRC. Taken together, these genes can be used as potential biomarkers and therapeutic targets for combating UC-induced human CRC.

Short- and Long-Term Regulation of HuD: A Molecular Switch Mediated by Folic Acid?

The RNA-binding protein HuD has been shown to play a crucial role in gene regulation in the nervous system and is involved in various neurological and psychiatric diseases. In this study, through the creation of an interaction network on HuD and its potential targets, we identified a strong association between HuD and several diseases of the nervous system. Specifically, we focused on the relationship between HuD and the brain-derived neurotrophic factor (BDNF), whose protein is implicated in several neuronal diseases and is involved in the regulation of neuronal development, survival, and function. To better investigate this relationship and given that we previously demonstrated that folic acid (FA) is able to directly bind HuD itself, we performed in vitro experiments in neuron-like human SH-SY5Y cells in the presence of FA, also known to be a pivotal environmental factor influencing the nervous system development. Our findings show that FA exposure results in a significant increase in both HuD and BDNF transcripts and proteins after 2 and 4 h of treatment, respectively. Similar data were obtained after 2 h of FA incubation followed by 2 h of washout. This increase was no longer detected upon 24 h of FA exposure, probably due to a signaling shutdown mechanism. Indeed, we observed that following 24 h of FA exposure HuD is methylated. These findings indicate that FA regulates BDNF expression via HuD and suggest that FA can behave as an epigenetic modulator of HuD in the nervous system acting via short- and long-term mechanisms. Finally, the present results also highlight the potential of BDNF as a therapeutic target for specific neurological and psychiatric diseases.

Uncovering the molecular mechanisms of Fructus Choerospondiatis against coronary heart disease using network pharmacology analysis and experimental pharmacology

Fructus Choerospondiatis (FC), a Mongolian medicine, was mainly used in Mongolian medical theory for the treatment of coronary heart disease (CHD). Nonetheless, the main components and mechanisms of action of FC in the treatment of coronary artery disease have not been studied clearly.

AIM OF THE STUDY

The aim of this study is to identify the components of FC and analyze the pathways affected by the targets of these components to probe into the potential mechanisms of action of FC on coronary heart disease.

MATERIALS AND METHODS

Identification of compounds in FC employing high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF-MS) method, then further investigate the network pharmacology and molecular docking to obtain potential targets and elucidate the potential mechanism of action of FC in the therapy of CHD. Experimental validation was established to verify the mechanism of FC in vitro.

RESULTS

21 FC components were identified and 65 overlapping targets were gained. In addition, these ingredients regulated AMPK and PPAR signaling pathway by 65 target genes including IL6, AKT1 and PPARg, etc. Molecular docking displayed that the binding ability of the key target PPARg to FC components turned out to be better. Experimental validation proved that FC treatment decreased the expression of PPARg (p < 0.05) compare with model group, which may be involved in the PPAR signaling pathway.

CONCLUSIONS

This study was the first to elucidate the mechanism of action of components of FC for the treatment of CHD using network pharmacology. It alleviated CHD by inhibiting the expression of PPARg to attenuate hypoxia/reoxygenation injury, and the results give a basis for elucidating the molecular mechanism of action of FC for the treatment of coronary heart disease.

Identification of immune-related gene signature for predicting prognosis in uterine corpus endometrial carcinoma

The objective of this study is to develop a gene signature related to the immune system that can be used to create personalized immunotherapy for Uterine Corpus Endometrial Carcinoma (UCEC). To classify the UCEC samples into different immune clusters, we utilized consensus clustering analysis. Additionally, immune correlation algorithms were employed to investigate the tumor immune microenvironment (TIME) in diverse clusters. To explore the biological function, we conducted GSEA analysis. Next, we developed a Nomogram by integrating a prognostic model with clinical features. Finally, we performed experimental validation in vitro to verify our prognostic risk model. In our study, we classified UCEC patients into three clusters using consensus clustering. We hypothesized that cluster C1 represents the immune inflammation type, cluster C2 represents the immune rejection type, and cluster C3 represents the immune desert type. The hub genes identified in the training cohort were primarily enriched in the MAPK signaling pathway, as well as the PD-L1 expression and PD-1 checkpoint pathway in cancer, all of which are immune-related pathways. Cluster C1 may be a more suitable for immunotherapy. The prognostic risk model showed a strong predictive ability. Our constructed risk model demonstrated a high level of accuracy in predicting the prognosis of UCEC, while also effectively reflecting the state of TIME.

Prognosis-related metabolic genes in the development of colorectal cancer progress and perspective

Colorectal cancer (CRC) is one of the most commonplace malignant tumors in the world. The occurrence and development of CRC are involved in numerous events. Metabolic reprogramming is one of the hallmarks of cancer and is convoluted and associated with carcinogenesis. Lots of metabolic genes are involved in the occurrence and progression of CRC. Study methods combining tumor genomics and metabolomics are more likely to explore this field in depth. In this mini-review, we make the latest progress and future prospects into the different molecular mechanisms of seven prognosis-related metabolic genes, we screened out in previous research, involved in the occurrence and development of CRC.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents

BACKGROUND

Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches.

METHODS

To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins.

RESULTS

We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC.

CONCLUSION

The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC. Computer-aided methods were used to uncover similar biological targets and signaling pathways associated with CRC, along with bioinformatics and network pharmacology techniques to assess the effects of enzastaurin on CRC. The KEGG and gene ontology (GO) pathway analysis revealed several significant pathways including in positive regulation of protein phosphorylation, negative regulation of the apoptotic process, nucleus, nucleoplasm, protein tyrosine kinase activity, PI3K-Akt signaling pathway, pathways in cancer, focal adhesion, HIF-1 signaling pathway, and Rap1 signaling pathway. Later, the hub protein module identified from the protein-protein interactions (PPIs) network, molecular docking and molecular dynamics simulation represented that enzastaurin showed strong binding interaction with two hub proteins including CASP3 (-8.6 kcal/mol), and MCL1 (-8.6 kcal/mol), which were strongly implicated in CRC management than other the five hub proteins. Moreover, the pharmacokinetic features of enzastaurin revealed that it is an effective therapeutic agent with minimal adverse effects. Enzastaurin may inhibit the potential biological targets that are thought to be responsible for the advancement of CRC and this study suggests a potential novel therapeutic target for CRC.

NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution

Motivation

The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables' disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject.

Results

The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject's disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene-disease association studies to identify genetic architecture in the context of epistasis.

Availability and implementation

The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach.

