Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis

Identification of Differentially Expressed Genes in Human Colorectal Cancer Using RNASeq Data Validated on the Molecular Level with Real-Time PCR

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study's analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival.

Identification of hub genes and pathways in hepatitis B virus-associated hepatocellular carcinoma: A comprehensive in silico study

Background and Aim

The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions.

Methods

GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis (GEPIA) analysis was carried out to confirm the target genes.

Results

One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of four genes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis.

Conclusion

In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.

Potential key genes are expected to become biomarker for early diagnosis of colorectal cancer through bioinformatics analysis

Colorectal cancer (CRC) is the leading cause of cancer-related deaths in the world. The aim of this study was to identify the potential key genes, and associated pathways for early-onset CRC through bioinformatics methods. We integrated the gene expression patterns of CRC from three RNAseq datasets (GSE8671, GSE20916, GSE39582) from GEO database to identify DEGs between CRC and normal samples. We established a gene co-expression network through WGCNA. Through the WGCNA calculation, the genes were divided into six modules. The WGCNA analysis screened 242 genes associated with pathological stage and colorectal adenocarcinoma, 31 of which had the ability to predict OS with an AUC >0.7. The GSE39582 dataset identified 2040 DEGs between the CRC and normal samples. The two were intersected to obtain two genes: NPM1 and PANK3. The two genes were used as a threshold to divide the samples into high group and low group for survival analysis. Survival analysis showed that increased expression of both genes was significantly associated with a poorer prognosis. These two genes (NPM1 and PANK3) could be possible marker genes for early diagnosis of CRC, providing ideas for other experimental studies in the future.

Knockdown of the UL-16 binding protein 1 promotes osteoblast differentiation of human mesenchymal stem cells by activating the SMAD2/3 pathway

Osteoporosis is caused by the imbalance of osteoblasts and osteoclasts. The regulatory mechanisms of differentially expressed genes (DEGs) in pathogenesis of osteoporosis are of significant and needed to be further investigated. GSE100609 dataset downloaded from Gene Expression Omnibus (GEO) database was used to identified DEGs in osteoporosis patients. KEGG analysis was conducted to demonstrate signaling pathways related to enriched genes. Osteoporosis patients and the human mesenchymal stem cells (hMSCs) were obtained for in vivo and in vitro resaerch. Lentivirus construction and viral infection was used to knockdown genes. mRNA expression and protein expression were detected via qRT-PCR and western blot assay separately. Alkaline phosphatase (ALP) activity detection, alizarin Red S (ARS) staining, and expression of bone morphogenetic protein 2 (BMP2), osteocalcin (OCN) and Osterix were evaluated to determine osteoblast differentiation capacity. UL-16 binding protein 1 (ULBP1) gene was upregulated in osteoporosis and downregulated in differentiated hMSCs. Knockdown of ULBP1 increased ALP activity, mineralization ability evaluated by ARS staining, expression of BMP2, OCN and Osterix in differentiated hMSCs. Furthermore, rescue experiment demonstrated that suppressed ULBP1 boosted osteoblast differentiation by activating TNF-β signaling pathway. Knockdown of ULBP1 gene could promoted osteoblast differentiation by activating TNF-β signaling pathway in differentiated hMSCs. ULBP1 may be a the Achilles' heel of osteoporosis, and suppression of ULBP1 could be a promising treatment for osteoporosis.

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.

Inhibition of MiR-155 Using Exosomal Delivery of Antagomir Can Up-Regulate PTEN in Triple Negative Breast Cancer

BACKGROUND

The most aggressive form of breast cancer (BC) is Triple-Negative BC (TNBC), with the poorest prognosis, accounting for nearly 15% of all cases. Since there is no effective treatment, novel strategies, especially targeted therapies, are essential to treat TNBC. Exosomes are nano-sized microvesicles derived from cells and transport various intracellular cargoes, including microRNAs (miRNAs). MiRNAs, small non-coding RNA, are an influential factor in the development of cancerous transformations in cells.

METHOD

Bioinformatics analysis of genes related to TNBC revealed that PTEN plays a crucial role in the disease. Relative expression of this gene was analyzed with RT-qPCR in 14 TNBC clinical samples. Electroporation was used to load miRNA antagomir into exosomes extracted from the conditioned medium. Then, the expression of miR-155 and PTEN was evaluated in MDA-MB-231 cells treated with antagomir-loaded exosomes.

RESULTS

Based on the bioinformatics analysis, miR-155 is a potent inhibitor of PTEN. Following treatment with antagomir-loaded exosomes, RT-qPCR showed significantly reduced miR- 155 and increased PTEN levels in MDA-MB-231 cells.

CONCLUSION

Based on the results of this study, exosomes can be effectively used as a cargo of oligonucleotides like miRNA mimics and antagomirs in targeted therapies.

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration

BACKGROUND

Age-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis.

METHODS

This study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database.

RESULTS

Eleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1).

CONCLUSIONS

Four possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

Multi-omics prognostic signatures of IPO11 mRNA expression and clinical outcomes in colorectal cancer using bioinformatics approaches

The most prevalent malignant illness of the gastrointestinal system, colorectal cancer, is the third most prevalent cancer in males and the second most prevalent cancer in women. Importin-11 is a protein that acts as a regulator of cancer cell proliferation in colorectal tumours by conveying β -catenin to the cell nucleus. However, the IPO11 gene was found to encode a protein called Importin-11, which functions as a nucleus importer for the cell. As a result, preventing β -catenin from entering the nucleus requires blocking Importin-11. As a result, we conducted a multi-omics investigation to assess IPO11 gene potential as a therapeutic biomarker for human colorectal cancer (CC). Oncomine, GEPIA2, immunohisto-chemistry, and UALCAN databases were used to analyses the mRNA expression profiles of IPO11 in CC. The investigation has yielded clear evidence of the increase of IPO11 expression in CC subtypes, as indicated by the data acquired. Analysing CC research from the cBioPortal database, the study discovered three new missense mutations in the importin-11 protein sequence at a frequency of 0.00-1.50% copy number changes. Additionally, the Kaplan-Meier plots demonstrated a strong connection concerning IPO11 downregulation and a poorer CC patient survival rate. The co-expressed gene profile of IPO11 was likewise associated with the onset of CC. IPO11 co-expressed gene profile was also linked to CC development. Moreover, the correlation analysis using bc-GenExMiner and the UCSC Xena server identified KIF2A as the most positively co-expressed gene. The study found that KIF2A and its co-expressed genes were involved in a wide variety of cancer progression pathways using the Enrichr database. Cumulatively, this result will not only provide new information about the expression of IPO11 associated with CC progression and patient survival, but could also serve as a therapeutic biomarker for treating CC in a significant and worthwhile manner.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00259-2.

