Mining Cancer Transcriptomes: Bioinformatic Tools and the Remaining Challenges

E2F8 knockdown suppresses cell proliferation and induces cell cycle arrest via Wnt/β-Catenin pathway in ovarian cancer

Ovarian cancer is one of the leading causes of death in female reproductive system cancers. However, the pathogenesis of ovarian cancer remains elusive. Our aim is to investigate the potential targets for ovarian cancer. Two microarray datasets were obtained from the Gene Expression Omnibus public database. Using R package limma, the differentially expressed genes (DEGs) were identified from the datasets. There were 95 overlapping DEGs in two microarray datasets. GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis were carried out based on the DEGs. Wnt signaling pathway and cell cycle were enriched in the KEGG pathway analysis. Moreover, the top 10 hub genes with the most nodes were determined by PPI network analysis. E2F8, one of hub genes was positively linked to a bad outcome in ovarian cancer patients. Furthermore, E2F8 knockdown suppressed cell proliferation and induced cell cycle arrest in ovarian cancer. In addition, we found that silencing E2F8 inhibited the Wnt/β-catenin signaling pathway. In ovarian cancer cells with E2F8 knockdown, overexpressing β-catenin restored both the suppressed capacity of cell proliferation and cell cycle progression. Therefore, our results revealed that E2F8 had an involvement in the development of ovarian cancer which might act as a therapeutic target.

ANXA6/TRPV2 axis promotes lymphatic metastasis in head and neck squamous cell carcinoma by inducing autophagy

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is highly aggressive with a significant tropism of lymph nodes, which restricts treatment options and negatively impacts patient outcomes. Although progress has been made in understanding the molecular mechanisms underlying lymphatic metastasis (LM), these mechanisms remain elusive. ANXA6 is a scaffold protein that participates in tumor pathogenesis and autophagy regulation; however, how ANXA6 affects autophagy and LM in HNSCC cells remains unknown.

METHODS

RNA sequencing was performed on HNSCC clinical specimens with or without metastasis as well as on The Cancer Genome Atlas dataset to investigate ANXA6 expression and survival. Both in vitro and in vivo studies were performed to investigate the role of ANXA6 in the regulation of LM in HNSCC. The molecular mechanism by which ANXA6 interacts with TRPV2 was examined at the molecular level.

RESULTS

ANXA6 expression was significantly upregulated in HNSCC patients with LM and higher expression was associated with poor prognosis. ANXA6 overexpression promoted the proliferation and mobility of FaDu and SCC15 cells in vitro; however, ANXA6 knockdown retarded LM in HNSCC in vivo. ANXA6 induced autophagy by inhibiting the AKT/mTOR signaling pathway in HNSCC, thereby regulating the metastatic capability of the disease. Furthermore, ANXA6 expression positively correlated with TRPV2 expression both in vitro and in vivo. Lastly, TRPV2 inhibition reversed ANXA6-induced autophagy and LM.

CONCLUSIONS

These results indicate that the ANXA6/TRPV2 axis facilitates LM in HNSCC by stimulating autophagy. This study provides a theoretical basis for investigating the ANXA6/TRPV2 axis as a potential target for the treatment of HNSCC, as well as a biomarker for predicting LM.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Analysis of m6A modulator-mediated methylation modification patterns and the tumor microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer. In the development and progression of LUAD, epigenetic aberration plays a crucial role. However, the function of RNA N6-methyladenosine (m⁶A) modifications in the LUAD progression is unknown. The m6A regulator modification patterns in 955 LUAD samples were analyzed comprehensively. Patterns were systematically correlated with the tumor microenvironment (TME) cell-infiltration characteristics. Using principal component analysis algorithms, the m6Ascore was generated to quantify m⁶A modification patterns in individual tumors. Then, their values for predicting prognoses and therapeutic response in LUAD patients were assessed. Three distinct m⁶A modification patterns in LUAD were identified. Among them, the prognosis of m6Acluster C was the best, while the prognosis of m⁶Acluster A was the worst. Interestingly, the characterization of TME cell infiltration and biological behavior differed among the three patterns. To evaluate m⁶A modification patterns within individual tumors, an m⁶Ascore signature was constructed. The results showed that the high m⁶Ascore group was associated with a better prognosis; tumor somatic mutations and tumor microenvironment differed significantly between the high- and low- m⁶Ascore groups. Furthermore, in the cohort with anti-CTLA-4 treatment alone, patients with a high m⁶Ascore had higher ICI scores, which indicated significant therapeutic advantage and clinical benefits.