Contact

ting.hu@queensu.ca.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Neuromedin U secreted by colorectal cancer cells promotes a tumour-supporting microenvironment

BACKGROUND

Neuromedin U (NMU) was identified as one of the hub genes closely related to colorectal cancer (CRC) progression and was recently shown to be a motility inducer in CRC cells. Its autocrine signalling through specific receptors increases cancer cell migration and invasiveness. Because of insufficient knowledge concerning NMU accessibility and action in the tumour microenvironment, its role in CRC remains poorly understood and its potential as a therapeutic target is still difficult to define.

METHODS

NMU expression in CRC tissue was detected by IHC. Data from The Cancer Genome Atlas were used to analyse gene expression in CRC. mRNA and protein expression was detected by real-time PCR, immunoblotting or immunofluorescence staining and analysed using confocal microscopy or flow cytometry. Proteome Profiler was used to detect changes in the profiles of cytokines released by cells constituting tumour microenvironment after NMU treatment. NMU receptor activity was monitored by detecting ERK1/2 activation. Transwell cell migration, wound healing assay and microtube formation assay were used to evaluate the effects of NMU on the migration of cancer cells, human macrophages and endothelial cells.

RESULTS

Our current study showed increased NMU levels in human CRC when compared to normal adjacent tissue. We detected a correlation between high NMUR1 expression and shorter overall survival of patients with CRC. We identified NMUR1 expression on macrophages, endothelial cells, platelets, and NMUR1 presence in platelet microparticles. We confirmed ERK1/2 activation by treatment of macrophages and endothelial cells with NMU, which induced pro-metastatic phenotypes of analysed cells and changed their secretome. Finally, we showed that NMU-stimulated macrophages increased the migratory potential of CRC cells.

CONCLUSIONS

We propose that NMU is involved in the modulation and promotion of the pro-metastatic tumour microenvironment in CRC through the activation of cancer cells and other tumour niche cells, macrophages and endothelial cells. Video abstract.

Network module function enrichment analysis of lung squamous cell carcinoma and lung adenocarcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis.

METHODS

To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated.

RESULTS

We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules "purple" and "green" and LUAD modules "brown" and "pink" are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival.

CONCLUSIONS

We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.

Custom gene expression panel for evaluation of potential molecular markers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. It is a highly heterogeneous disease with poor prognosis and limited treatment options, which highlights the need for reliable biomarkers. This study aims to explore molecular markers that allow stratification of HCC and may lead to better prognosis and treatment prediction.

MATERIALS AND METHODS

We studied 20 candidate genes (HCC hub genes, potential drug target genes, predominant somatic mutant genes) retrieved from literature and public databases with potential to be used as the molecular markers. We analysed expression of the genes by RT-qPCR in 30 HCC tumour and adjacent non-tumour paired samples from Vietnamese patients. Fold changes in expression were then determined using the 2^-∆∆CT method, and unsupervised hierarchical clustering was generated using Cluster v3.0 software.

RESULTS

Clustering of expression data revealed two subtypes of tumours (proliferative and normal-like) and four clusters for genes. The expression profiles of the genes TOP2A, CDK1, BIRC5, GPC3, IGF2, and AFP were strongly correlated. Proliferative tumours were characterized by high expression of the c-MET, ARID1A, CTNNB1, RAF1, LGR5, and GLUL1 genes. TOP2A, CDK1, and BIRC5 HCC hub genes were highly expressed (> twofold) in 90% (27/30), 83% (25/30), and 83% (24/30) in the tissue samples, respectively. Among the drug target genes, high expression was observed in the GPC3, IGF2 and c-MET genes in 77% (23/30), 63% (19/30), and 37% (11/30), respectively. The somatic mutant Wnt/ß-catenin genes (CTNNB1, GLUL and LGR5) and TERT were highly expressed in 40% and 33% of HCCs, respectively. Among the HCC marker genes, a higher percentage of tumours showed GPC3 expression compared to AFP expression [73% (23/30) vs. 43% (13/30)].

CONCLUSION

The custom panel and molecular markers from this study may be useful for diagnosis, prognosis, biomarker-guided clinical trial design, and prediction of treatment outcomes.

Expression of NMU, PPBP and GNG4 in colon cancer and their influences on prognosis

BACKGROUND

This study aims to identify the core genes that influence the prognosis of colon cancer (CC) and analyze their relationships with clinical characteristics.

METHODS

The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools and screened through the Oncomine database. The expression of core genes in CC tissues and cells was validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Spearman correlation was used to analyze the relationship between different parameters. Overall survival was assessed by the Kaplan-Meier method. The area under the curve (AUC) and the receiver operating curve (ROC) were applied to assess the accuracy of genes for predicting prognosis.

RESULTS

There were 1,665 DEGs that were identified from TCGA database. Bioinformatics analysis found that GNGT1, NMU, PPBP, AGT, and GNG4 were differentially expressed in CC tissue. Overexpression of NMU, PPBP, AGT, and GNG4 in CC was associated with shortened survival time (P<0.05). In the validation studies, the high expression levels of NMU, PPBP and GNG4 in CC cells and tissues were confirmed compared to the control groups (P<0.05) and were adverse prognostic biomarkers (P<0.01). The combination prognostic model of the three core genes predicted the 1-, 3-, and 5-year survival of CC with AUCs of 0.868, 0.635 and 0.770, respectively.

CONCLUSIONS

High levels of NMU, PPBP, and GNG4 were associated with poor prognosis in CC. The combination prognostic model of these three genes could be a new option.

Integrated analysis of intestinal microbiota and host gene expression in colorectal cancer patients

Introduction. Colorectal cancer (CRC) is one of the most common cancers and poses heavy burden on global health. The relationship between mucosal microbiome composition and colorectal gene expression are rarely studied. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between them in colorectal cancer patients.Gap statement. Previous studies have identified the contribution of gut microbiota and DEGs to the pathogenesis of CRC, but the relationship between mucosal microbiome composition and colorectal gene expression are rarely studied.Aim. In this study, we integrated transcriptome data with microbiome data to investigate the relationship between mucosal microbiome composition and colorectal gene expression.Methodology. First, three independent CRC gene expression profiles (GSE184093, GSE156355 and GSE146587) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs). Second, another dataset (GSE163366) was used to analyse gut mucosal microbiome differential abundance. GO (Gene Ontology) function and KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway enrichment analyses of the DEGs were performed. Protein-protein interactions (PPIs) of the DEGs were constructed. The Spearman correlation analysis was computed between host DEGs and gut microbiome abundance data.Results. A total of 1036 upregulated DEGs and 1194 downregulated DEGs between noncancerous tissues and cancerous tissues were identified based on the analysis. One significant module with a score 37.65 was selected out via MCODE including 41 upregulated DEGs, which are were mostly enriched in two pathways, including microtubule binding and tubulin binding. In particular, significant negative correlations are prevalent between Fusobacterium and the 41 DEGs with the correlation ranging between -0.54 and -0.35, and there commonly exist significant positive correlations between Blautia and the 41 DEGs with the correlation ranging between 0.42 and 0.54, indicating that Fusobacterium and Blautia are two of the most important microbes interacting with the gene regulation.Conclusion. Our results demonstrate significant correlation between some gut microbes and DEGs, providing a comprehensive bioinformatics analysis of them for future investigation into the molecular mechanisms and biomarkers.