Label-free based quantitative proteomics analysis to explore the molecular mechanism of gynecological cold coagulation and blood stasis syndrome

Cold coagulation and blood stasis (CCBS) syndrome is one of the common traditional Chinese medicine (TCM) syndromes of gynecological diseases. However, the molecular mechanism of CCBS syndrome is still unclear. Thus, there is a need to reveal the occurrence and regulation mechanism of CCBS syndrome, in order to provide a theoretical basis for the treatment of CCBS syndrome in gynecological diseases. The plasma proteins in primary dysmenorrhea (PD) patients with CCBS syndrome, endometriosis (EMS) patients with CCBS syndrome, and healthy women were screened using Label-free quantitative proteomics. Based on the TCM theory of "same TCM syndrome in different diseases," the differentially expressed proteins (DEPs) identified in each group were subjected to intersection mapping to obtain common DEPs in CCBS syndrome. The DEPs of gynecological CCBS syndrome in the intersection part were again cross-mapped with the DEPs of gynecological CCBS syndrome obtained by the research group according to the TCM theory of "different TCM syndromes in same disease" theory in the early stage, so as to obtain the DEPs of gynecological CCBS syndrome that were shared by the two parts. The common DEPs were subjected to bioinformatics analysis, and were verified by enzyme-linked immunosorbent assay (ELISA). A total of 67 common DEPs were identified in CCBS syndrome, of which 33 DEPs were upregulated and 34 DEPs were downregulated. The functional classification of DEPs involved in metabolic process, energy production and conversion, immune system process, antioxidant activity, response to stimulus, and biological adhesion. The subcellular location mainly located in the cytoplasm, nucleus, and extracellular. Gene ontology (GO) enrichment analysis showed that the upregulated DEPs mainly concentrated in lipid transport, cell migration, and inflammatory reaction, and the downregulated DEPs mostly related to cell junction, metabolism, and energy response. Protein domain enrichment analysis and clustering analysis revealed that the DEPs mainly related to cell proliferation and differentiation, cell morphology, metabolism, and immunity. The Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis clustering analysis showed that the upregulated DEPs were involved in inflammation and oxidative damage, while the downregulated DEPs were involved in inflammation, cell adhesion, cell apoptosis, and metabolism. The results of ELISA showed significantly increased levels of Cell surface glycoprotein MUC18 (MCAM) and Apolipoprotein C1 (APOC1), and significantly decreased levels of Vasodilator-stimulated phosphoprotein (VASP), Fatty acid-binding protein 5 (FABP5), and Vinculin (VCL) in patients with CCBS syndrome compared with healthy women. We speculated that cold evil may affect the immune process, inflammatory response, metabolic process, energy production and conversion, oxidative damage, endothelial cell dysfunction, and other differential proteins expression to cause CCBS syndrome in gynecological diseases.

Integrated bioinformatics approach to unwind key genes and pathways involved in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the fifth leading cause of death in India. Until now, the exact pathogenesis concerning CRC signaling pathways is largely unknown; however, the diseased condition is believed to deteriorate with lifestyle, aging, and inherited genetic disorders. Hence, the identification of hub genes and therapeutic targets is of great importance for disease monitoring.

OBJECTIVE

Identification of hub genes and targets for identification of candidate hub genes for CRC diagnosis and monitoring.

MATERIALS AND METHODS

The present study applied gene expression analysis by integrating two profile datasets (GSE20916 and GSE33113) from NCBI-GEO database to elucidate the potential key candidate genes and pathways in CRC. Differentially expressed genes (DEGs) between CRC (195 CRC tissues) and healthy control (46 normal mucosal tissue) were sorted using GEO2R tool. Further, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed using Cluster Profiler in Rv. 3.6.1. Moreover, protein-protein interactions (PPI), module detection, and hub gene identification were accomplished and visualized through the Search Tool for the Retrieval of Interacting Genes, Molecular Complex Detection (MCODE) plug-in of Cytoscape v3.8.0. Further hub genes were imported into ToppGene webserver for pathway analysis and prognostic expression analysis was conducted using Gene Expression Profiling Interactive Analysis webserver.

RESULTS

A total of 2221 DEGs, including 1286 up-regulated and 935down-regulated genes mainly enriched in signaling pathways of NOD-like receptor, FoxO, AMPK signalling and leishmaniasis. Three key modules were detected from PPI network using MCODE. Besides, top 20 high prioritized hub genes were selected. Further, prognostic expression analysis revealed ten of the hub genes, namely IL1B, CD44, Glyceraldehyde-3-phosphate dehydrogenase (GAPDH, MMP9, CREB1, STAT1, vascular endothelial growth factor (VEGFA), CDC5 L, Ataxia-telangiectasia mutated (ATM + and CDH1 to be differently expressed in normal and cancer patients.

CONCLUSION

The present study proposed five novel therapeutic targets, i.e., ATM, GAPDH, CREB1, VEGFA, and CDH1 genes that might provide new insights into molecular oncogenesis of CRC.

Identification of brain endothelial cell-specific genes and pathways in ischemic stroke by integrated bioinformatical analysis

BACKGROUND

Ischemic stroke (IS) is a life-threatening condition with limited treatment options; thus, finding the potential key genes for novel therapeutic targets is urgently needed. This study aimed to explore novel candidate genes and pathways of brain microvessel endothelial cells (ECs) in IS by bioinformatics analysis.

MATERIALS AND METHODS

The gene expression profiles of brain tissues or brain ECs in IS mice were downloaded from the online gene expression omnibus (GEO) to obtain the differentially expressed genes (DEGs) by R software. Functional enrichment analyses were used to cluster the functions and signaling pathways of the DEGs, while DEG-associated protein-protein interaction network was performed to identify hub genes. The target microRNAs and competitive endogenous RNA networks of key hub genes were constructed by Cytoscape.

RESULTS

Totally 84 DEGs were obtained from 6 brain tissue samples and 4 brain vascular EC samples both from IS mice in the datasets GSE74052 and GSE137482, with significant enrichment in immune responses, such as immune system processes and T-cell activation. Eight hub genes filtered by Cytoscape were validated by two other GEO datasets, wherein key genes of interest were verified by reverse transcription-polymerase chain reaction using an in vitro ischemic model of EC cultures. Our data indicated that AURKA and CENPF might be potential therapeutic target genes for IS, and Malat1/Snhg12/Xist-miR-297b-3p-CENPF, as well as Mir17 hg-miR-34b-3p-CENPF, might be RNA regulatory pathways to control IS progression.

CONCLUSIONS

Our work identified two brain EC-specific expressed genes in IS, namely, AURKA and CENPF, as potential gene targets for IS treatment. In addition, we presented miR-297b-3p/miR-34b-3p-CENPF as the potential RNA regulatory axes to prevent pathogenesis of IS.

Combined with multiplex and network analysis to reveal the key genes and mechanisms of nonalcoholic fatty liver disease

BACKGROUND AND AIMS

Non-alcoholic fatty liver disease (NAFLD) has become a significant cause of chronic liver disease in developed countries, as a result of the worldwide trend of obesity and associated metabolic syndrome. Obesity and high-fat diet (HFD) are very common in patients with NAFLD. However, how to screen out key differentially expressed genes (DEGs) is a challenging task. The purpose of this study is to study the screen of key genes and pathways of HFD on the formation process of non-alcoholic fatty liver through network pharmacological analysis.

METHODS

In this study, 173 genes associated with NAFLD were collected from the Gene Expression Omnibus (GEO) database. To find significant genes and pathways, combine network clustering analysis, topology analysis, and pathway analysis.

