Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SIGNIFICANCE: Liver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Role of immune related genes in predicting prognosis and immune response in patients with hepatocellular carcinoma

Immunotherapy has developed rapidly in recent years. This study aimed to establish a prognostic signature for immune-related genes (IRGs) and explore related potential immunotherapies. The RNA-seq transcriptome profiles and clinicopathological information of patients were obtained from The Cancer Genome Atlas. Differentially expressed IRGs in tumors and normal tissues were screened and a risk score signature was constructed to predict the prognosis in patients with hepatocellular carcinoma (HCC). Receiver operating characteristic curves, survival analyses, and correlation analyses were used to explore the clinical application of this model. We further analyzed the differences in clinical characteristics, immune infiltration, somatic mutations, and treatment sensitivity between the high- and low-risk populations characterized by the prognostic models. The immune cell infiltration score and immune-related pathway activity were calculated using the single sample gene set enrichment analysis (ssGSEA) set enrichment analysis. Gene ontology (GO), Kyoto encyclopedia of genes and genomes, and GSEA were used to explore the underlying mechanisms. We constructed a nine-IRG formula to predict the prognosis in HCC patients. The higher the risk score, the higher the malignancy of the tumor and the worse the prognosis. There were significant differences in immune related processes between the high- and low-risk groups. TP53 and CTNNB1 mutations were significantly different between different risk groups. The expression of model gene was closely related to the sensitivity of tumor cells to chemotherapeutic drugs. This risk score model, which is helpful for the individualized treatment of patients with different risk factors, could be a reliable prognostic tool for HCC patients.

Identification of candidate genes associated with clinical onset of Alzheimer's disease

Background and objective

Alzheimer's disease (AD) is the most common type of dementia, with its pathology like beta-amyloid and phosphorylated tau beginning several years before the clinical onset. The aim is to identify genetic risk factors associated with the onset of AD.

Methods

We collected three microarray data of post-mortem brains of AD patients and the healthy from the GEO database and screened differentially expressed genes between AD and healthy control. GO/KEGG analysis was applied to identify AD-related pathways. Then we distinguished differential expressed genes between symptomatic and asymptomatic AD. Feature importance with logistic regression analysis is adopted to identify the most critical genes with symptomatic AD.

Results

Data was collected from three datasets, including 184 AD patients and 132 healthy controls. We found 66 genes to be differently expressed between AD and the control. The pathway enriched in the process of exocytosis, synapse, and metabolism and identified 19 candidate genes, four of which (VSNL1, RTN1, FGF12, and ENC1) are vital.

Conclusion

VSNL1, RTN1, FGF12, and ENC1 may be the essential genes that progress asymptomatic AD to symptomatic AD. Moreover, they may serve as genetic risk factors to identify high-risk individuals showing an earlier onset of AD.

Identification of Potential Prognostic Markers and Key Therapeutic Targets in Hepatocellular Carcinoma Using Weighted Gene Co-Expression Network Analysis: A Systems Biology Approach

BACKGROUND

As the most prevalent form of liver cancer, hepatocellular carcinoma (HCC) ranks the fifth highest cause of cancer-related death worldwide. Despite recent advancements in diagnostic and therapeutic techniques, the prognosis for HCC is still unknown.

OBJECTIVES

This study aimed to identify potential genes contributing to HCC pathogenicity.

MATERIALS AND METHODS

To this end, we examined the GSE39791 microarray dataset, which included 72 HCC samples and 72 normal samples. An investigation of co-expression networks using WGCNA found a highly conserved blue module with 665 genes that were strongly linked to HCC.

RESULTS

APOF, NAT2, LCAT, TTC36, IGFALS, ASPDH, and VIPR1 were the blue module's top 7 hub genes. According to the results of hub gene enrichment, the most related issues in the biological process and KEGG were peroxisome organization and metabolic pathways, respectively. In addition, using the drug-target network, we discovered 19 FDA-approved medication candidates for different reasons that might potentially be employed to treat HCC patients through the modulation of 3 hub genes of the co-expression network (LCAT, NAT2, and VIPR1). Our findings also demonstrated that the 3 scientifically validated miRNAs regulated the co-expression network by the VIPR1 hub gene.

CONCLUSION

We found co-expressed gene modules and hub genes linked with HCC advancement, offering insights into the mechanisms underlying HCC progression as well as some potential HCC treatments.

CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

Background

Hepatocellular carcinoma (HCC) is a highly malignant, recurrent and drug-resistant tumor, and patients often lose the opportunity for surgery when they are diagnosed. Abnormal gene expression is closely related to the occurrence of HCC. The aim of the present study was to identify the differentially expressed genes (DEGs) between tumor tissue and non-tumor tissue of HCC samples in order to investigate the mechanisms of liver cancer.

Methods

The gene expression profile (GSE62232, GSE89377, and GSE112790) was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan–Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression. HPA database was used to reveal the differences in protein level of hub genes.

Results

A total of 50 upregulated DEGs and 122 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of HCC.

Conclusion

Our study suggests that CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM were overexpressed in HCC compared with normal liver tissue. Overexpression of these genes was an unfavorable prognostic factor of HCC patients. Further study is needed to explore the value of them in the diagnosis and treatment of HCC.

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Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Predicting the Clinical Outcome of Lung Adenocarcinoma Using a Novel Gene Pair Signature Related to RNA-Binding Protein

Adenocarcinoma is the most common type of lung cancer, and patients have varying prognoses. RNA-binding proteins (RBP) are deemed to be closely associated with tumorigenesis and development, but the exact mechanism is currently unknown. This study was aimed at constructing a new robust prognostic model based on RNA-binding protein-related gene pair scores for better clinical guidance. The model for this study was constructed based on data of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Prognosis-related RBP gene pair models were created based on differentially expressed genes, and the accuracy of the models was verified in a different age, staging, and other subdatasets. A total of 379 RNA-binding protein-related genes were differentially expressed in tumor tissue. From these genes, we constructed a prognostic model consisting of 33 gene pairs, which were found to be significantly associated with survival in TCGA dataset (P < 0.0001, hazard ratio (HR) = 4.380 (3.139 to 6.111)) and different subdatasets. As expected, the results were verified in the GEO validation cohort (P = 7.8 × 10⁻³, HR = 1.597 (1.095 to 2.325)). We found that the signature exhibited an independent prognostic factor in both the univariate and multivariate Cox regression analyses (P < 0.001). CIBERSORT was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues. CD8 T cells, activated dendritic cells, regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group (P < 0.01). Patients in the high-risk group had significantly higher tumor mutational burden (TMB) (P = 4.953e − 04) and lower levels of immune cells (P = 3.473e − 05) and stromal cells (P = 0.005) in the tumor microenvironment than those in the low-risk group. Furthermore, the Protein-protein interaction (PPI) network and various enrichment analyses have genuinely uncovered the interrelationships and potential functions of the RBP genes within the model. The results of the present study validated the importance of RNA-binding proteins in tumorigenesis and progression and support the RBP gene-related signature as a promising marker for prognosis prediction in lung adenocarcinoma.

Paired like homeodomain 1 and SAM and SH3 domain-containing 1 in the progression and prognosis of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is an aggressive malignancy with high morbidity and mortality rates. In spite of numerous advancements have been made in therapeutic methods, the prognosis of HNSCC patients remains poor. Therefore, investigation of crucial genes during HNSCC tumorigenesis which could be exploited as biomarkers and therapeutic targets is greatly needed. In this study, original data of four independent datasets was downloaded from the Gene Expression Omnibus database and analyzed through R language to screen out differentially expressed genes. Paired like homeodomain 1 and SAM and SH3 domain-containing 1 were selected to be further explored through multiple online databases. Quantitative real-time polymerase chain reaction analysis and immunohistochemistry assay were adopted to validate the downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and statistical analysis indicated their close associations with patient prognosis. In vitro experiments demonstrated the inhibitory effect of paired like homeodomain 1 and SAM and SH3 domain-containing 1 on HNSCC progression. Overall, we identified the aberrant downregulation of paired like homeodomain 1 and SAM and SH3 domain-containing 1 in HNSCC and suggested the potential of utilizing them as therapeutic targets or efficient biomarkers for diagnosis and prognosis evaluation. Our findings may provide novel evidences for the development of new strategies for HNSCC treatment.

A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients

BACKGROUND

Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aim to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.

METHODS

Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival (OS) were identified using Cox regression and LASSO analysis. Then the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. CIBERSORT was used for estimating the fractions of immune cell types.