Type 2 diabetes is more closely associated with risk of colorectal cancer based on elevated DNA methylation levels of ADCY5

Type 2 diabetes mellitus (T2DM) has an increased risk of cancer. In the present study, the relationship between T2DM and 13 types of cancer was analyzed and key methylation genes were searched. First, DNA methylation and mRNA expression were obtained data for T2DM and 13 types of cancer from The Cancer Genome Atlas and Gene Expression Omnibus. The t-test was used to screen the differentially methylated expression overlapping genes (DE-MGs) in T2DM and cancer on both methylation and expression levels. DE-MGs are weighted based on the methylation and projected into the human protein interaction network. The correlation between T2DM and each type of cancer was analyzed, and key genes were identified. The results showed that 293 DE-MGs were related to T2DM and 3307 were related to cancer. The network found that T2DM is more related to colorectal cancer (CRC) compare with the other 12 types of cancer. A total of 5 from 8 candidate genes were associated with CRC. A total of 28 clinical patients were used to validate these 5 genes. A CRC tissue sample was collected from each patient, as well as a paracancerous sample that served as a control. A total of 56 tissue samples were divided into 4 groups: control group, T2DM group, CRC group and T2DM with CRC group (combination group). Compared with the control group, the methylation level of adenylate cyclase 5 (ADCY5), neuregulin 1 and ELAV-like RNA-binding protein 4 in the combination group was significantly upregulated, and the mRNA level was significantly downregulated. Furthermore, based on the methylation level of ADCY5, the correlation coefficient between the combination group and the T2DM group was greater than that of the CRC group. In conclusion, T2DM is most likely to be associated with CRC among 13 common types of cancer based on methylation characteristics. An upregulated methylation of ADCY5 in T2DM may have a higher risk of CRC.

Development of an Accessible Gene Expression Bioinformatics Pipeline to Study Driver Mutations of Colorectal Cancer

Colorectal cancer (CRC) is a global cause of cancer-related mortality driven by genetic and environmental factors which influence therapeutic outcomes. The emergence of next-generation sequencing technologies enables the rapid and extensive collection and curation of genetic data for each cancer type into clinical gene expression biobanks. We report the application of bioinformatics tools for investigating the expression patterns and prognostic significance of three genes that are commonly dysregulated in colon cancer: adenomatous polyposis coli (APC); B-Raf proto-oncogene (BRAF); and Kirsten rat sarcoma viral oncogene homologue (KRAS). Through the use of bioinformatics tools, we show the patterns of APC, BRAF and KRAS genetic alterations and their role in patient prognosis. Our results show mutation types, the frequency of mutations, tumour anatomical location and differential expression patterns for APC, BRAF and KRAS for colorectal tumour and matched healthy tissue. The prognostic value of APC, BRAF and KRAS genetic alterations was investigated as a function of their expression levels in CRC. In the era of precision medicine, with significant advancements in biobanking and data curation, there is significant scope to use existing clinical data sets for evaluating the role of mutational drivers in carcinogenesis. This approach offers the potential for studying combinations of less well-known genes and the discovery of novel biomarkers, or for studying the association between various effector proteins and pathways.

GNG2 acts as a tumor suppressor in breast cancer through stimulating MRAS signaling

G-protein gamma subunit 2 (GNG2) is involved in several cell signaling pathways, and is essential for cell proliferation and angiogenesis. However, the role of GNG2 in tumorigenesis and development remains unclear. In this study, 1321 differentially expressed genes (DEGs) in breast cancer (BC) tissues were screened using the GEO and TCGA databases. KEGG enrichment analysis showed that most of the enriched genes were part of the PI3K-Akt signaling pathway. We identified GNG2 from the first five DEGs, its expression was markedly reduced in all BC subtype tissues. Cox regression analysis showed that GNG2 was independently associated with overall survival in patients with luminal A and triple-negative breast cancers (TNBC). GNG2 over-expression could significantly block the cell cycle, inhibit proliferation, and promote apoptosis in BC cells in vitro. In animal studies, GNG2 over-expression inhibited the growth of BC cells. Further, we found that GNG2 significantly inhibited the activity of ERK and Akt in an MRAS-dependent manner. Importantly, GNG2 and muscle RAS oncogene homolog (MRAS) were co-localized in the cell membrane, and the fluorescence resonance energy transfer (FRET) experiment revealed that they had direct interaction. In conclusion, the interaction between GNG2 and MRAS likely inhibits Akt and ERK activity, promoting apoptosis and suppressing proliferation in BC cells. Increasing GNG2 expression or disrupting the GNG2-MRAS interaction in vivo could therefore be a potential therapeutic strategy to treat BC.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

Identification of key pathways and genes in vestibular schwannoma using bioinformatics analysis

The aim of the present study is to identify novel promising marks and targets of diagnosis, therapy and prognosis for patients with vestibular schwannoma at the molecular level. The gene expression profiles of GSE54934, GSE39645 and GSE56597 datasets were obtained respectively from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by comparing between gene expression profiles of the vestibular schwannoma tissues and normal tissues. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analysis were performed. The function and pathway enrichment analysis were performed for DEGs with DAVID. Reverse transcription-quantitative PCR were conducted to confirm the expression of BCL2, AGT, IL6 and ITGA2 in human Schwann cells and vestibular schwannoma cells. A total of 4,025, 1,1291 and 1,513 DEGs were identified from GSE54934, GSE56597 and GSE39645 datasets, respectively. GO and KEGG analysis showed that the mutual upregulated genes were mainly enriched in cell division, mitotic nuclear division, and transition of mitotic cell cycle, whilst mutual downregulated genes were enriched in chemical synaptic transmission, neurotransmitter transport, and synaptic vesicle membrane. Subsequently, 20 genes, including BCL2, AGT, IL6 and ITGA2 were selected as hub genes with high degrees after PPI network analysis. The significant differential expression of those genes were detected among vestibular schwannoma tissues compared with normal nerve tissues. In conclusion, BCL2, AGT, IL6 and ITGA2 are significantly higher expressed in vestibular schwannoma tissues compared with human Schwann tissues. The DEGs identified in the present study provide novel targets for the diagnosis and treatment of vestibular schwannoma.