RESULTS

The results showed that there were four key signaling pathways related to HFD, including complement cascade, Atorvastatin ADME, Asthma and Aflatoxin activation and detoxification. In addition, we identified six representative key genes, including Ccl5, Tlr2, Cd274, Cxcl10, Cxcl9 and Cd74, and screened three intersecting genes in Mus musculus and Homo sapiens sample, including C3, F2 and C7.

CONCLUSIONS

In conclusion, our study constructed the NAFLD gene regulatory network of C57BL/6J mice for the first time and jointly analyzed the Mus musculus samples and Homo sapiens samples. It provides new insights for identifying potential biomarkers and valuable therapeutic clues, and puts forward a new method for web-based research. These findings may provide potential targets for early diagnosis, effective therapy and prognostic markers of NAFLD.

Identification of hub genes and microRNAs with prognostic values in esophageal cancer by integrated analysis

Esophageal cancer (EC) is the eighth most common cancer in the world, and the sixth most common cause of cancer-related mortality. The aim of the present study was to identify cell and molecular mechanisms involved in EC, and to provide the potential targets for diagnosis and treatment. Here, a microarray dataset (GSE20347) was screened to find differentially expressed genes (DEGs). Different bioinformatic methods were used to analyze the identified DEGs. The up-regulated DEGs were significantly involved in different biological processes and pathways including extracellular matrix organization and ECM-receptor interaction. FN1, CDK1, AURKA, TOP2A, FOXM1, BIRC5, CDC6, UBE2C, TTK, and TPX2 were identified as the most important genes among the up-regulated DEGs. Our analysis showed that has-miR-29a-3p, has-miR-29b-3p, has-miR-29c-3p, and has-miR-767-5p had the largest number of common targets among the up-regulated DEGs. These findings strengthen the understanding of EC development and progression, as well as representing potential markers for EC diagnosis and treatment.

Identification of key genes and small molecule drugs in osteoarthritis by integrated bioinformatics analysis

Background

Osteoarthritis (OA) is a common joint degenerative disease that can affect multiple joints. Genetic events may play an important regulatory role in the early stages of the disease, but the specific mechanisms have not yet been fully elucidated. The main purpose of this study was to screen for disease-causing hub genes and effective small molecule drugs to reveal the pathogenesis of OA and to develop novel drugs for treatment.

Methods

Two gene expression profile datasets, GSE55235 and GSE55457, were integrated and further analyzed. The consistently differentially expressed genes (DEGs) were identified, and functional annotation and pathway analysis of these genes were performed with GO and KEGG. A protein-protein interaction network (PPI) of the DEGs was generated using STRING, and potential small molecule drug screening was performed on the connectivity map (CMap).

Results

A total of 158 consistently differentially expressed genes were identified from the two profile datasets. The functions of these DEGs are mainly related to the TNF signaling pathway, osteoclast differentiation, MAPK signaling pathway and so on. The PPI network contains 127 nodes and 1802 edges, and the ten hub genes were interleukin 6 (IL6), vascular endothelial growth factor A (VEGFA)and so on. 7 small molecule drugs were identified as potential interactors with these hubs.

Conclusions

This study explains the disorder of expression in the pathological process of OA at transcriptome, which will help to understand the pathogenesis of OA.

Dynamic heterogeneity of colorectal cancer during progression revealed clinical risk-associated cell types and regulations in single-cell resolution and spatial context

Background

Tumor heterogeneity is contributed by tumor cells and the microenvironment. Dynamics of tumor heterogeneity during colorectal cancer (CRC) progression have not been elucidated.

Methods

Eight single-cell RNA sequencing (scRNA-seq) data sets of CRC were included. Milo was utilized to reveal the differential abundance of cell clusters during progression. The differentiation trajectory was imputed by using the Palantir algorithm and metabolic states were assessed by using scMetabolism. Three spatial transcription sequencing (ST-seq) data sets of CRC were used to validate cell-type abundances and colocalization. Cancer-associated regulatory hubs were defined as communication networks affecting tumor biological behaviors. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemistry staining were performed for validation.

Results

TM4SF1+, SOX4+, and MKI67+ tumor cells; CXCL12+ cancer-associated fibroblasts; CD4+ resident memory T cells; Treg; IgA+ plasma cells; and several myeloid subsets were enriched in stage IV CRC, most of which were associated with overall survival of patients. Trajectory analysis indicated that tumor cells from patients with advanced-stage CRC were less differentiated, when metabolic heterogeneity showed a highest metabolic signature in terminal states of stromal cells, T cells, and myeloid cells. Moreover, ST-seq validated cell-type abundance in a spatial context and also revealed the correlation of immune infiltration between tertiary lymphoid structures and tumors followed by validation in our cohort. Importantly, analysis of cancer-associated regulatory hubs revealed a cascade of activated pathways including leukocyte apoptotic process, MAPK pathway, myeloid leukocyte differentiation, and angiogenesis during CRC progression.

Conclusions

Tumor heterogeneity was dynamic during progression, with the enrichment of immunosuppressive Treg, myeloid cells, and fibrotic cells. The differential state of tumor cells was associated with cancer staging. Assessment of cancer-associated regulatory hubs suggested impaired antitumor immunity and increased metastatic ability during CRC progression.

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.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

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.

Prognostic value of des-γ-carboxyprothrombin in patients with AFP-negative HCC treated with TACE

In patients with AFP-negative hepatocellular carcinoma (HCC), des-γ-carboxyprothrombin (DCP) is an important prognostic indicator for the preoperative assessment of transarterial chemoembolization (TACE). However, the association between the serum DCP levels and the degree of progression and prognosis of patients with AFP-negative HCC treated with TACE has not been thoroughly investigated to date, and the molecular mechanism is also unclear. The present study retrospectively analyzed the clinical data of 107 patients with AFP-negative HCC treated with TACE and divided them into two groups based on the median serum DCP levels. The association between DCP and the clinical characteristics of the patients was analyzed, and the survival data were analyzed using Kaplan-Meier curves and Cox regression models. The results demonstrated that the median follow-up time was 755 days (range, 64-1,556 days), and patients in the low-DCP group (n=11; 20.8%) had a lower mortality rate than those in the high-DCP group (n=20; 37.0%). Cox multivariate regression analysis suggested that preoperative lymph node metastasis [hazard ratio (HR), 3.903; 95% CI, 1.778-8.519; P=0.001] and DCP group (HR, 2.465; 95% CI, 1.038-5.854; P=0.041) were independent risk factors. Furthermore, the Gene Expression Omnibus database was utilized to screen differentially expressed mRNAs. Enrichment analyses were then performed, and a protein-protein interaction (PPI) network was constructed to identify hub genes. A total of 169 differentially expressed genes were screened. Enrichment analyses revealed that cancer-related and ribosomal pathways were significantly enriched. Furthermore, 10 hub genes were identified in the PPI network by counting the number of gene interactions, the majority of which belonged to the ribosomal protein (RPS) family, and the top three significant genes were RPS23, RPS11 and RPS3A. In patients with AFP-negative HCC, higher serum DCP levels were associated with poor prognosis after TACE. This may be associated with genes such as those belonging to the RPS family, which may contribute to future personalized therapy for this disease.