RESULTS

A total of 397 hypoxia-related DEGs in HCC were detected and three genes (PDSS1, CDCA8 and SLC7A11) among them were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response. Meanwhile, the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.

CONCLUSIONS

Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

NACC-1 regulates hepatocellular carcinoma cell malignancy and is targeted by miR-760

Hepatocellular carcinoma (HCC) is the most prominent form of presentation in liver cancer. It is also the fourth most common cause of cancer-associated deaths globally. The role of nucleus accumbens associated protein-1 (NACC-1) has been evaluated in several cancers. This protein is a transcriptional regulator that regulates a number of significant cellular processes. In the current study, we aimed to understand the role of NACC-1 in HCC. Primarily, we measured the expression of NACC-1 using quantitative real time polymerase chain reaction and western blot analysis. We knocked down the expression of NACC-1 in HCC cell lines Huh7 and HepG2 by transferring a commercially synthesized small interfering RNA and explored the impact of NACC-1 knockdown on cellular growth, migration, invasion, and chemoresistance to doxorubicin. Through bioinformatic analysis, we identified NACC-1 as a potential target of miR-760. Using a dual reporter luciferase assay, we confirmed the predicted target and assessed miR-760-mediated regulation of NACC-1 and rescue of tumorigenic phenotypes. We observed increased expression of NACC-1 in HCC. Furthermore, knockdown of NACC-1 resulted in reduced cell proliferation and invasion and increased susceptibility to doxorubicin-mediated chemosensitivity. Overexpression of miR-760 in HCC cell lines rescued NACC-1-mediated migration and invasion. We revealed that miR-760 regulated NACC-1 expression in HCC. Our data indicated that both miR-760 and NACC-1 could be used as prognostic markers, and miR-760 may have therapeutic benefits for HCC and other cancers.

Identification of Hub Genes and Analysis of Prognostic Values in Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most frequent cancers in the world. In this study, differentially expressed genes (DEGs) between tumor tissues and normal tissues were identified using the comprehensive analysis method in bioinformatics.

MATERIALS AND METHODS

We downloaded 3 mRNA expression profiles from the Gene Expression Omnibus database to identify DEGs between tumor tissues and adjacent normal tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction network was performed to understand the function of DEGs. OncoLnc, which was linked to The Cancer Genome Atlas survival data, was used to investigate the prognostic values of hub genes. The expression of selected hub genes was validated by the quantitative real-time polymerase chain reaction.

RESULTS

A total of 235 DEGs, consisting of 36 upregulated and 199 downregulated genes, were identified between tumor tissue and normal tissue. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis results showed the upregulated DEGs to be significantly enriched in cell division, mid-body, ATP binding and oocyte meiosis pathways. The downregulated DEGs were mainly involved in epoxygenase P450 pathway, extracellular region, oxidoreductase activity and metabolic pathways. Ten hub genes, including Aurora kinase A, Cell division cycle 20, formiminotransferase cyclodeaminase, UBE2C, Cyclin B2, pituitary tumor-transforming gene 1, CDKN3, CKS1B, Topoisomerase-II alpha and KIF20A, were identified as the key genes in HCC. Survival analysis found the expression of hub genes to be significantly correlated with the survival of patients with HCC.

CONCLUSIONS

The present study identified hub genes and pathways in HCC that may be potential targets for diagnosis, treatment and prognostic prediction.

SPP1 functions as an enhancer of cell growth in hepatocellular carcinoma targeted by miR-181c

Patients diagnosed with hepatocellular carcinoma (HCC) suffered a high risk of recurrence and poor prognosis. Identification of differentially expressed genes (DEGs) in HCC provides potential biomarkers for evaluating prognosis and specific therapeutic treatments. In this study, DEGs over-expressed in HCC specimens with a fold change over 2.0 were collected through integrative bioinformatics analysis from GEO datasets. Gene ontology and KEGG pathway enrichment were conducted by applying DAVID database. We noticed Secreted phosphoprotein 1 (SPP1) as one of the signature genes up-regulated in HCC tissues with a close relation to the tumor process. Eighty-seven paired HCC specimens from our medical center were explored to verify the aberrant expression of SPP1 by IHC and qRT-PCR assay. Depletion of SPP1 in HCC Hep3B cells was established. The cell proliferation was impaired in SPP1 depleted cells, along with a resistance of cell apoptosis by down-regulating SPP1. Intriguingly, we further validated a direct interaction between miR-181c and SPP1, which indicated a post-transcriptional regulation mechanism of SPP1 in HCC. Thus, our results suggest that SPP1 may function as an enhancer of HCC growth targeted by miR-181c, and probably provide us an innovational target for HCC diagnose and therapeutic treatment.

Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles

Background

Liver Hepatocellular Carcinoma (LIHC) is one of the major cancers worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting the stage of LIHC from the genomic profile of samples.

Methods

The Cancer Genome Atlas (TCGA) dataset of 173 early stage (stage-I), 177 late stage (stage-II, Stage-III and stage-IV) and 50 adjacent normal tissue samples for 60,483 RNA transcripts and 485,577 methylation CpG sites, was extensively analyzed to identify the key transcriptomic expression and methylation-based features using different feature selection techniques. Further, different classification models were developed based on selected key features to categorize different classes of samples implementing different machine learning algorithms.

Results

In the current study, in silico models have been developed for classifying LIHC patients in the early vs. late stage and cancerous vs. normal samples using RNA expression and DNA methylation data. TCGA datasets were extensively analyzed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early vs. late stages and cancer vs. normal samples of LIHC with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC (Matthew’s correlation coefficient) 0.58 with an accuracy of 78.87% on the validation dataset in discrimination of early and late stage. Additionally, the prediction models developed based on 5 RNA transcripts and 5 CpG sites classify LIHC and normal samples with an accuracy of 96–98% and AUC (Area Under the Receiver Operating Characteristic curve) 0.99. Besides, multiclass models also developed for classifying samples in the normal, early and late stage of cancer and achieved an accuracy of 76.54% and AUC of 0.86.

Conclusion

Our study reveals stage prediction of LIHC samples with high accuracy based on the genomics and epigenomics profiling is a challenging task in comparison to the classification of cancerous and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

miR-1249-3p accelerates the malignancy phenotype of hepatocellular carcinoma by directly targeting HNRNPK

Background

microRNAs (miRNAs) have been implicated to play crucial roles in carcinogenesis. miR‐1249‐3p was reported to be abnormally expressed in multiple human cancers. However, its biological role and the associated underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unknown.

Methods

miR‐1249‐3p expression level in HCC cell lines and normal cell line was measured by quantitative real‐time PCR. Role of miR‐1249‐3p on HCC cell proliferation, colony formation, and invasion was examined by cell counting kit‐8 assay, colony formation assay, and transwell invasion assay, respectively. Luciferase activity reporter assay and western blot were performed to validate whether heterogeneous nuclear ribonucleoprotein K (HNRNPK) was a direct target of miR‐1249‐3p. Effect of miR‐1249‐3p on overall survival of HCC patients was analyzed at KM Plotter website.

Results

We found miR‐1249‐3p expression level was increased, while HNRNPK expression level was decreased in HCC cell lines compared with normal cell line. Knockdown miR‐1249‐3p expression inhibits HCC cell proliferation, colony formation, and cell invasion through regulating HNRNPK in vitro. We also showed high miR‐1249‐3p expression was a predictor for poor overall survival of HCC patients.

Conclusions

These findings about miR‐1249‐3p/HNRNPK pair provide a novel therapeutic method for HCC patients.

Elevated SPINK2 gene expression is a predictor of poor prognosis in acute myeloid leukemia

Acute myeloid leukemia (AML) has a high mortality rate and its clinical management remains challenging. The aim of the present study was to identify the hub genes involved in AML. In order to do so, the gene expression data of the GSE9476 database, including 26 AML and 10 normal samples, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were then identified via bioinformatics analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed on DEGs. Furthermore, the most upregulated genes were selected for further investigation in the Oncomine, gene expression profiling interactive analysis and UALCAN datasets. In total, 1,744 upregulated and 1,956 downregulated genes were detected. The GO and KEGG results revealed that upregulated genes were enriched in metabolic processes, while downregulated genes were associated with the immune response. Serine protease inhibitor Kazal-type 2 (SPINK2) ranked first among all the upregulated genes and was regarded as a hub gene in the development of AML. The overexpression of SPINK2 was validated in 12 patients with AML from the Linyi Central Hospital and in data from the Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Furthermore, the UALCAN and GEPIA datasets demonstrated that patients with high SPINK2 levels had shorter survival times. In conclusion, the results from the present study revealed that the SPINK2 gene was upregulated in patients with AML and that elevated SPINK2 expression was associated with poor outcomes in these patients.