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

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

Study on the Multitarget Mechanism and Active Compounds of Essential Oil from Artemisia argyi Treating Pressure Injuries Based on Network Pharmacology

In order to comprehensively explore multitarget mechanism and key active compounds of Artemisia argyi essential oil (AAEO) in the treatment of pressure injuries (PIs), we analyzed the biological functions and pathways involved in the intersection targets of AAEO and PIs based on network pharmacology, and the affinity of AAEO active compounds and core targets was verified by molecular docking finally. In our study, we first screened 54 effective components according to the relative content and biological activity. In total, 103 targets related to active compounds of AAEO and 2760 targets associated with PIs were obtained, respectively, and 50 key targets were overlapped by Venny 2.1.0. The construction of key targets-compounds network was achieved by the STRING database and Cytoscape 3.7.2 software. GO analysis from Matespace shows that GO results are mainly enriched in biological processes, including adrenergic receptor activity, neurotransmitter clearance, and neurotransmitter metabolic process. KEGG analysis by the David and Kobas website shows that the key targets can achieve the treatment on PIs through a pathway in cancer, PI3K-Akt signaling pathway, human immunodeficiency virus 1 infection, MAPK signaling pathway, Wnt signaling pathway, etc. In addition, molecular docking results from the CB-Dock server indicated that active compounds of AAEO had good activity docking with the first 10 key targets. In conclusion, the potential targets and regulatory molecular mechanisms of AAEO in the treatment of PIs were analyzed by network pharmacology and molecular docking. AAEO can cure PIs through the synergistic effect of multicomponent, multitarget, and multipathway, providing a theoretical basis and new direction for further study.

MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma

Background: Most cholangiocarcinoma (CCA) patients are diagnosed at an advanced stage of disease, and the postoperational recurrence rates are high in those undergoing resection. The lack of satisfying biomarkers for early diagnoses and effective targeting of driver pathways is the leading reason for therapeutic failures. The goal of this study was to find a biomarker for making improved diagnoses with enhanced prognostic capabilities for CCA. Materials and Methods: Our study used bioinformatic analyses of microarray data from the Gene Expression Omnibus (GEO) database and investigated mitotic arrest deficient 2-like protein 1 (MAD2L1) expression in tumor and adjacent non-neoplastic biliary ducts through immunocytochemistry in 42 surgically removed primary CCAs from a single institute. In vitro and in vivo models were used to explore the function of MAD2L1. Results: In total, 297 high probability differentially expressed genes (DEGs) were obtained from overlapping the DEGs from the three individual data sets. Through enrichment assays and protein-protein interaction networks analyses, seven hub genes were identified. MAD2L1 was picked up as a novel biomarker based on hierarchical cluster analyses and Kaplan-Meier survival analyses. MAD2L1 was expressed in cancer tissues but not in the surrounding normal tissue, with 31 (73.81%) of 42 CCAs MAD2L1 positive by immunohistochemistry (IHC). MAD2L1 expression levels were significantly correlated with tumor size, pathological grade, and clinical stage. A Kaplan-Meier survival analysis demonstrated an inverse correlation with MAD2L1 expression. Real-time polymerase chain reaction and immunoblotting results further confirmed the results of IHC and bioinformatic analyses. In vitro and in vivo models demonstrated decreasing MAD2L1 could significantly suppress tumor growth, whereas increasing MAD2L1 could promote tumor growth. Conclusion: MAD2L1 could be used as a biomarker to predict prognosis and potential therapeutic target in CCA. Clinical Trial Registration Number: [2020]KY157-01.

Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis

COVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein–protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

Identification of potential biomarkers with colorectal cancer based on bioinformatics analysis and machine learning

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Biomarker discovery is critical to improve CRC diagnosis, however, machine learning offers a new platform to study the etiology of CRC for this purpose. Therefore, the current study aimed to perform an integrated bioinformatics and machine learning analyses to explore novel biomarkers for CRC prognosis. In this study, we acquired gene expression microarray data from Gene Expression Omnibus (GEO) database. The microarray expressions GSE103512 dataset was downloaded and integrated. Subsequently, differentially expressed genes (DEGs) were identified and functionally analyzed via Gene Ontology (GO) and Kyoto Enrichment of Genes and Genomes (KEGG). Furthermore, protein protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software to identify hub genes; however, the hub genes were subjected to Support Vector Machine (SVM), Receiver operating characteristic curve (ROC) and survival analyses to explore their diagnostic values. Meanwhile, TCGA transcriptomics data in Gene Expression Profiling Interactive Analysis (GEPIA) database and the pathology data presented by in the human protein atlas (HPA) database were used to verify our transcriptomic analyses. A total of 105 DEGs were identified in this study. Functional enrichment analysis showed that these genes were significantly enriched in biological processes related to cancer progression. Thereafter, PPI network explored a total of 10 significant hub genes. The ROC curve was used to predict the potential application of biomarkers in CRC diagnosis, with an area under ROC curve (AUC) of these genes exceeding 0.92 suggesting that this risk classifier can discriminate between CRC patients and normal controls. Moreover, the prognostic values of these hub genes were confirmed by survival analyses using different CRC patient cohorts. Our results demonstrated that these 10 differentially expressed hub genes could be used as potential biomarkers for CRC diagnosis.

Interleukin-38 Suppresses Cell Migration and Proliferation and Promotes Apoptosis of Colorectal Cancer Cell Through Negatively Regulating Extracellular Signal-Regulated Kinases Signaling

Inflammatory cytokines has been of great interest in the field of colorectal cancer (CRC) tumor immunology in recent years. As an anti-inflammatory interleukin (IL), IL-38 may contribute to the early diagnosis of CRC and improve the prognosis of CRC patients. This study was designed to investigate the role of circulating IL-38 and the regulatory mechanism of IL-38 in CRC. Expression of IL-38 were detected by ELISA and immunohistochemical staining. The influence of IL-38 on CRC were evaluated by Western blot and cell biology assays after CRC cells were treated by rhIL-38 or LM22B-10. We also verified the anti-tumor activity of IL-38 in transgenic mouse model. The expression of IL-38 was found to be correlated with progression of CRC. IL-38 inhibits CRC metastasis, proliferation and facilitates apoptosis through suppressing the activation of extracellular signal-regulated kinases (ERK) signaling pathway inducing the decrease of downstream genes, which were partially abrogated by ERK activator LM22B-10 in vitro. We also found that IL-38 overexpression inhibits tumorigenesis in vivo. Our findings indicate that IL-38 may serve as a serum prediction marker to identify the prognosis of CRC patients. IL-38 may inhibit the progression of CRC by negatively regulation on ERK signaling pathway.