A Novel Insight into Paraptosis-Related Classification and Signature in Lower-Grade Gliomas

Lower-grade gliomas (LGG) are the most common intracranial malignancies that readily evolve to high-grade gliomas and increase drug resistance. Paraptosis is defined as a nonapoptotic form of programmed cell death, which is gradually focused on patients with gliomas to develop treatment options. However, the specific role of paraptosis in LGG and its correlation is still vague. In this study, we first establish the novel paraptosis-based prognostic model for LGG patients. The relevant data of LGG patients were acquired from The Cancer Genome Atlas database, and we found that LGG patients could be divided into three different clusters based on paraptosis via consensus cluster analysis. Through least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis, 10-paraptosis-related gene (PRG) signatures (CDK4, TNK2, DSTYK, CDKN3, CCR4, CASP9, HSPA5, RGR, LPAR1, and PDCD6IP) were identified to separate LGG patients into high- and low-risk subgroups successfully. The Kaplan-Meier analysis and time-dependent receiver-operating characteristic showed that the performances of predicting overall survival (OS) were dramatically high. The parallel results were reappeared and verified by using the Chinese Glioma Genome Atlas and Gene Expression Omnibus databases. Independent prognostic analysis and nomogram construction implied that risk scores could be considered the independent factor to predict OS. Enrichment analysis indicated that immune-related biological processes were generally enriched, and different immune statuses were highly infiltrated in high-risk group. We also confirmed the potential relationship of 10-PRG signatures and drug sensitivity of Food and Drug Administration-approved drugs. In summary, our findings provide a novel knowledge of paraptosis status and crucial direction to further explore the role of PRG signatures in LGG.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Astragalus mongholicus Bunge and Curcuma aromatica Salisb. inhibits liver metastasis of colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway

BACKGROUND

One of the most challenging aspects of colon cancer (CC) prognosis and treatment is liver-tropic metastasis. Astragalus mongholicus Bunge-Curcuma aromatica Salisb. (AC) is a typical medication combination for the therapy of many malignancies. Our previous studies found that AC intervention inhibits liver metastasis of colon cancer (LMCC). Nevertheless, the comprehensive anti-metastasis mechanisms of AC have not been uncovered.

METHODS

In bioinformatics analysis, RNA-seq data of CC and LMCC patients were collected from TCGA and GEO databases, and differentially expressed genes (DEGs) were identified. The biological processes and signaling pathways involved in DEGs were enriched by GO and KEGG. The protein-protein interaction (PPI) network of DEGs was established and visualized using the Cytocape software, followed by screening Hub genes in the PPI network using Degree value as the criterion. Subsequently, the expression and survival relevance of Hub gene in COAD patients were verified. In the experimental study, the effects of AC on the inhibition of colon cancer growth and liver metastasis were comprehensively evaluated by cellular and animal models. Finally, based on the results of bioinformatics analysis, the possible mechanisms of AC inhibition of colon cancer EMT and liver metastasis were explored by in vivo and in vitro pharmacological experiments.

RESULTS

In this study, we obtained 2386 DEGs relevant to LMCC from the COAD (colon adenocarcinoma) and GSE38174 datasets. Results of GO gene function and KEGG signaling pathway enrichment analysis suggested that cellular EMT (Epithelial-mesenchymal transition) biological processes, Cytokine-cytokine receptor interaction and PI3K/Akt signaling pathways might be closely related to LMCC mechanism. We then screened for CXCL8, the core hub gene with the highest centrality within the PPI network of DEGs, and discovered that CXCL8 expression was negatively correlated with the prognosis of COAD patients. In vitro and in vivo experimental evidence presented that AC significantly inhibited colon cancer cell proliferation, migration and invasion ability, and suppressed tumor growth and liver metastasis in colon cancer orthotopic transplantation mice models. Concomitantly, AC significantly reduced CXCL8 expression levels in cell supernatants and serum. Moreover, AC reduced the expression and transcription of genes related to the PI3K/AKT pathway while suppressing the EMT process in colon cancer cells and model mice.

CONCLUSIONS

In summary, our research predicted the potential targets and pathways of LMCC, and experimentally demonstrated that AC might inhibit the growth and liver metastasis in colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway, which may facilitate the discovery of mechanisms and new therapeutic strategies for LMCC.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

HypoxaMIRs: Key Regulators of Hallmarks of Colorectal Cancer

Hypoxia in cancer is a thoroughly studied phenomenon, and the logical cause of the reduction in oxygen tension is tumor growth itself. While sustained hypoxia leads to death by necrosis in cells, there is an exquisitely regulated mechanism that rescues hypoxic cells from their fatal fate. The accumulation in the cytoplasm of the transcription factor HIF-1α, which, under normoxic conditions, is marked for degradation by a group of oxygen-sensing proteins known as prolyl hydroxylases (PHDs) in association with the von Hippel-Lindau anti-oncogene (VHL) is critical for the cell, as it regulates different mechanisms through the genes it induces. A group of microRNAs whose expression is regulated by HIF, collectively called hypoxaMIRs, have been recognized. In this review, we deal with the hypoxaMIRs that have been shown to be expressed in colorectal cancer. Subsequently, using data mining, we analyze a panel of hypoxaMIRs expressed in both normal and tumor tissues obtained from TCGA. Finally, we assess the impact of these hypoxaMIRs on cancer hallmarks through their target genes.

Granzymes-Their Role in Colorectal Cancer

Colorectal cancer (CRC) is among the most common malignancies worldwide. CRC is considered a heterogeneous disease due to various clinical symptoms, biological behaviours, and a variety of mutations. A number of studies demonstrate that as many as 50% of CRC patients have distant metastases at the time of diagnosis. However, despite the fact that social and medical awareness of CRC has increased in recent years and screening programmes have expanded, there is still an urgent need to find new diagnostic tools for early detection of CRC. The effectiveness of the currently used classical tumour markers in CRC diagnostics is very limited. Therefore, new proteins that play an important role in the formation and progression of CRC are being sought. A number of recent studies show the potential significance of granzymes (GZMs) in carcinogenesis. These proteins are released by cytotoxic lymphocytes, which protect the body against viral infection as well specific signalling pathways that ultimately lead to cell death. Some studies suggest a link between GZMs, particularly the expression of Granzyme A, and inflammation. This paper summarises the role of GZMs in CRC pathogenesis through their involvement in the inflammatory process. Therefore, it seems that GZMs could become the focus of research into new CRC biomarkers.

Bioinformatics analysis for identifying micro-RNAs, long noncoding RNAs, transcription factors, and immune genes regulatory networks in diabetic cardiomyopathy using an integrated bioinformatics analysis

OBJECTIVES

We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools.

METHODS

Original gene expression profiles were obtained from the GSE21610 and GSE112556 data sets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein-protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas data set.

RESULTS

The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues.

CONCLUSIONS

The identified hub genes could be associated with DCM pathogenesis and could be used for treating DCM.