Identification and analysis of genes associated with head and neck squamous cell carcinoma by integrated bioinformatics methods

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide, exhibiting high morbidity and mortality. The prognosis of HNSCC patients has remained poor, though considerable efforts have been made to improve the treatment of this cancer. Therefore, identifying significant differentially expressed genes (DEGs) involved in HNSCC progression and exploiting them as novel biomarkers or potential therapeutic targets for HNSCC is highly valuable.

Methods

Overlapping differentially expressed genes (DEGs) were screened out from three independent gene expression omnibus (GEO) datasets and subjected to GO and kyoto encyclopedia of genes and genomes pathway enrichment analyses. The protein–protein interactions network of DEGs was constructed in the STRING database, and the top ten hub genes were selected using cytoHubba. The relative expression of hub genes was detected in GEPIA, Oncomine, and human protein atlas (HPA) databases. Furthermore, the relationship of hub genes with the overall survival and disease‐free survival in HNSCC patients was investigated using the cancer genome atlas data.

Results

The top ten hub genes (SPP1, POSTN, COL1A2, FN1, IGFBP3, APP, MMP3, MMP13, CXCL8, and CXCL12) could be utilized as potential diagnostic indicators for HNSCC. The relative levels of FN1, APP, SPP1, and POSTN could be associated with the prognosis of HNSCC patients. The mRNA expression of APP and COL1A2 was validated in HNSCC samples.

Conclusion

This study identified effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting novel and essential therapeutic targets for HNSCC.

INTS8 accelerates the epithelial-to-mesenchymal transition in hepatocellular carcinoma by upregulating the TGF-β signaling pathway

Background

Hepatocellular carcinoma (HCC) is the third leading cause of death by malignancy worldwide. HCC has a poor prognosis due to tumor invasiveness and metastasis. There is substantial evidence that the epithelial-to-mesenchymal transition (EMT) plays a central role in cancer metastasis. In a previous study, a possible association between integrator complex 8 (INTS8) and the progression and development of HCC was discovered. However, its role and the molecular mechanisms in HCC are poorly understood.

Methods

The PROGgeneV2 platform database and Kaplan–Meier plotter analysis were used to analyze the potential effects of INTS8 in HCC. Moreover, we performed migration, transwell, and metastasis assays to investigate the effects of INTS8 on HCC cells. In addition, relevant signaling pathways were examined by western blot and RT-qPCR assays.

Results

We used the PROGgeneV2 platform database and Kaplan–Meier plotter analysis, which indicated that increased expression of INTS8 is associated with poor overall survival of HCC. Moreover, INTS8 expression was higher in HCC tissues than in adjacent noncancerous tissues. INTS8 depletion reduced the invasion and migration of HCC cell lines. Downregulation of INTS8 in vivo resulted in fewer observed metastatic nodules in lungs. Moreover, INTS8 knockdown also increased the expression of epithelial markers (E-cadherin) and decreased the expression of mesenchymal markers (N-cadherin and vimentin) following the downregulation of SMAD4. In addition, pretreatment with TGF-β1 could partly prevent the decrease in the expression of SMAD4 and EMT markers induced by INTS8 knockdown.

Conclusion

Overall, these findings suggest that INTS8 accelerates the EMT in HCC by upregulating the TGF-β signaling pathway.

Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.

METHODS

Integrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.

RESULTS

A total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.

CONCLUSION

This study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.

Integrative analysis of gene expression profiles reveals distinct molecular characteristics in oral tongue squamous cell carcinoma