Transcriptome profiling based on protein-protein interaction networks provides a set of core genes for understanding the immune response mechanisms of the egg-protecting behavior in Octopus ocellatus

Protection via of the immune system is indispensable to the life of organisms. Within an immune network, problems with a given link will affect the normal life activities of the organism. Octopus ocellatus is cephalopod widely distributed throughout the world's oceans. Because of its unique nervous system and locomotive organs, research on this species has gradually increased in recent years. Many immune response mechanisms associated with behaviors of O. ocellatus are still unclear. Moreover, as a factor affecting the normal growth of O. ocellatus, egg protection has rarely been considered in previous behavioral studies. In this study, we analyzed the transcriptome profile of gene expression in O. ocellatus larvae, and identified 5936 differentially expressed genes (DEGs). GO and KEGG enrichment analyses were used to search for immune-related DEGs. Protein-protein interaction networks were constructed to examine the interactions between immune-related genes. Fifteen hub genes involved in multiple KEGG signaling pathways or with multiple protein-protein interaction relationships were obtained and verified by quantitative RT-PCR. We first studied the effects of egg protection on the immunity of O. ocellatus larvae by means of protein-protein interaction networks, and the results provide valuable genetic resources for understanding the immunity of invertebrate larvae. The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

Identification of Hub Gene TIMP1 and Relative ceRNAs Regulatory Network in Colorectal Cancer

Objective

This study aimed to discover the ceRNAs network in the pathophysiological development of human colorectal cancer (CRC) and to screen biomarkers for target therapy and prognosis by using integrated bioinformatics analysis.

Methods

Data on gene expressions of mRNAs, miRNAs, and circRNAs and clinical information were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Differentially expressed mRNAs (DEmRNAs) were identified by using the DESeq2 package of R software. Functional enrichment analysis was conducted using the ClusterProfiler package of R software. The protein–protein interaction (PPI) network was shown by the STRING website. Survival analysis of hub genes was performed using the survival package in R software. Interactions among hub genes, differentially expressed miRNAs (DEmiRNAs), and differentially expressed circRNAs (DEcircRNAs) were used to construct the ceRNAs network.

Results

A total of 412 DEmRNAs including 82 upregulated and 330 downregulated genes were screened out between 473 CRC and 41 normal samples. Two hundred and sixty DEcircRNAs including 253 upregulated and 7 downregulated genes were altered between 23 CRC and 23 normal samples. One hundred and ninety DEmiRNAs including 82 upregulated and 108 downregulated genes were obtained between 450 CRC and 8 normal samples. A ceRNAs and PPI network were successfully constructed, and TIMP1 associated with prognosis was employed.

Conclusion

The present study identified a novel circRNAs-miRNAs-mRNA ceRNAs network, which implied that TIMP1 and related miRNAs, circRNAs were potential biomarkers underlying the development of CRC, providing new insights for survival predictions and therapeutic targets.

Preliminary therapeutic and mechanistic evaluation of S-allylmercapto-N-acetylcysteine in the treatment of pulmonary emphysema

The objective of this work was to study the effects and mechanisms of S-allylmercapto-N-acetylcysteine (ASSNAC) in the treatment of pulmonary emphysema based on network pharmacology analysis and other techniques. Firstly, the potential targets associated with ASSNAC and COPD were integrated using public databases. Then, a protein-protein interaction network was constructed using String database and Cytoscape software. The Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed on DAVID platform. The molecular docking of ASSNAC with some key disease targets was implemented on the SwissDock platform. To verify the results of the network pharmacology, a pulmonary emphysema mice model was established and treated with ASSNAC. Besides, the expressions of the predicted targets were detected by immunohistochemistry, Western blot analysis or enzyme-linked immunosorbent assay. Results showed that 33 overlapping targets are achieved, including CXCL8, ICAM1, MAP2K1, PTGS2, ACE and so on. The critical pathways of ASSNAC against COPD involved arachidonic acid metabolism, chemokine pathway, MAPK pathway, renin-angiotensin system, and others. Pharmacodynamic experiments demonstrated that ASSNAC decreased the pulmonary emphysema and inflammation in the pulmonary emphysema mice. Therefore, these results confirm the perspective of network pharmacology in the target verification, and indicate the treatment potential of ASSNAC against COPD.

Development and Validation of Novel Biomarkers Related to M2 Macrophages Infiltration by Weighted Gene Co-Expression Network Analysis in Prostate Cancer

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

Patterns of Immune Infiltration and the Key Immune-Related Genes in Acute Type A Aortic Dissection in Bioinformatics Analyses

Background

Immune-inflammatory mechanisms contribute greatly to the complex process leading to type A aortic dissection (TAAD). This study aims to explore immune infiltration and key immune-related genes in acute TAAD.

Methods

ImmuCellAI algorithm was applied to analyze patterns of immune infiltration in TAAD samples and normal aortic vessel samples in the GSE153434 dataset. Differentially expressed genes (DEGs) were screened. Immune-related genes were obtained from overlapping DEGs of GSE153434 and immune genes of the ImmPort database. The hub genes were obtained based on the protein–protein interaction (PPI) network. The hub genes in TAAD were validated in the GSE52093 dataset. The correlation between the key immune-related genes and infiltrating immune cells was further analyzed.

Results

In the study, the abundance of macrophages, neutrophils, natural killer T cells (NKT cells), natural regulatory T cells (nTreg), T-helper 17 cells (Th17 cells) and monocytes was increased in TAAD samples, whereas that of dendritic cells (DCs), CD4 T cells, central memory T cells (Tcm), mucosa associated invariant T cells (MAIT cells) and B cells was decreased. Interleukin 6 (IL-6), C-C motif chemokine ligand 2 (CCL2) and hepatocyte growth factor (HGF) were identified and validated in the GSE52093 dataset as the key immune-related genes. Furthermore, IL-6, CCL2 and HGF were correlated with different types of immune cells.

Conclusion

In conclusion, several immune cells such as macrophages, neutrophils, NKT cells, and nTreg may be involved in the development of TAAD. IL-6, CCL2 and HGF were identified and validated as the key immune-related genes of TAAD via bioinformatics analyses. The key immune cells and immune-related genes have the potential to be developed as targets of prevention and immunotherapy for patients with TAAD.

Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein-protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

TPQCI: A topology potential-based method to quantify functional influence of copy number variations

Copy number variation (CNV) is a major type of chromosomal structural variation that play important roles in many diseases including cancers. Due to genome instability, a large number of CNV events can be detected in diseases such as cancer. Therefore, it is important to identify the functionally important CNVs in diseases, which currently still poses a challenge in genomics. One of the critical steps to solve the problem is to define the influence of CNV. In this paper, we provide a topology potential based method, TPQCI, to quantify this kind of influence by integrating statistics, gene regulatory associations, and biological function information. We used this metric to detect functionally enriched genes on genomic segments with CNV in breast cancer and multiple myeloma and discovered biological functions influenced by CNV. Our results demonstrate that, by using our proposed TPQCI metric, we can detect disease-specific genes that are influenced by CNVs. Source codes of TPQCI are provided in Github (https://github.com/usos/TPQCI).

Integrated Bioinformatics Analysis Exhibits Pivotal Exercise-Induced Genes and Corresponding Pathways in Malignant Melanoma

Malignant melanoma represents a sort of neoplasm deriving from melanocytes or cells developing from melanocytes. The balance of energy and energy-associated body composition and body mass index could be altered by exercise, thereby directly affecting the microenvironment of neoplasm. However, few studies have examined the mechanism of genes induced by exercise and the pathways involved in melanoma. This study used three separate datasets to perform comprehensive bioinformatics analysis and then screened the probable genes and pathways in the process of exercise-promoted melanoma. In total, 1,627 differentially expressed genes (DEGs) induced by exercise were recognized. All selected genes were largely enriched in NF-kappa B, Chemokine signaling pathways, and the immune response after gene set enrichment analysis. The protein-protein interaction network was applied to excavate DEGs and identified the most relevant and pivotal genes. The top 6 hub genes (Itgb2, Wdfy4, Itgam, Cybb, Mmp2, and Parp14) were identified, and importantly, 5 hub genes (Itgb2, Wdfy4, Itgam, Cybb, and Parp14) were related to weak disease-free survival and overall survival (OS). In conclusion, our findings demonstrate the prognostic value of exercise-induced genes and uncovered the pathways of these genes in melanoma, implying that these genes might act as prognostic biomarkers for melanoma.

Identification of Targets and Active Components of Yiqi SanJie Formula Against Lung Neoplasms Based on Network Pharmacology Analysis and Molecular Docking

Yiqi Sanjie formula (YQSJF) is mainly applied clinically for the treatment of lung neoplasms. The purpose of this study was to explore the pharmacodynamics of the active components of YQSJF and the mechanism of therapeutic effects in the treatment of lung neoplasm diseases based on network pharmacology. The network of component-target, target-pathway, and pathway-disease of YQSJF was constructed by using Cytoscape software. According to the screening result, 37 key components, 57 important targets, and 866 candidate pathways were obtained. The enrichment analysis results indicated that YQSJF might play a therapeutic role in lung cancer by regulating several signaling pathways, such as the PI3K-AKT, non-small cell lung cancer, small cell lung cancer, and apoptosis pathways. There were 53 intersection genes between YQSJF and the lung cancer gene, 52 common genes, and 11 key targets, including CASP8, CASP9, AR, ESR1, PTGS2, NOS3, PGR, TGFB1, PPARG, RELA, and NOS2, screened by using Protein-Protein Interaction (PPI) analysis. These could be the potential therapeutic targets of YQSJF against lung cancer. Enrichment analysis of the intersection gene pathways revealed 10 major functional pathways, including the VEGF, apoptosis, and IL-17 signaling pathways. The molecular docking results showed the potential regulating activity of kaempferol against AR, pelargonidin against PGR, and baicalein against both PTGS2 and AR. In conclusion, combinational network pharmacology analysis results indicated that YQSJF might present its efficacy of alleviating lung neoplasm symptoms through multiple targets in a synergetic way.

Mdfi Promotes C2C12 Cell Differentiation and Positively Modulates Fast-to-Slow-Twitch Muscle Fiber Transformation

Muscle development requires myoblast differentiation and muscle fiber formation. Myod family inhibitor (Mdfi) inhibits myogenic regulatory factors in NIH3T3 cells, but how Mdfi regulates myoblast myogenic development is still unclear. In the present study, we constructed an Mdfi-overexpression (Mdfi-OE) C2C12 cell line by the CRISPR/Cas9 system and performed RNA-seq on Mdfi-OE and wild-type (WT) C2C12 cells. The RNA-seq results showed that the calcium signaling pathway was the most significant. We also established the regulatory networks of Mdfi-OE on C2C12 cell differentiation and muscle fiber type transformation and identified hub genes. Further, both RNA-seq and experimental verification demonstrated that Mdfi promoted C2C12 cell differentiation by upregulating the expression of Myod, Myog, and Myosin. We also found that the positive regulation of Mdfi on fast-to-slow-twitch muscle fiber transformation is mediated by Myod, Camk2b, and its downstream genes, such as Pgc1a, Pdk4, Cs, Cox4, Acadm, Acox1, Cycs, and Atp5a1. In conclusion, our results demonstrated that Mdfi promotes C2C12 cell differentiation and positively modulates fast-to-slow-twitch muscle fiber transformation. These findings further our understanding of the regulatory mechanisms of Mdfi in myogenic development and muscle fiber type transformation. Our results suggest potential therapeutic targets for muscle- and metabolic-related diseases.

Integration of gene expression data identifies key genes and pathways in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignant tumor and prevalent cause of cancer-related death worldwide. In this study, we analyzed the gene expression profiles of patients with CRC with the aim of better understanding the molecular mechanism and key genes in CRC. Four gene expression profiles including, GSE9348, GSE41328, GSE41657, and GSE113513 were downloaded from GEO database. The data were processed using R programming language, in which 319 common differentially expressed genes including 94 up-regulated and 225 down-regulated were identified. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were conducted to find the most significant enriched pathways in CRC. Based on the GO and KEGG pathway analysis, the most important dysregulated pathways were regulation of cell proliferation, biocarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism. The protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software and hub genes including MYC, CXCL1, CD44, MMP1, and CXCL12 were identified as the most critical hub genes. The present study enhances our understanding of the molecular mechanisms of the CRC, which might potentially be applied in the treatment strategies of CRC as molecular targets and diagnostic biomarkers.

Identifications of Candidate Genes Significantly Associated With Rectal Cancer by Integrated Bioinformatics Analysis

INTRODUCTION

Rectal cancer ranks as the eighth in cancer-related morbidity and the tenth in the cancer-related mortality. A few studies have explored several biomarkers for colorectal cancer. However, there is still a great need for us to excavate novel biomarkers with effective and efficient diagnostic and prognostic values to discover the etiology and pathogenesis of rectal cancer separately. Therefore, we aimed to identify more novel candidate genes that were significantly associated with rectal cancer through integrated bioinformatics analysis.

METHODS

We analyzed the gene expression profiles of GSE15781 and GSE20842 from Gene Expression Omnibus database to identify differentially expressed genes between normal rectal tissue and rectal cancer tissue.

