Multi-omics data analysis reveals the biological implications of alternative splicing events in lung adenocarcinoma.

Cancer is characterized by the dysregulation of alternative splicing (AS). However, the comprehensive regulatory mechanisms of AS in lung adenocarcinoma (LUAD) are poorly understood. Here, we displayed the AS landscape in LUAD based on the integrated analyses of LUAD's multi-omics data. We identified 13,995 AS events in 6309 genes as differentially expressed alternative splicing events (DEASEs) mainly covering protein-coding genes. These DEASEs were strongly linked to "cancer hallmarks", such as apoptosis, DNA repair, cell cycle, cell proliferation, angiogenesis, immune response, generation of precursor metabolites and energy, p53 signaling pathway and PI3K-AKT signaling pathway. We further built a regulatory network connecting splicing factors (SFs) and DEASEs. In addition, RNA-binding protein (RBP) mutations that can affect DEASEs were investigated to find some potential cancer drivers. Further association analysis demonstrated that DNA methylation levels were highly correlated with DEASEs. In summary, our results can bring new insight into understanding the mechanism of AS and provide novel biomarkers for personalized medicine of LUAD.

GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression.

The single-cell pseudotemporal trajectory inference is an important way to explore the process of developmental changes within a cell. Due to the uneven rate of cell growth, changes in gene expression depend less on the time of data collection and more on a cell's "internal clock". To overcome the challenges of gene analysis, and replicate biological developmental processes, several strategies have been put forth. However, due to the size of single-cell datasets, locating relevant signposts usually necessitate clustering analysis or a sizable amount of priori information. To this end, we propose a novel single-cell pseudotemporal trajectory inference technique: GCSTI method, which is based on graph compression and doesn't rely on a priori knowledge or clustering procedures, can handle the trajectory inference problem for a large network in a stable and efficient manner. Additionally, we simultaneously improve the pseudotime defining method currently employed in this study in order to obtain more trustworthy and beneficial outcomes for trajectory inference. Finally, we validate the efficacy and stability of the GCSTI method using datasets from human skeletal muscle myogenic cells and four simulated datasets.

Nonsense-Mediated Decay Targeted RNA (ntRNA): Proposal of a ntRNA-miRNA-lncRNA Triple Regulatory Network Usable as Biomarker of Prognostic Risk in Patients with Kidney Cancer.

The most prevalent subtype of renal cell carcinoma (RCC), kidney renal clear cell carcinoma (KIRC) may be associated with a poor prognosis in a high number of cases, with a stage-specific prognostic stratification currently in use. No reliable biomarkers have been utilized so far in clinical practice despite the efforts in biomarker research in the last years. Nonsense-mediated mRNA decay (NMD) is a critical safeguard against erroneous transcripts, particularly mRNA transcripts containing premature termination codons (called nonsense-mediated decay targeted RNA, ntRNA). In this study, we first characterized 296 differentially expressed ntRNAs that were independent of the corresponding gene, 261 differentially expressed miRNAs, and 4653 differentially expressed lncRNAs. Then, we constructed a hub ntRNA-miRNA-lncRNA triple regulatory network associated with the prognosis of KIRC. Moreover, the results of immune infiltration analysis indicated that this network may influence the changes of the tumor immune microenvironment. A prognostic model derived from the genes and immune cells associated with the network was developed to distinguish between high- and low-risk patients, which was a better prognostic than other models, constructed using different biomarkers. Additionally, correlation of methylation and ntRNAs in the network suggested that some ntRNAs were regulated by methylation, which is helpful to further study the causes of abnormal expression of ntRNAs. In conclusion, this study highlighted the possible clinical implications of ntRNA functions in KIRC, proposing potential significant biomarkers that could be utilized to define the prognosis and design personalized treatment plans in kidney cancer management in the next future.

Gene-microRNA Network Analysis Identified Seven Hub Genes in Association with Progression and Prognosis in Non-Small Cell Lung Cancer.