Oral tongue squamous cell carcinoma (OTSCC) is the most common type of oral cancer. Despite advances in knowledge regarding the genome-scale gene expression pattern of oral cancer, the molecular portrait of OTSCC biology has remained unclear over the last few decades. Furthermore, studies concerning OTSCC gene-expression profiles are limited or inconsistent owing to tissue heterogeneity in single-cohort studies. Consequently, the present study integrated the profile datasets of three cohorts in order to screen for differentially expressed genes (DEGs), and subsequently identified the potential candidate genes and pathways in OTSCC through gene enrichment analysis and protein-protein interaction (PPI) network construction. Using the selected Gene Expression Omnibus datasets GSE13601, GSE31056 and GSE78060, 206 DEGs (125 upregulated and 81 downregulated) were identified in OTSCC, principally associated with extracellular matrix (ECM) organization and the phosphoinositide 3-kinase/protein kinase B signaling pathway. Furthermore, 146/206 DEGs were filtered into the PPI network and 20 hub genes were sorted. Further results indicated that the two most significant modules filtered from the PPI network were associated with ECM organization and human papillomavirus infection, which are important factors affecting OTSCC pathology. Overall, a set of OTSCC-associated DEGs has been identified, including certain key candidate genes that may be of vital importance for diagnosis, therapy and prevention of this disease.

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Background

Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis.

Methods

In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUAD. The Cancer Genome Atlas database was used to analyze the association of gene expression with survival result.

Results

Our results indicated that 80 genes were significantly dysregulated in LUAD according to four microarrays covering 356 cases of LUAD and 164 cases of normal lung tissues. Twenty genes were consistently and stably dysregulated by more than twofold. Ten of 20 genes had a relationship with overall survival or disease‐free survival in a cohort of 516 LUAD patients, and 19 genes were associated with tumor stage, gender, age, lymph node, or smoking. Low expression of AGER and high expression of CCNB1 were specifically associated with poor survival.

Conclusion

Our findings implicate AGER and CCNB1 might be potential biomarkers for diagnosis and prognosis targets for LUAD.

MicroRNA-31-5p regulates chemosensitivity by preventing the nuclear location of PARP1 in hepatocellular carcinoma

BACKGROUND

MicroRNAs (miRNAs) posttranscriptionally regulate gene expression and thereby contribute to the modulation of numerous complex and disease-relevant cellular processes, including cell proliferation, cell motility, apoptosis and stress response. miRNA-31-5p is encoded on a genomic fragile site, 9p21.3, which is reportedly lost in many hepatocellular carcinoma (HCC) tumors. Based on previous findings, we hypothesized that miR-31-5p alters chemosensitivity and that miR-31-5p mimics may influence sensitivity to chemotherapeutics in HCC as well as in a variety of other cancers.

METHODS

MiR-31-5p and PARP1 in HCC tissues were tested by RT-PCR and histological analysis, respectively. Next, clonogenic assay and western blot were used to detect miR-31-5p and PARP1 to modulate sensitivity to OXA-based chemotherapy. The distribution of OXA in the nuclear and intracellular was detected by ICP-MS. Coimmunoprecipitation was used to characterize the protein-protein interaction between PARP1 and ABCB9. A xenograft nude mouse model was used to examine the in vivo effects of miR-31-5p.

RESULTS

Reintroduction of miR-31-5p into miR-31-5p-null Hep3B cells significantly enhanced clonogenic resistance to oxaliplatin. Although miR-31-5p re-expression increased chemoresistance, it paradoxically increased the relative intracellular accumulation of oxaliplatin. This effect was coupled with a significantly decreased intranuclear concentration of oxaliplatin by ICP-MS. miR-31-5p prevents the nuclear location of PARP1 detected by immunofluorescence, histological analysis and Western blotting analysis. We subsequently identified an indirect miR-31-5p-mediated upregulation of ABCB9, which is a transporter associated with drug accumulation in lysosomes, along with an increased uptake of oxaliplatin to lysosomes; these phenomena were associated with a downregulation of PARP1, a bipotential transcriptional regulator with multiple miR-31-5p binding sites. However, the indirect overexpression of ABCB9 promoted cellular chemosensitivity, suggesting that miR-31-5p promotes chemoresistance largely via an ABCB9-independent mechanism.

CONCLUSIONS

Overall, our data suggest that the loss of miR-31-5p from HCC tumors promotes chemosensitivity, and this knowledge may be prognostically beneficial in the context of therapeutic sensitivity.