Identification of Down-Regulated ADH1C is Associated With Poor Prognosis in Colorectal Cancer Using Bioinformatics Analysis

Colorectal cancer (CRC) is the second most deadly cancer in the whole world, with the underlying mechanisms largely indistinct. Therefore, we aimed to identify significant pathways and genes involved in the initiation, formation and poor prognosis of CRC using bioinformatics methods. In this study, we compared gene expression profiles of CRC cases with those from normal colorectal tissues from three chip datasets (GSE33113, GSE23878 and GSE41328) to identify 105 differentially expressed genes (DEGs) that were common to the three datasets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that the highest proportion of up-regulated DEGs was involved in extracellular region and cytokine-cytokine receptor interaction pathways. Integral components of membrane and bile secretion pathways were identified as containing down-regulated DEGs. 13 hub DEGs were chosen and their expression were further validated by GEPIA. Only four DEGs (ADH1C, CLCA4, CXCL8 and GUCA2A) were associated with a significantly lower overall survival after the prognosis analysis. Lower ADH1C protein level and higher CXCL8 protein level were verified by immunohistochemical staining and western blot in clinical CRC and normal colorectal tissues. In conclusion, our study indicated that the extracellular tumor microenvironment and bile metabolism pathways play critical roles in the formation and progression of CRC. Furthermore, we confirmed ADH1C being down-regulated in CRC and reported ADH1C as a prognostic predictor for the first time.

The initial expression alterations occurring to transcription factors during the formation of breast cancer: Evidence from bioinformatics

BACKGROUND

Breast cancer (BC) is the leading malignancy among women worldwide.

AIM

This work aimed to present a comprehensively bioinformatic analysis of gene expression profiles and to identify the hub genes during BC tumorigenesis, providing potential biomarkers and targets for the diagnosis and therapy of BC.

MATERIALS & METHODS

In this study, multiple public databases, bioinformatics approaches, and online analytical tools were employed and the real-time reverse transcription polymerase chain reaction was implemented.

RESULTS

First, we identified 10, 107, and 3869 differentially expressed genes (DEGs) from three gene expression datasets (GSE9574, GSE15852, and GSE42568, covering normal, para-cancerous, and BC samples, respectively), and investigated different biological functions and pathways involved. Then, we screened out 8, 16, and 29 module genes from these DEGs, respectively. Next, 10 candidate genes were determined through expression and survival analyses. We noted that seven candidate genes JUN, FOS, FOSB, EGR1, ZFP36, CFD, and PPARG were downregulated in BC compared to normal tissues and lower expressed in aggressive types of BC (basal, HER2+ , and luminal B), TP53 mutation group, younger patients, higher stage BC, and lymph node metastasis BC, while CD27, PSMB9, and SELL were upregulated. The present study discovered that the expression levels of these candidate genes were correlated with the infiltration of immune cells (CD8+ T cell, macrophage, natural killer [NK] cell, and cancer-associated fibroblast) in BC, as well as biomarkers of immune cells and immune checkpoints. We also revealed that promoter methylation, amplification, and deep deletion might contribute to the abnormal expressions of candidate genes. Moreover, we illustrated downstream-targeted genes of JUN, FOS, FOSB, EGR1, and ZFP36 and demonstrated that these targeted genes were involved in "positive regulation of cell death", "pathways in cancer", "PI3K-Akt signaling pathway", and so on.

DISCUSSION & CONCLUSION

We presented differential gene expression profiles among normal, para-cancerous, and BC tissues and further identified candidate genes that might contribute to tumorigenesis and progression of BC, as potential diagnostic and prognostic targets for BC patients.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Association between chronic periodontitis and the risk of Alzheimer's disease: combination of text mining and GEO dataset

BACKGROUND

Although chronic periodontitis has previously been reported to be linked with Alzheimer's disease (AD), the pathogenesis between the two is unclear. The purpose of this study is to analyze and screen the relevant and promising molecular markers between chronic periodontitis and Alzheimer's disease (AD).

METHODS

In this paper, we analyzed three AD expression datasets and extracted differentially expressed genes (DEGs), then intersected them with chronic periodontitis genes obtained from text mining, and finally obtained integrated DEGs. We followed that by enriching the matching the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein-protein interaction (PPI) network and the matching hub gene. Finally, we verified our data using a different independent AD cohort.

RESULTS

The chronic periodontitis gene set acquired from text abstracting was intersected with the previously obtained three AD groups, and 12 common genes were obtained. Functional enrichment assessment uncovered 12 cross-genes, which were mainly linked to cell morphogenesis involved in neuron differentiation, leading edge membrane, and receptor ligand activity. After PPI network creation, the ten hub genes linked to AD were retrieved, consisting of SPP1, THY1, CD44, ITGB1, HSPB3, CREB1, SST, UCHL1, CCL5 and BMP7. Finally, the function terms in the new independent dataset were used to verify the previous dataset, and we found 22 GO terms and one pathway, "ECM-receptor interaction pathways", in the overlapping functional terms.

CONCLUSIONS

The establishment of the above-mentioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular markers for chronic periodontitis-related AD, which may help the diagnosis and treatment of AD patients in the future.

Genomic and transcriptomic analyses reveal a tandem amplification unit of 11 genes and mutations in mismatch repair genes in methotrexate-resistant HT-29 cells

DHFR gene amplification is commonly present in methotrexate (MTX)-resistant colon cancer cells and acute lymphoblastic leukemia. In this study, we proposed an integrative framework to characterize the amplified region by using a combination of single-molecule real-time sequencing, next-generation optical mapping, and chromosome conformation capture (Hi-C). We identified an amplification unit spanning 11 genes, from the DHFR gene to the ATP6AP1L gene position, with high adjusted interaction frequencies on chromosome 5 (~2.2 Mbp) and a twenty-fold tandemly amplified region, and novel inversions at the start and end positions of the amplified region as well as frameshift insertions in most of the MSH and MLH genes were detected. These mutations might stimulate chromosomal breakage and cause the dysregulation of mismatch repair. Characterizing the tandem gene-amplified unit may be critical for identifying the mechanisms that trigger genomic rearrangements. These findings may provide new insight into the mechanisms underlying the amplification process and the evolution of drug resistance.

Sequencing a large region of DNA containing many surplus copies of genes linked to drug resistance in colon cancer cells may illuminate how these genomic rearrangements arise. Such regions of gene amplification are highly repetitive, making them impossible to sequence using ordinary methods, and little is known about how they are generated. Using advanced methods, Jeong-Sun Seo at Seoul National University Bundang Hospital in South Korea and co-workers sequenced a region of gene amplification in colon cancer cells. The amplified region was approximately 20 times the length of that in healthy cells and contained many copies of an eleven-gene segment, including a gene implicated in drug resistance. The region also contained mutations in chromosomal repair genes which would disrupt repair pathways. These results illuminate the genetic changes that lead to gene amplification and drug resistance in cancer cells.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

INTRODUCTION

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

METHODS

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

RESULTS

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

CONCLUSION

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

Colorectal anastomotic leak: transcriptomic profile analysis

BACKGROUND

Anastomotic leakage in patients undergoing colorectal surgery is associated with morbidity and mortality. Although multiple risk factors have been identified, the underlying mechanisms are mainly unknown. The aim of this study was to perform a transcriptome analysis of genes underlying the development of anastomotic leakage.