Identify Potential Urine Biomarkers for Bladder Cancer Prognosis Using NGS Data Analysis and Experimental Validation

Bladder cancer (BC) is one of the most often reported malignancies globally, with a high recurrence rate and associated morbidity and mortality, especially in advanced BC. There has been a surge in the number of molecular targets revealed for BC prognosis and treatment. However, there is still a great need to discover novel biomarkers. Consequently, the current study investigated biomarkers that might indicate the progression of bladder cancer. In this study, bioinformatics analysis was done on a single GEO dataset, and TCGA-BLCA information was connected with differentially expressed genes (DEGs). The levels of mRNA and protein expression were validated using qRT-PCR. According to our findings, CRYAB, ECM1, ALDOB, AOC, GPX3, IGFBP7, AQP2, LASS2, TMEM176A, GALNT1, and LASS2 were highly enriched in cell division, identical protein binding, and developmental process in bladder cancer patients. In addition, among the highly differentiated genes, ECM1, GALNT1, LASS2, and GPX3 showed significant molecular alterations in BC, which are crucial for marker identification. Moreover, the mRNA, CNVs, and protein levels of ECM1, GALNT1, LASS2, and GPX3 were significantly increased in BC patients. Our predictions and analysis studies stated that these four genes act as urine biomarkers and played a crucial role in disease prognosis and the therapeutic process of bladder cancer. Our outcomes showed that these four novel urine biomarkers have the potential to provide innovative diagnostics, early predictions, and disease targets, ultimately improving the BC patient's prognosis.

Identification of potential hub genes of gastric cancer

BACKGROUND

Gastric cancer (GC) is a malignant tumor originated from gastric mucosa epithelium. It is the third leading cause of cancer mortality in China. The early symptoms are not obvious. When it is discovered, it has developed to the advanced stage, and the prognosis is poor. In order to screen for potential genes for GC development, this study obtained GSE118916 and GSE109476 from the gene expression omnibus (GEO) database for bioinformatics analysis.

METHODS

First, GEO2R was used to identify differentially expressed genes (DEG) and the functional annotation of DEGs was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The Search Tool for the Retrieval of Interacting Genes (STRING) tool was used to construct protein-protein interaction (PPI) network and the most important modules and hub genes were mined. Real time quantitative polymerase chain reaction assay was performed to verify the expression level of hub genes.

RESULTS

A total of 139 DEGs were identified. The functional changes of DEGs are mainly concentrated in the cytoskeleton, extracellular matrix and collagen synthesis. Eleven genes were identified as core genes. Bioinformatics analysis shows that the core genes are mainly enriched in many processes related to cell adhesion and collagen.

CONCLUSION

In summary, the DEGs and hub genes found in this study may be potential diagnostic and therapeutic targets.

Identification of new biomarkers in immune microenvironment of testicular germ cell tumour

To seek novel prognostic biomarkers for testicular germ cell tumour (TGCT) and investigate the tumour immune microenvironment, we identified critical differentially expressed genes (DEGs) by overlapping GSE1818 dataset from Gene Expression Omnibus (GEO). Protein-protein interaction (PPI) network was used to investigate key modules and hub genes. Functional enrichment analysis was performed to investigate the underlying molecular functions of the DEGs in TGCT development and progression. The following survival analysis based on The Cancer Genome Atlas (TCGA) TGCT dataset indicated that AKAP4, SPA17 and TNP1 are correlated with TGCT prognosis. Immunohistochemistry and quantitative real-time polymerase chain reaction verified the down-regulation of the 3 hub genes in TGCT. Gene set enrichment analysis was conducted to further explore the role of the 3 hub genes in TGCT respectively. In addition, TGCT samples had high infiltration of CD8+ T cells, M0 and M1 macrophage cells, and resting myeloid dendritic cells in immune microenvironment. We also constructed the microRNA-gene regulatory networks to identify the key upstream microRNAs in TGCT. In conclusion, our findings indicated that AKAP4, SPA17 and TNP1 are promising biomarkers of TGCT. AKAP4 and TNP1 might regulate immune cells infiltration in immune microenvironment.

Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer

Objective: To develop and validate a prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NCT) of triple-negative breast cancer (TNBC). Methods: We systematically searched Gene Expression Omnibus, ArrayExpress, and PubMed for the gene expression profiles of operable TNBC accessible to NCT. Molecular heterogeneity was detected with hierarchical clustering method, and the biological profiles of differentially expressed genes were investigated by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses, and Gene Set Enrichment Analysis (GSEA). Next, machine-learning algorithms including random-forest analysis and least absolute shrinkage and selection operator (LASSO) analysis were synchronously performed and, then, the intersected proportion of significant genes was undergone binary logistic regression to fulfill variables selection. The predictive response score (pRS) system was built as the product of the gene expression and coefficient obtained from the logistic analysis. Last, the cohorts were randomly divided in a 7:3 ratio into training cohort and validation cohort for the introduction of a robust model, and a nomogram was constructed with the independent predictors for pCR rate. Results: A total of 217 individuals from four cohort datasets (GSE32646, GSE25065, GSE25055, GSE21974) with complete clinicopathological information were included. Based on the microarray data, a six-gene panel (ATP4B, FBXO22, FCN2, RRP8, SMERK2, TET3) was identified. A robust nomogram, adopting pRS and clinical tumor size stage, was established and the performance was successively validated by calibration curves and receiver operating characteristic curves with the area under curve 0.704 and 0.756, respectively. Results of GSEA revealed that the biological processes including apoptosis, hypoxia, mTORC1 signaling and myogenesis, and oncogenic features of EGFR and RAF were in proactivity to attribute to an inferior response. Conclusions: This study provided a robust prediction model for pCR rate and revealed potential mechanisms of distinct response to NCT in TNBC, which were promising and warranted to further validate in the perspective.