INTRODUCTION: Lung cancer is the leading cause of cancer deaths in the world and is usually divided into non-small cell lung cancer (NSCLC) and small cell lung cancer. NSCLC is dominant and accounts for 85% of the total cases. Currently, the therapeutic method of NSCLC is not so satisfactory, and thus identification of new biomarkers is critical for new clinical therapy for this disease. METHODS: Datasets of miRNA and gene expression were obtained from the NCBI database. The differentially expressed genes (DEGs) and miRNAs (DEMs) were analyzed by GEO2R tools. The DEG-DEM interaction was built via miRNA-targeted genes by miRWalk. Several hub genes were selected via network topological analysis in Cytoscape. RESULTS: A set of 276 genes were found to be significantly differentially expressed in the three datasets. Functional enrichment by the DAVID tool showed that these 276 DEGs were significantly enriched in the term "cancer", with a statistic p-value of 1.9 × 10-5. The subdivision analysis of the specific cancer types indicated that "lung cancer" occupies the largest category with a p-value of 2 × 10-3. Furthermore, 75 miRNAs were shown to be differentially expressed in three representative datasets. A group of 13 DEGs was selected by analysis of the miRNA-gene interaction of these DEGs and DEMs. The investigation of these 13 genes by GEPIA tools showed that eight of them had consistent results with NSCLC samples in the TCGA database. In addition, we applied the KMplot to conduct the survival analysis of these eight genes and found that seven of them have a significant effect on the prognosis survival of patients. We believe that this study could provide effective research clues for the prevention and treatment of non-small cell lung cancer.

TF-RBP-AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers.

Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF-RBP-AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA-KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF-RBP-AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.

The association study between CYP24A1 gene polymorphisms and risk of liver, lung and gastric cancer in a Chinese population.

Recently, four single nucleotide polymorphisms (rs2585428, rs4809960, rs6022999 and rs6068816) in CYP24A1 gene were extensively studied for their associations with cancer risk. However, these studies included only a few types of cancer, which calls for further investigations. In view of this, we here conducted a case-control study to explore the associations between these four CYP24A1 gene polymorphisms and risk of liver, lung and gastric cancer in a Chinese population. A total of 480 liver cancer patients, 550 lung cancer patients, 460 gastric cancer patients and 800 normal controls were recruited in this study. The genotyping of CYP24A1 gene polymorphisms was applied with Sanger sequencing assay. Single-locus analysis demonstrated that rs6022999 was significantly associated with risk of liver and lung cancer, while rs6068816 was significantly associated with the risk of gastric cancer. Haplotype analysis revealed that haplotype GTAT was associated with an increased risk of liver cancer and a decreased risk of lung cancer, and haplotype ATGC was associated with a decreased risk of lung cancer. The further meta-analysis of rs6068816 and lung cancer risk showed that rs6068816 was not associated with lung cancer risk in Chinese population, which confirmed our present finding. Conclusively, rs6022999 may be a genetic biomarker for liver and lung cancer susceptibility in Chinese population, and rs6068816 may be used to predict gastric cancer risk in Chinese population.

Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis.

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

Network-based identification of biomarkers for colon adenocarcinoma.

BACKGROUND: As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges. METHODS: We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively. RESULTS: Classification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD. CONCLUSIONS: In conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD.

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self-organizing map (SOM) neural network, and hierarchical clustering methods to classify LUAD into four subtypes. We determined that 24 differentially expressed genes could be used as therapeutic targets, and five genes (i.e., RTKN2, ADAM6, SPINK1, COL3A1, and COL1A2) could be potential novel markers for LUAD. Multivariate analysis showed that the four subtypes could serve as prognostic subtypes. Representative genes of each subtype were also identified, which could be potentially targetable markers for the different subtypes. The function and pathway enrichment analyses of these representative genes showed that the four subtypes have different pathological mechanisms. Mutations associated with the subtypes, e.g., epidermal growth factor receptor (EGFR) mutations in subtype 4 and tumor protein p53 (TP53) mutations in subtypes 1 and 2, could serve as potential markers for drug development. The four subtypes provide a foundation for subtype-specific therapy of LUAD.