Microarray-based identification of genes associated with prognosis and drug resistance in ovarian cancer

The outcome for patients with ovarian cancer (OC) is poor because of drug resistance. Therefore, identification of factors that affect drug resistance and prognosis in OC is needed. In the present study, we identified 131 genes significantly dysregulated in 90 platinum-resistant OC tissues compared with 197 sensitive tissues, of which 30 were significantly associated with disease-free survival (DFS; n = 16), overall survival (OS; n = 6), or both (n = 8) in 489 OC patients of the The Cancer Genome Atlas cohort. Of these 30 genes, 17 were significantly upregulated and 13 were downregulated in the 90 resistant tissues, and with one exception, all of the up-/downregulated genes in resistant tissues were predictors of shorter DFS or/and OS. LAX1, MECOM, and PDIA4 were independent risk factors for DFS, and KLF1, SLC7A11, and PDIA4 for OS; combining these genes provided more accurate predictions for DFS and OS than any of the genes used individually. We further verified downregulation of PDIA4 protein in 51 specimens of patients with OC (24 drug resistant's and 27 sensitive's), which confirmed that downregulated PDIA4 predicted DFS and OS. PDIA4 also consistently predicted OS in a larger sample of 1656 patients with OC. These 30 genes, particularly the PDIA4, could be therapeutic targets or biomarkers for managing OC.

A pan-cancer study of copy number gain and up-regulation in human oncogenes

AIM

There has been limited research on CNVs in oncogenes and we conducted a systematic pan-cancer analysis of CNVs and their gene expression changes. The aim of the present study was to provide an insight into the relationships between gene expression and oncogenesis.

MAIN METHODS

We collected all the oncogenes from ONGene database and overlapped with CNVs TCGA tumour samples from Catalogue of Somatic Mutations in Cancer database. We further conducted an integrative analysis of CNV with gene expression using the data from the matched TCGA tumour samples.

KEY FINDINGS

From our analysis, we found 637 oncogenes associated with CNVs in 5900 tumour samples. There were 204 oncogenes with frequent copy number of gain (CNG). These 204 oncogenes were enriched in cancer-related pathways including the MAPK cascade and Ras GTPases signalling pathways. By using corresponding tumour samples data to perform integrative analyses of CNVs and gene expression changes, we identified 95 oncogenes with consistent CNG occurrence and up-regulation in the tumour samples, which may represent the recurrent driving force for oncogenesis. Surprisingly, eight oncogenes shown concordant CNG and gene up-regulation in at least 250 tumour samples: INTS8 (355), ECT2 (326), LSM1 (310), DDHD2 (298), COPS5 (286), EIF3E (281), TPD52 (258) and ERBB2 (254).

SIGNIFICANCE

As the first report about abundant CNGs on oncogene and concordant change of gene expression, our results may be valuable for the design of CNV-based cancer diagnostic strategy.

The Cancer Omics Atlas: an integrative resource for cancer omics annotations

Background

The Cancer Genome Atlas (TCGA) is an important data resource for cancer biologists and oncologists. However, a lack of bioinformatics expertise often hinders experimental cancer biologists and oncologists from exploring the TCGA resource. Although a number of tools have been developed for facilitating cancer researchers to utilize the TCGA data, these existing tools cannot fully satisfy the large community of experimental cancer biologists and oncologists without bioinformatics expertise.

Methods

We developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn) for fast and straightforward querying of TCGA “omics” data.

Results

TCOA provides the querying of gene expression, somatic mutations, microRNA (miRNA) expression, protein expression data based on a single molecule or cancer type. TCOA also provides the querying of expression correlation between gene pairs, miRNA pairs, gene and miRNA, and gene and protein. Moreover, TCOA provides the querying of the associations between gene, miRNA, or protein expression and survival prognosis in cancers. In addition, TCOA displays transcriptional profiles across various human cancer types based on the pan-cancer analysis. Finally, TCOA provides the querying of molecular profiles for 2877 immune-related genes in human cancers. These immune-related genes include those that are established or promising targets for cancer immunotherapy such as CTLA4, PD1, PD-L1, PD-L2, IDO1, LAG3, and TIGIT.

Conclusions

TCOA is a useful tool that supplies a number of unique and new functions complementary to the existing tools to facilitate exploration of the TCGA resource.

Identification of molecular target genes and key pathways in hepatocellular carcinoma by bioinformatics analysis

Background and aim

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and is increasing incidence worldwide. The aim of this study was to identify the key genes and microRNAs in HCC and explore their potential mechanisms.