RESULTS

We searched for core genes, carried out survival analysis and analyzed the expressions of core genes. We found that 142 genes were significantly upregulated, and 229 genes were significantly downregulated in all 3 independent studies. In KEGG analysis, the upregulated genes were significantly enriched in cytokine-cytokine receptor interaction, IL-17 signaling pathway, cell cycle, etc. The downregulated genes were primarily enriched in nitrogen metabolism, mineral absorption and pentose and glucuronate interconversions. Inhibin subunit beta B (INHBB) expressed markedly higher in rectal cancer tissues compared with normal tissues, and claudins (CLDN) 23 expressed significantly lower in rectal cancer tissues.

CONCLUSION

In conclusion, we discovered that INHBB could provide a great significant diagnostic and prognostic values for rectal cancer.

Immune infiltration-associated serum amyloid A1 predicts favorable prognosis for hepatocellular carcinoma

BACKGROUND

Serum amyloid A1 (SAA1) is an acute-phase protein involved in acute or chronic hepatitis. Its function is still controversial. In addition, the effect of the expression of SAA1 and its molecular function on the progression in hepatocellular carcinoma (HCC) is still unclear.

AIM

To demonstrate the expression of SAA1 and its effect on the prognosis in HCC and explain further the correlation of SAA1 and immunity pathways.

METHODS

SAA1 expression in HCC was conducted with The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) in GEPIA tool, and the survival analysis based on the SAA1 expression level was achieved in the Kaplan-Meier portal. The high or low expression group was then drawn based on the median level of SAA1 expression. The correlation of SAA1 and the clinical features were conducted in the UALCAN web-based portal with TCGA-LIHC, including tumor grade, patient disease stage, and the TP53 mutation. The correlation analysis between SAA1 expression and TP53 mutation was subjected to the TCGA portal. The tumor purity score and the immune score were analyzed with CIBERSORT. The correlation of SAA1 expression and tumor-infiltrating lymphocytes was achieved in TISIDB web-based integrated repository portal for tumor-immune system interactions. GSE125336 dataset was used to test the SAA1 expression in the responsive or resistant group with anti-PD1 therapy. Gene set enrichment analysis was applied to evaluate the gene enrichment signaling pathway in HCC. The similar genes of SAA1 in HCC were identified in GEPIA, and the protein-protein interaction of SAA1 was conducted in the Metascape tool. The expression of C-X-C motif chemokine ligand 2, C-C motif chemokine ligand 23, and complement C5a receptor 1 was studied and overall survival analysis in HCC was conducted in GEPIA and Kaplan-Meier portal, respectively.

RESULTS

SAA1 expression was decreased in HCC, and lower SAA1 expression predicted poorer overall survival, progression-free survival, and disease-specific survival. Furthermore, SAA1 expression was further decreased with increased tumor grade and patient disease stage. Also, SAA1 expression was further downregulated in patients with TP53 mutation compared with patients with wild type TP53. SAA1 expression was negatively correlated with the TP53 mutation. Lower SAA1 predicted poorer survival rate, especially in the patients with no hepatitis virus infection, other than those with hepatitis virus infection. Moreover, the SAA1 expression was negatively correlated with tumor purity. In contrast, SAA1 expression was positively correlated with the immune score in HCC, and the correlation analysis between SAA1 expression and tumor-infiltrating lymphocytes also showed a positive correlation in HCC. Decreased SAA1 was closely associated with the immune tolerance of HCC. C-X-C motif chemokine ligand 2 and C-C motif chemokine ligand 23 genes were identified as the hub genes associated with SAA1, which could also serve as favorable prognosis markers for HCC.

CONCLUSION

SAA1 is downregulated in the liver tumor, and it is closely involved in the progression of HCC. Lower SAA1 expression indicates lower survival rate, especially for those patients without hepatitis virus infection. Lower SAA1 expression also suggests lower immune infiltrating cells, especially for those with immune cells exerting anti-tumor immune function. SAA1 expression is closely associated with the anti-tumor immune pathways.

Identification of MTHFD2 as a novel prognosis biomarker in esophageal carcinoma patients based on transcriptomic data and methylation profiling

DNA methylation is an important epigenetic regulatory mechanism in esophageal carcinoma (EC) and is associated with genomic instability and carcinogenesis. In the present study, we aimed to identify tumor biomarkers for predicting prognosis of EC patients.We downloaded mRNA expression profiles and DNA methylation profiles associated with EC from the Gene Expression Omnibus database. Differentially expressed and differentially methylated genes between tumor tissues and adjacent normal tissue samples were identified. Functional enrichment analyses were performed, followed by the construction of protein-protein interaction networks. Data were validated based on methylation profiles from The Cancer Genome Atlas. Candidate genes were further verified according to survival analysis and Cox regression analysis.We uncovered multiple genes with differential expression or methylation in tumor samples compared with normal samples. After taking the intersection of 3 differential gene sets, we obtained a total of 232 overlapping genes. Functional enrichment analysis revealed that these genes are related to pathways such as "glutathione metabolism," "p53 signaling pathway," and "focal adhesion." Furthermore, 8 hub genes with inversed expression and methylation correlation were identified as candidate genes. The abnormal expression levels of MSN, PELI1, and MTHFD2 were correlated with overall survival times in EC patients (P < .05). Only MTHFD2 was significantly associated with a pathologic stage according to univariate analysis (P = .037) and multivariate analysis (P = .043).Our study identified several novel EC biomarkers with prognostic value by integrated analysis of transcriptomic data and methylation profiles. MTHFD2 could serve as an independent biomarker for predicting prognosis and pathological stages of EC.

Systems-level differential gene expression analysis reveals new genetic variants of oral cancer

Oral cancer (OC) ranked as eleventh malignancy worldwide, with the increasing incidence among young patients. Limited understanding of complications in cancer progression, its development system, and their interactions are major restrictions towards the progress of optimal and effective treatment strategies. The system-level approach has been designed to explore genetic complexity of the disease and to identify novel oral cancer related genes to detect genomic alterations at molecular level, through cDNA differential analysis. We analyzed 21 oral cancer-related cDNA datasets and listed 30 differentially expressed genes (DEGs). Among 30, we found 6 significant DEGs including CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13 and studied their functional role in OC. Our genomic and interactive analysis showed significant enrichment of xenobiotics metabolism, p53 signaling pathway and microRNA pathways, towards OC progression and development. We used human proteomic data for post-translational modifications to interpret disease mutations and inter-individual genetic variations. The mutational analysis revealed the sequence predicted disordered region of 14%, 12.5%, 10.5% for ADCY2, CYP1B1, and C7 respectively. The MiRNA target prediction showed functional molecular annotation including specific miRNA-targets hsa-miR-4282, hsa-miR-2052, hsa-miR-216a-3p, for CYP1B1, C7, and ADCY2 respectively associated with oral cancer. We constructed the system level network and found important gene signatures. The drug-gene interaction of OC source genes with seven FDA approved OC drugs help to design or identify new drug target or establishing novel biomedical linkages regarding disease pathophysiology. This investigation demonstrates the importance of system genetics for identifying 6 OC genes (CYP1A1, CYP1B1, ADCY2, C7, SERPINB5, and ANAPC13) as potential drugs targets. Our integrative network-based system-level approach would help to find the genetic variants of OC that can accelerate drug discovery outcomes to develop a better understanding regarding treatment strategies for many cancer types.