METHODS

A set of human samples from the anastomotic site collected during stapled colorectal anastomosis were used in the study. Transcriptomic profiles were generated for patients who developing anastomotic leakage and case-matched controls with normal anastomotic healing to identify genes and biological processes associated with the development of anastomotic leakage.

RESULTS

The analysis included 22 patients with and 69 without anastomotic leakage. Differential expression analysis showed that 44 genes had adjusted P < 0.050, consisting of two upregulated and 42 downregulated genes. Co-functionality analysis of the 150 most upregulated and 150 most downregulated genes using the GenetICA framework showed formation of clusters of genes with different enrichment for biological pathways. The enriched pathways for the downregulated genes are involved in immune response, angiogenesis, protein metabolism, and collagen cross-linking. The enriched pathways for upregulated genes are involved in cell division.

CONCLUSION

These data indicate that patients who develop anastomotic leakage start the healing process with an error at the level of gene regulation at the time of surgery. Despite normal macroscopic appearance during surgery, the transcriptome data identified several differences in gene expression between patients who developed anastomotic leakage and those who did not. The expressed genes and enriched processes are involved in the different stages of wound healing. These provide therapeutic and diagnostic targets for patients at risk of anastomotic leakage.

Transcription factor POU4F2 promotes colorectal cancer cell migration and invasion through hedgehog-mediated epithelial-mesenchymal transition

As a POU homeodomain transcription factor, POU4F2 has been implicated in regulating tumorigenic processes in various cancers. However, the role of POU4F2 in colorectal cancer (CRC) remains unclear. Here, we revealed that POU4F2 functions as a tumor promotor in CRC. Bioinformatics analysis in specimens from CRC patients and expression analysis in CRC cell lines showed that POU4F2 was upregulated at the mRNA and protein levels in CRC. Depletion of POU4F2 suppressed the metastatic phenotypes of CRC cells, including cell migration, invasion, and the expression of epithelial‐mesenchymal transition (EMT) markers. Moreover, depletion of POU4F2 decreased the number of lung metastatic nodes in nude mice. Mechanistically, POU4F2 positively regulated the Hedgehog signaling pathway, as inferred from the downregulation of the expression of sonic Hedgehog homolog, patched 1, Smoothened, and GLI family zinc finger 1 in vitro and vivo following silencing of POU4F2. Furthermore, the SMO agonist SAG reversed the effects of POU4F2 knockdown in CRC. Functionally, POU4F2 contributed to the Hedgehog signaling‐regulated activation of the EMT process and promotion of CRC cell migration and invasion. Collectively, these findings elucidated the role of POU4F2 as a tumor promotor in CRC through the regulation of Hedgehog signaling‐mediated EMT and suggested that POU4F2 suppression might be a promising therapeutic target in inhibiting CRC metastasis.

Network-Based Analysis Reveals Gene Signature in Tip Cells and Stalk Cells

BACKGROUND

Angiogenesis occurs during various physiological or pathological processes such as wound healing and tumor growth. Differentiation of vascular endothelial cells into tip cells and stalk cells initiates formation of new blood vessels. Tip cells and stalk cells are endothelial cells with different biological characteristics and functions.

OBJECTIVE

The aim of this study was to determine the mechanisms of angiogenesis by exploring differences in gene expression of tip cells and stalk cells.

METHODS

Raw data were retrieved from NCBI Gene Expression Omnibus (GSE19284). Data were reanalyzed using bioinformatics methods that employ robust statistical methods, including identification of differentially expressed genes (DEGs) between stalk and tip cells, weighted gene correlation network analysis (WGCNA), gene ontology and pathway enrichment analysis using DAVID tools, integration of protein-protein interaction (PPI) networks and screening of hub genes. DEGs of stalk and tip cells were grouped as dataset A. Gene modules associated with differentiation of stalk and tip cells screened by WGCNA were named dataset B. Further, we retrieved existing markers of angiogenesis from previous experimental studies on tip and stalk cells which we named dataset C. Intersection of datasets A, B and C was used as a candidate gene. Subsequently, we verified the results applying quantitative polymerase chain reaction (Q -PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile.

RESULTS

We identified five candidate genes, including ESM1，CXCR4，JAG1，FLT1 and PTK2 and two pathways, including Rap1 signaling pathway and PI3K-Akt signaling pathway in vascular endothelial cells that differentiate into tip cells and stalk cells using bioinformatic analysis.

CONCLUSION

Bioinformatics approaches provide new avenues for basic research in different fields such as angiogenesis. The findings of this study provide new perspectives and basis for the study of molecular mechanisms of vascular endothelial cell differentiation into stalk and tip cells. Genes and pathways identified in this study are potential biomarkers and therapeutic targets for angiogenesis in tumor.

Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

Background

The molecular prognostic biomarkers of clear cell renal cell carcinoma (ccRCC) are still unknown. We aimed at researching the candidate biomarkers and potential therapeutic targets of ccRCC.

Methods

Three ccRCC expression microarray datasets (include GSE14762, GSE66270 and GSE53757) were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) between ccRCC and normal tissues were explored. The potential functions of identified DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). And then the protein - protein interaction network (PPI) was established to screen the hub genes. After that, the expressions of hub genes were identified by the oncomine database. The hub genes’ prognostic values of patients with ccRCC were analyzed by GEPIA database.

Results

A total of 137 DEGs were identified by utilizing the limma package and RRA method, including 63 upregulated genes and 74 downregulated genes. It is found that 137 DEGs were mainly enriched in 82 functional terms and 24 pathways in accordance with the research results. Thirteen highest-scoring genes were screened as hub genes (include 10 upregulated genes and 3 downregulated candidate genes) by utilizing the PPI network and module analysis. Through integrating the oncoming database and GEPIA database, the author found that C3 and CXCR4 are not only overexpressed in ccRCC, but also associated with the prognosis of ccRCC. Further results could reveal that patients with high C3 expression had a poor overall survival (OS), while patients with high CTSS and TLR3 expressions had a good OS; patients with high C3 and CXCR4 expressions had a poor disease-free survival (DFS), while ccRCC patients with high TLR3 expression had a good DFS.

Conclusion

These findings suggested that C3 and CXCR4 were the candidate biomarkers and potential therapeutic targets of ccRCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08525-w.