Methods

The gene expression profiles of GSE76427, GSE64041, GSE57957, and the microRNA dataset GSE67882 were downloaded from the Gene Expression Omnibus database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs) and miRNAs (DEMs). The gene ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed for DEGs using the Database for Annotation, Visualization, and Integrated Discovery. A protein–protein interaction (PPI) network of the DEGs was constructed by Search Tool for the Retrieval of Interacting Genes and visualized by Cytoscape. Moreover, miRecords was used to predict the target genes of DEMs.

Results

In total, 106 DEGs were screened out in HCC, consisting of 89 upregulated genes and 17 downregulated genes, which were mainly enriched in biological processes associated with oxidation–reduction process. Besides, the Kyoto Encyclopedia of Genes and Genomes pathways including chemical carcinogenesis, drug metabolism-cytochrome P450, tryptophan metabolism, and retinol metabolism were involved. A PPI network was constructed consisting of 105 nodes and 66 edges. A significant module including nine hub genes, ASPM, AURKA, CCNB2, CDKN3, MELK, NCAPG, NUSAP1, PRC1, and TOP2A, was detected from the PPI network by Molecular Complex Detection. The enriched functions were mainly associated with the mitotic cell cycle process, cell division, and mitotic cell cycle. In addition, a total of 21 DEMs were identified, including 9 upregulated and 12 downregulated miRNAs. Interestingly, ZBTB41 was the potential target of seven miRNAs. Finally, the nine hub genes and three miRNA-target genes expression levels were validated by reverse transcription-polymerase chain reaction. The relative expression levels of nine genes (ASPM, AURKA, CDKN3, MELK, NCAPG, PRC1, TOP2A, ZBTB41, and ZNF148) were significantly upregulated in cancer tissues.

Conclusion

This study identified the key genes and potential molecular mechanisms underlying the development of HCC, which could provide new insight for HCC interventional strategies.

Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray

Osteosarcoma is a common and highly malignant tumour in children and teenagers that is characterized by drug resistance and high metastatic potential. Patients often develop pulmonary metastasis and have a low survival rate. However, the mechanistic basis for pulmonary metastasis remains unclear. To identify key gene and pathways associated with pulmonary metastasis of osteosarcoma, the authors downloaded the gene expression dataset GSE85537 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in primary tumours and lung metastases. Subsequently, the authors performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In total, 2,493 genes were identified as DEGs. Of these, 485 genes (19.45%) were upregulated, and the remaining 2,008 genes (80.55%) were downregulated. The authors identified the predominant GO categories and KEGG pathways that were significantly over-represented in the metastatic OS samples compared with the non-metastatic OS samples. A PPI network was constructed, and the results indicated that ALB, EGFR, INS, IL6, CDH1, FYN, ERBB2, IL8, CXCL12 and RAC2 were the top 10 core genes. The enrichment analyses of the genes involved in the top three significant modules demonstrated that the DEGs were principally related to neuroactive ligand-receptor interaction, the Rap1 signaling pathway, and protein digestion and absorption. Together, these data elucidated the molecular mechanisms of OS patients with pulmonary metastasis and provide potential therapeutic targets. However, further experimental studies are needed to confirm these results.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BACKGROUND

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

RESULTS

This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.

CONCLUSIONS

Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

Microarray analyses reveal genes related to progression and prognosis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma is a high morbidity and mortality cancer in China. Here are few biomarkers and therapeutic targets. Our study was aimed to identify candidate genes correlated to ESCC. Oncomine, The Cancer Genome Atlas, Gene Expression Omnibus were retrieved for eligible ESCC data. Deregulated genes were identified by meta-analysis and validated by an independent dataset. Survival analyses and bioinformatics analyses were used to explore potential mechanisms. Copy number variant analyses identified upstream mechanisms of candidate genes. In our study, top 200 up/down-regulated genes were identified across two microarrays. A total of 139 different expression genes were validated in GSE53625. Survival analysis found that nine genes were closely related to prognosis. Furthermore, Gene Ontology analyses and Kyoto Encyclopedia of Genes and Genomes analyses showed that different expression genes were mainly enriched in cell division, cell cycle and cell-cell adhesion pathways. Copy number variant analyses indicated that overexpression of ECT2 and other five genes were correlated with copy number amplification. The current study demonstrated that ECT2 and other eight candidate genes were correlated to progression and prognosis of esophageal squamous cell carcinoma, which might provide novel insights to the mechanisms.