Identification of six candidate genes for endometrial carcinoma by bioinformatics analysis

Background

Endometrial carcinoma (EC) is the most common gynecological malignant tumors which poses a serious threat to women health. This study aimed to screen the candidate genes differentially expressed in EC by bioinformatics analysis.

Methods

GEO database and GEO2R online tool were applied to screen the differentially expressed genes (DEGs) of EC from the microarray datasets. Protein-protein interaction (PPI) network for the DEGs was constructed to further explore the relationships among these genes and identify hub DEGs. Gene ontology and KEGG enrichment analyses were performed to investigate the biological role of DEGs. Besides, correlation analysis, genetic alteration, expression profile, and survival analysis of these hub DEGs were also investigated to further explore the roles of these hub gene in mechanism of EC tumorigenesis. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs.

Results

A total of 40 DEGs were screened out as the DEGs with 3 upregulated and 37 downregulated in EC. The gene ontology analysis showed that these genes were significantly enriched in cell adhesion, response to estradiol, and growth factor activity, etc. The KEGG pathway analysis showed that DEGs were enriched in focal adhesion, leukocyte transendothelial migration, PI3K-Akt signaling pathway, and ECM-receptor interaction pathway. More importantly, COL1A1, IGF1, COL5A1, CXCL12, PTEN, and SPP1 were identified as the hub genes of EC. The genetic alteration analysis showed that hub genes were mainly altered in mutation and deep deletion. Expression validation by bioinformatic analysis and qRT-PCR also proved the expression of these six hub genes were differentially expressed in EC. Additionally, significantly better overall survival and disease-free survival were observed with six hub genes altered, and survival outcome in high expression of COL1A1, IGF1, and PTEN patients was also significantly better than low expression patients.

Conclusions

COL1A1, IGF1, COL5A1, CXCL12, PTEN, and SPP1 involved in the pathogenesis of EC and might be candidate genes for diagnosis of EC.

Identification of Key mRNAs and Pathways in Colorectal Cancer

Colorectal cancer (CRC) is the third most cancer-related death worldwide. This work aimed to identify potential hub genes and dysregulated pathways in the CRC by bioinformatics analysis. Three gene expression datasets were collected from GEO datasets, including tumor sample (N = 242) and adjacent nontumor tissue sample (N = 59). RankProd was used to discover the differential expressed genes between tumor and adjacent nontumor tissues for datasets generated by different laboratories. The gene set enrichment analysis conducted on the DE genes, followed by the protein-protein interaction (PPI) network. In total, 2,007 significant differential expression (DE) genes between tumor and adjacent nontumor tissues, include 1,090 upregulated genes and 917 downregulated genes in the tumor. The DE mRNAs are involved in cancer-related pathways. We comprehensively identified the CRC-related key mRNAs. Our data demonstrated combined different resources of transcriptomes will promote the understanding of the molecular mechanisms underlying CRC development and may be useful in discovering candidate molecular biomarkers for diagnosing, prognosis, and treating of CRC.

Identification of key genes and pathways in syphilis combined with diabetes: a bioinformatics study

We noticed that syphilis patients seem to be more susceptible to diabetes and the lesions often involve the kidneys, but the pathogenesis is not yet completely understood. In this study, microarray analysis was performed to investigate the dysregulated expressed genes (DEGs) in rabbit model of syphilis combined with diabetes. A total of 1045 genes were identified to be significantly differentially expressed, among which 571 were up-regulated and 474 were down-regulated (≥ 2.0fold, p < 0.05). Using the database visualization and integration discovery for the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis. The downregulated DEGs were significantly enriched for biosynthesis of antibiotics, carbon metabolism and protein digestion, while the upregulated DEGs were mainly enriched for cancer and PI3K-Akt signaling pathway. Molecular Complex Detection (MCODE) plugins were used to visualize protein–protein interaction (PPI) network of DEGs and Screening for hub genes and gene modules. ALB, FN1, CASP3, MMP9, IL8, CTGF, STAT3, IGF1, VCAM-1 and HGF were filtrated as the hub genes according to the degree of connectivity from the PPI network. To the best of our knowledge, this study is the first to comprehensively identify the expression patterns of dysregulated genes in syphilis combined with diabetes, providing a basis for revealing the underlying pathogenesis of syphilis combined with diabetes and exploring the goals of therapeutic intervention.

Exploring Molecular Mechanism of Huangqi in Treating Heart Failure Using Network Pharmacology

Heart failure (HF), a clinical syndrome with a high incidence due to various reasons, is the advanced stage of most cardiovascular diseases. Huangqi is an effective treatment for cardiovascular disease, which has multitarget, multipathway functions. Therefore, we used network pharmacology to explore the molecular mechanism of Huangqi in treating HF. In this study, 21 compounds of Huangqi, which involved 407 targets, were obtained and reconfirmed using TCMSP and PubChem databases. Moreover, we used Cytoscape 3.7.1 to construct compound-target network and screened the top 10 compounds. 378 targets related to HF were obtained from CTD and GeneCards databases and HF-target network was constructed by Cytoscape 3.7.1. The 46 overlapping targets of HF and Huangqi were gotten by Draw Venn Diagram. STRING database was used to set up a protein-protein interaction network, and MCODE module and the top 5 targets with the highest degree for overlapping targets were obtained. GO analysis performed by Metascape indicated that the overlapping targets were mainly enriched in blood vessel development, reactive oxygen species metabolic process, response to wounding, blood circulation, and so on. KEGG analysis analyzed by ClueGO revealed that overlapping targets were mainly enriched in AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, c-type lectin receptor signaling pathway, relaxin signaling pathway, and so on. Finally, molecular docking showed that top 10 compounds of Huangqi also had good binding activities to important targets compared with digoxin, which was carried out in CB-Dock molecular docking server. In conclusion, Huangqi has potential effect on regulating overlapping targets and GE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, HIF-1 signaling pathway, and so on to be a latent multitarget, multipathway treatment for HF.