Integrative analysis of key candidate genes and signaling pathways in acute coronary syndrome related to obstructive sleep apnea by bioinformatics

Although obstructive sleep apnea (OSA) has been clinically reported to be associated with acute coronary syndrome (ACS), the pathogenesis between the two is unclear. Herein, we analyzed and screened out the prospective molecular marker. To explore the candidate genes, as well as signaling cascades involved in ACS related to OSA, we extracted the integrated differentially expressed genes (DEGs) from the intersection of genes from the Gene Expression Omnibus (GEO) cohorts and text mining, followed by enrichment of the matching cell signal cascade through DAVID analysis. Moreover, the MCODE of Cytoscape software was employed to uncover the protein-protein interaction (PPI) network and the matching hub gene. A total of 17 and 56 integrated human DEGs in unstable angina (UA) and myocardial infarction (MI) group associated with OSAs that met the criteria of |log2 fold change (FC)|≥ 1, adjusted P < 0.05, respectively, were uncovered. After PPI network construction, the top five hub genes associated with UA were extracted, including APP, MAPK3, MMP9, CD40 and CD40LG, whereas those associated with MI were PPARG, MAPK1, MMP9, AGT, and TGFB1. The establishment of the aforementioned candidate key genes, as well as the enriched signaling cascades, provides promising molecular marker for OSA-related ACS, which will to provide a certain predictive value for the occurrence of ACS in OSA patients in the future.

miRNA Clusters with Up-Regulated Expression in Colorectal Cancer

Simple Summary

As miRNAs show the capacity to be used as CRC biomarkers, we analysed experimentally validated data about frequently up-regulated miRNA clusters in CRC tissue. We identified 15 clusters that showed increased expression in CRC: miR-106a/363, miR-106b/93/25, miR-17/92a-1, miR-181a-1/181b-1, miR-181a-2/181b-2, miR-181c/181d, miR-183/96/182, miR-191/425, miR-200c/141, miR-203a/203b, miR-222/221, mir-23a/27a/24-2, mir-29b-1/29a, mir-301b/130b and mir-452/224. Cluster positions in the genome are intronic or intergenic. Most clusters are regulated by several transcription factors, and by long non-coding RNAs. In some cases, co-expression of miRNA with other cluster members or host gene has been proven. miRNA expression patterns in cancer tissue, blood and faeces were compared. The members of the selected clusters target 181 genes. Their functions and corresponding pathways were revealed with the use of Panther analysis. Clusters miR-17/92a-1, miR-106a/363, miR-106b/93/25 and miR-183/96/182 showed the strongest association with metastasis occurrence and poor patient survival, implicating them as the most promising targets of translational research.

Abstract

Colorectal cancer (CRC) is one of the most common malignancies in Europe and North America. Early diagnosis is a key feature of efficient CRC treatment. As miRNAs can be used as CRC biomarkers, the aim of the present study was to analyse experimentally validated data on frequently up-regulated miRNA clusters in CRC tissue and investigate their members with respect to clinicopathological characteristics of patients. Based on available data, 15 up-regulated clusters, miR-106a/363, miR-106b/93/25, miR-17/92a-1, miR-181a-1/181b-1, miR-181a-2/181b-2, miR-181c/181d, miR-183/96/182, miR-191/425, miR-200c/141, miR-203a/203b, miR-222/221, mir-23a/27a/24-2, mir-29b-1/29a, mir-301b/130b and mir-452/224, were selected. The positions of such clusters in the genome can be intronic or intergenic. Most clusters are regulated by several transcription factors, and miRNAs are also sponged by specific long non-coding RNAs. In some cases, co-expression of miRNA with other cluster members or host gene has been proven. miRNA expression patterns in cancer tissue, blood and faeces were compared. Based on experimental evidence, 181 target genes of selected clusters were identified. Panther analysis was used to reveal the functions of the target genes and their corresponding pathways. Clusters miR-17/92a-1, miR-106a/363, miR-106b/93/25 and miR-183/96/182 showed the strongest association with metastasis occurrence and poor patient survival, implicating them as the most promising targets of translational research.

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.

Identification ACTA2 and KDR as key proteins for prognosis of PD-1/PD-L1 blockade therapy in melanoma

Programmed cell death protein 1 (PD-1) /programmed cell death ligand 1 (PD-L1) blockade is an important therapeutic strategy for melanoma, despite its low clinical response. It is important to identify genes and pathways that may reflect the clinical outcomes of this therapy in patients. We analyzed clinical dataset GSE96619, which contains clinical information from five melanoma patients before and after anti-PD-1 therapy (five pairs of data). We identified 704 DEGs using these five pairs of data, and then the number of DEGs was narrowed down to 286 in patients who responded to treatment. Next, we performed KEGG pathway enrichment and constructed a DEG-associated protein-protein interaction network. Smooth muscle actin 2 (ACTA2) and tyrosine kinase growth factor receptor (KDR) were identified as the hub genes, which were significantly downregulated in the tumor tissue of the two patients who responded to treatment. To confirm our analysis, we demonstrated similar expression tendency to the clinical data for the two hub genes in a B16F10 subcutaneous xenograft model. This study demonstrates that ACTA2 and KDR are valuable responsive markers for PD-1/PD-L1 blockade therapy.

CDC20 and PTTG1 are Important Biomarkers and Potential Therapeutic Targets for Metastatic Prostate Cancer

INTRODUCTION

Metastatic prostate cancer (mPCa) is responsible for most prostate cancer (PCa) deaths worldwide. The present study aims to explore the molecular differences between mPCa and PCa.

METHODS

The authors downloaded GSE6752, GSE6919, and GSE32269 from the Gene Expression Omnibus and employed integrated analysis to identify differentially expressed genes (DEGs) between mPCa and PCa. Functional and pathway-enrichment analyses were performed, and a protein-protein interaction (PPI) network and modules were constructed. Clinical mPCa specimens were collected to verify the results by performing RT-qPCR. The Cancer Genome Atlas database was used to conduct a survival analysis, and an immunohistochemical assay was performed. The invasion ability of PCa cells was verified by Transwell assay.

RESULTS

One-hundred six consistently DEGs were found in mPCa compared with PCa. DEGs significantly enriched the positive regulation of cell proliferation, cell division, and cell adhesion in small cell lung cancer and PCa. Cell division, nucleoplasm, and cell cycle were selected from the PPI network, and the top 10 hub genes were selected. CDC20 and PTTG1 with genetic alterations were significantly associated with poorer disease-free survival. Immunohistochemical assay results showed that the expression levels of CDC20 and PTTG1 in mPCa were higher than those in PCa. The results of the migration assay indicated that CDC20 and PTTG1 could enhance the migration ability of PCa cells.

CONCLUSION

The present study revealed that CDC20 and PTTG1 contribute more to migration, progression, and poorer prognoses in mPCa compared with PCa. CDC20 and PTTG1 could represent therapeutic targets in mPCa medical research and clinical studies.

Overexpression of NOP58 as a Prognostic Marker in Hepatocellular Carcinoma: A TCGA Data-Based Analysis

INTRODUCTION

NOP58 ribonucleoprotein, a core component of box C/D small nucleolar ribonucleoproteins, is involved in various cell physiological processes. However, its role in hepatocellular carcinoma (HCC) remains very unclear. We aim to investigate NOP58 expression and its probable prognostic value in patients with HCC based on The Cancer Genome Atlas (TCGA) database.

METHODS

RNA sequencing data and clinicopathological characteristics of patients with HCC were collected from TCGA database. Expression of NOP58 in HCC tissues and normal tissues was analyzed by Wilcoxon rank-sum test. Patients were divided into high and low subgroups according to median expression of NOP58. Logistic regression, gene set enrichment analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were conducted to annotate biological function and immune infiltration of NOP58.