Role of COL3A1 and POSTN on Pathologic Stages of Esophageal Cancer

BACKGROUND

Esophageal cancer (EC) is a primary malignant tumor originating from the esophageal of the epithelium. Surgical resection is a potential treatment for EC, but this is only appropriate for patients who have locally resectable lesions suitable for surgery. However, most patients with EC are at a late stage when diagnosed. Therefore, there is an urgent need to further explore the pathogenesis of EC to enable early diagnosis and treatment.

METHODS

Our study downloaded 2 expression spectrum datasets (GSE92396 and GSE100942) in the Gene Expression Omnibus (GEO) database. GEO2 R was used to identify the Differentially expressed genes (DEGs) between the samples of EC and control. Using the DAVID tool to make the Functional enrichment analysis. Constructing A protein-protein interaction (PPI) network. Identifying the Hub genes. The impact of hub gene expression on overall survival and their expression based on immunohistochemistry were analyzed. Associated microRNAs were also predicted.

RESULTS

There were 36 common DEGs identified. The analysis of GO and KEGG results shown that the variations were predominantly concentrated in the extracellular matrix (ECM), ECM organization, DNA binding, platelet activation, and ECM-receptor interactions. COL3A1 and POSTN had high expression in EC tissues which was compared with their expression in healthy tissues. Analysis of pathologic stages showed that when COL3A1 and POSTN were highly expressed, the stage of the pathologic of EC patients was relatively high (P < 0.005).

CONCLUSIONS

COL3A1 and POSTN may play an important role in the advancement and occurrence of EC. These genes could provide some novel ideas and basis for the diagnosis and targeted treatment of EC.

A novel role of Krüppel-like factor 8 as an apoptosis repressor in hepatocellular carcinoma

Background

Krüppel-like factor 8 (KLF8), a cancer-promoting factor that regulates critical gene transcription and cellular cancer-related events, has been implicated in tumor development and progression. However, the functional role of KLF8 in the pathogenesis of hepatocellular carcinoma (HCC) remains largely unknown.

Methods

The gene expression patterns and genome-wide regulatory profiles of HCC cells after KLF8 knockout were analyzed by using RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) of histone H3 lysine 27 acetylation (H3K27ac) combined with bioinformatics analysis. Transcription factor-binding motifs that recognized by KLF8 were evaluated by motif analysis. For the predicted target genes, transcriptional changes were examined by ChIP, and loss of function experiments were conducted by siRNA transfection.

Results

KLF8 functioned as a transcription repressor in HCC and mainly regulated apoptotic-related genes directly. A total of 1,816 differentially expressed genes after KLF8 knockout were identified and significantly corresponded to global changes in H3K27ac status. Furthermore, two predicted target genes, high-mobility group AT-hook 2 (HMGA2) and matrix metalloproteinase 7 (MMP7), were identified as important participants in KLF8-mediated anti-apoptotic effect in HCC. Knockout of KLF8 enhanced cell apoptosis process and caused increase in the associated H3K27ac, whereas suppression HMGA2 or MMP7 attenuated these biological effects.

Conclusions

Our work suggests a novel role and mechanism for KLF8 in the regulation of cell apoptosis in HCC and facilitates the discovery of potential therapeutic targets for HCC treatment.

Gene Expression Profiles Identified Novel Urine Biomarkers for Diagnosis and Prognosis of High-Grade Bladder Urothelial Carcinoma

Bladder urothelial carcinoma (BC) has been identified as one of the most common malignant neoplasm worldwide. High-grade bladder urothelial carcinoma (HGBC) is aggressive with a high risk of recurrence, progression, metastasis, and poor prognosis. Therefore, HGBC clinical management is still a challenge. We performed the present study to seek new urine biomarkers for HGBC and investigate how they promote HGBC progression and thus affect the prognosis based on large-scale sequencing data. We identified the overlapped differentially expressed genes (DEGs) by combining GSE68020 and The Cancer Genome Atlas (TCGA) datasets. Subsequent receiver operating characteristic (ROC) curves, Kaplan-Meier (KM) curves, and Cox regression were conducted to test the diagnostic and prognostic role of the hub genes. Chi-square test and logistic regression were carried out to analyze the associations between clinicopathologic characteristics and the hub genes. Ultimately, we performed gene set enrichment analysis (GSEA), protein-protein interaction (PPI) networks, and Bayesian networks (BNs) to explore the underlying mechanisms by which ECM1, CRYAB, CGNL1, and GPX3 are involved in tumor progression. Immunohistochemistry based on The Human Protein Atlas and quantitative real-time polymerase chain reaction based on urine samples confirmed the downregulation and diagnostic values of the hub genes in HGBC. In conclusion, our study indicated that CRYAB, CGNL1, ECM1, and GPX3 are potential urine biomarkers of HGBC. These four novel urine biomarkers will have attractive applications to provide new diagnostic methods, prognostic predictors and treatment targets for HGBC, which could improve the prognosis of HGBC patients, if validated by further experiments and larger prospective clinical trials.