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.

Investigation of gene evolution in vertebrate genome reveals novel insights into spine study.

Vertebrate genomes have been considered to have undergone a complicated evolution during their early period and to have generated a large number of genetic templates with novel functions, such as an extended spinal cord and a dorsal central nervous system. However, consistent gene evolution in vertebrate genomes has not been fully elucidated. In this study, we have systematically investigated the gene evolution in vertebrates utilizing a series of comparative genomics tools. We determined that three critical genes were consistently lost in vertebrate genomes, and 14 genes initially emerged in vertebrate formation. Furthermore, another 29 genes were identified with consistent amino acid variation between the vertebrates and invertebrates. A function analysis of five genes (TEP3, ABLIM2, ABLIM3, GAD1 and GAD2) was performed, and their evolution mechanisms in vertebrate genomes further investigated. These findings provide novel insights for studying the vertebrate evolution and spine development.

Comprehensive molecular profiling of the B7 family in gastrointestinal cancer.

OBJECTIVES: B7 family has been identified as co-stimulatory or co-inhibitory molecules on T-cell response and plays an important role in tumour mortality and malignancy. In this study, the expression pattern of B7 family in gastrointestinal (GI) cancer was examined. Its upstream regulating mechanism, downstream targets and association with clinical parameters were also studied. MATERIALS AND METHODS: The expression level of B7 members was analysed by FIREHOUSE. The gene mutation, DNA methylation, association with clinical parameters and downstream network of B7 members were analysed in cBioportal. The mutation frequency was analysed by Catalogue of Somatic Mutations in Cancer (COSMIC) analysis. The phylogenetic tree was constructed in MEGA7. The interaction protein domain analysis was performed by Pfam 31.0. RESULTS: Differential expression of B7 family molecules was detected in different kinds of GI cancer. High-frequency gene alteration was found in tumour samples. There was negative correlation of promoter methylation and mRNA expression of B7 family members in tumour samples, suggesting the epigenetic basis of B7 family gene deregulation in GI cancer. The overexpression of B7-H1 in pancreatic cancer, B7-H5 in oesophageal cancer and B7-H6 in liver cancer were significantly associated with worse overall survival. Finally, by network analysis, we identified some potential interacting proteins for B7-1/2 and B7-H1/DC. CONCLUSIONS: Overall, our study suggested that B7 member deregulation was strongly involved in GI cancer tumorigenesis.

A systems biology approach to identify the signalling network regulated by Rho-GDI-γ during neural stem cell differentiation.

Understanding the molecular mechanism that underlies the differentiation of neural stem cells (NSCs) is vital to develop regenerative medicines for neurological disorders. In our previous work, Rho-GDI-γ was found to be able to prompt neuronal differentiation when it was down regulated. However, it is unclear how Rho-GDI-γ regulates this differentiation process. Therefore, a novel systems biology approach is presented here to identify putative signalling pathways regulated by Rho-GDI-γ during NSC differentiation, and these pathways can provide insights into the NSC differentiation mechanisms. In particular, our proposed approach combines the predictive power of computational biology and molecular experiments. With different biological experiments, the genes in the computationally identified signalling network were validated to be indeed regulated by Rho-GDI-γ during the differentiation of NSCs. In particular, one randomly selected pathway involving Vcp, Mapk8, Ywhae and Ywhah was experimentally verified to be regulated by Rho-GDI-γ. These promising results demonstrate the effectiveness of our proposed systems biology approach, indicating the potential predictive power of integrating computational and experimental approaches.

CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method.

BACKGROUND: Signal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways. RESULTS: We propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results. CONCLUSIONS: CASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/.