RESULTS

NOP58 was significantly overexpressed in HCC tissues and correlated with significantly high tumor stage [odds ratio (OR) 10.01, 95% confidence interval (CI) 10.01-10.03; P = 0.003], advanced pathologic stage (OR 10.02, 95% CI 10.01-10.03; P < 0.001), advanced histologic stage (OR 10.03, 95% CI 10.02-10.04; P < 0.001), vascular invasion (OR 10.02, 95% CI 10.01-10.03; P = 0.003), poor performance status (OR 10.01, 95% CI 10.01-10.03; P = 0.003), and Mut-TP53 status (OR 10.02, 95% CI 10.01-10.03; P < 0.001). Elevated NOP58 expression had poor disease-specific survival (DSS; P < 0.001), progression-free interval (P = 0.006), and overall survival (OS; P < 0.001). NOP58 expression was independently correlated with OS (HR 1.731, 95% CI 10.037-2.890; P = 0.036). GSEA demonstrated that various cell cycle pathways along with RB-1 pathway, interleukin-10 signaling, regulation of TP53 activity, and P53 downstream pathway were differentially enriched in NOP58 high expression phenotype. NOP58 expression was positively correlated with infiltrating the levels of T helper type 2 (Th2) cells.

CONCLUSIONS

Overexpression of NOP58 is negatively correlated with overall survival in patients with HCC and might be a potential biomarker for prognosis of HCC.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

Prognostic biomarkers and therapeutic targets in oral squamous cell carcinoma: a study based on cross-database analysis

Background

Oral squamous cell carcinoma (OSCC) is a malignant cancer, the survival rate of patients is disappointing. Therefore, it is necessary to identify the driven-genes and prognostic biomarkers in OSCC.

Methods

Four Gene Expression Omnibus (GEO) datasets were integratedly analyzed using bioinformatics approaches, including identification of differentially expressed genes (DEGs), GO and KEGG analysis, construction of protein-protein interaction (PPI) network, selection of hub genes, analysis of prognostic information and genetic alterations of hub genes. ONCOMINE, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases were used to evaluate the expression and prognostic value of hub genes. Tumor immunity was assessed to investigate the functions of hub genes. Finally, Cox regression model was performed to construct a multiple-gene prognostic signature.

Results

Totally 261 genes were found to be dysregulated. 10 genes were considered to be the hub genes. The Kaplan-Meier analysis showed that upregulated SPP1, FN1, CXCL8, BIRC5, PLAUR, and AURKA were related to poor outcomes in OSCC patients. FOXM1 and TPX2 were considered as the potential immunotherapeutic targets with future clinical significance. Moreover, we constructed a nine-gene signature (TEX101, DSG2, SCG5, ADA, BOC, SCARA5, FST, SOCS1, and STC2), which can be utilized to predict prognosis of OSCC patients effectively.

Conclusion

These findings may provide new clues for exploring the molecular mechanisms and targeted therapy in OSCC. The hub genes and risk gene signature are helpful to the personalized treatment and prognostic judgement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00181-1.

Joint analysis of expression levels and histological images identifies genes associated with tissue morphology

Histopathological images are used to characterize complex phenotypes such as tumor stage. Our goal is to associate features of stained tissue images with high-dimensional genomic markers. We use convolutional autoencoders and sparse canonical correlation analysis (CCA) on paired histological images and bulk gene expression to identify subsets of genes whose expression levels in a tissue sample correlate with subsets of morphological features from the corresponding sample image. We apply our approach, ImageCCA, to two TCGA data sets, and find gene sets associated with the structure of the extracellular matrix and cell wall infrastructure, implicating uncharacterized genes in extracellular processes. We find sets of genes associated with specific cell types, including neuronal cells and cells of the immune system. We apply ImageCCA to the GTEx v6 data, and find image features that capture population variation in thyroid and in colon tissues associated with genetic variants (image morphology QTLs, or imQTLs), suggesting that genetic variation regulates population variation in tissue morphological traits.

Identification of potential oncogenes in triple-negative breast cancer based on bioinformatics analyses

Triple-negative breast cancer (TNBC) is a subtype with high rates of metastasis, poor prognosis and limited therapeutic options. The present study aimed to identify the potential pivotal genes for prognosis and treatment in TNBC. A total of two microarray expression datasets, GSE38959 and GSE65212, were downloaded from the Gene Expression Omnibus database, and RNA-sequencing data of breast cancer from The Cancer Genome Atlas database were analyzed to screen out differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The intersection of DEGs was submitted to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A protein-protein interaction (PPI) network was constructed and visualized using Cytoscape software. Furthermore, module, centrality and survival analyses were performed to identify the potential hub genes. Reverse transcription-quantitative (RT-q)PCR analysis was performed to detect the expression levels of key genes in TNBC samples, and 377 DEGs were identified. Functional analysis revealed that the DEGs were significantly involved in cell cycle process, nuclear division and the p53 signaling pathway. A PPI network was constructed with these DEGs, and 66 core genes with high centrality features in module 1 were selected. Relapse-free survival analysis confirmed that high expression levels of five genes [cyclin B1 (CCNB1), GINS complex subunit 2, non-SMC condensin I complex subunit G (NCAPG), minichromosome maintenance 4 (MCM4) and ribonucleotide reductase regulatory subunit M2 (RRM2)] were significantly associated with poor prognosis in TNBC. RT-qPCR analysis demonstrated that CCNB1, NCAPG, MCM4 and RRM2 were significantly upregulated in 25 TNBC tissues compared with adjacent normal breast tissues. Furthermore, gene set enrichment analysis revealed that CCNB1, NCAPG, MCM4 and RRM2 were closely associated with tumor proliferation. Taken together, these results suggest that CCNB1, NCAPG, MCM4 and RRM2 are associated with tumorigenesis and TNBC progression, and thus may act as promising prognostic biomarkers and therapeutic targets for TNBC.

Computational screening of potential glioma-related genes and drugs based on analysis of GEO dataset and text mining

BACKGROUND

Considering the high invasiveness and mortality of glioma as well as the unclear key genes and signaling pathways involved in the development of gliomas, there is a strong need to find potential gene biomarkers and available drugs.

METHODS

Eight glioma samples and twelve control samples were analyzed on the GSE31095 datasets, and differentially expressed genes (DEGs) were obtained via the R software. The related glioma genes were further acquired from the text mining. Additionally, Venny program was used to screen out the common genes of the two gene sets and DAVID analysis was used to conduct the corresponding gene ontology analysis and cell signal pathway enrichment. We also constructed the protein interaction network of common genes through STRING, and selected the important modules for further drug-gene analysis. The existing antitumor drugs that targeted these module genes were screened to explore their efficacy in glioma treatment.

RESULTS

The gene set obtained from text mining was intersected with the previously obtained DEGs, and 128 common genes were obtained. Through the functional enrichment analysis of the identified 128 DEGs, a hub gene module containing 25 genes was obtained. Combined with the functional terms in GSE109857 dataset, some overlap of the enriched function terms are both in GSE31095 and GSE109857. Finally, 4 antitumor drugs were identified through drug-gene interaction analysis.

CONCLUSIONS

In this study, we identified that two potential genes and their corresponding four antitumor agents could be used as targets and drugs for glioma exploration.