Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues

Oncogenic zinc finger protein ZNF322A promotes stem cell-like properties in lung cancer through transcriptional suppression of c-Myc expression

ZNF322A, a C2H2 zinc finger transcription factor, is an oncoprotein in lung cancer. However, the transcription mechanisms of ZNF322A in lung cancer stem cell-like reprogramming remain elusive. By integrating our chromatin immunoprecipitation-sequencing and RNA-sequencing datasets, we identified and validated the transcriptional targets of ZNF322A, which were significantly enriched in tumorigenic functions and developmental processes. Indeed, overexpression of ZNF322A promoted self-renewal ability and increased stemness-related gene expressions in vitro and in vivo. Importantly, ZNF322A bound directly to c-Myc promoter and recruited histone deacetylase 3 to transcriptionally suppress c-Myc expression, which in turn increased mitochondrial oxidative phosphorylation and promoted cell motility, thus maintaining stem cell-like properties of lung cancer. Clinically, ZNF322AHigh/c-MycLow expression profile was revealed as an independent indicator of poor prognosis in lung cancer patients. Our study provides the first evidence that ZNF322A-centered transcriptome promotes lung tumorigenesis and ZNF322A acts as a transcription suppressor of c-Myc to maintain lung cancer stem cell-like properties by shifting metabolism towards oxidative phosphorylation.

Expression of CENPE and its Prognostic Role in Non-small Cell Lung Cancer

Background

Non-small cell lung cancer (NSCLC) is one of the most important causes of death worldwide. Most patients are diagnosed in the advanced stage and have a poor prognosis. This study was to investigate the expression and significance of CENPE in NSCLC.

Method

Collecting information about CENPE in the Oncoming database, and perform a further analysis of the data in the current database to conduct a meta-analysis for its functional role in NSCLC. Patient life cycle analysis using Kaplan-Meier Plotter and GEPIA databases are used to perform patient survival analysis.

Result

A total of 12 studies involved the expression of CENPE in NSCLC cancer tissues and normal tissues, including 1195 samples. CENPE was highly expressed in NSCLC cell carcinoma compared with the control group (P < 0.05). Moreover, the expression of CENPE was correlated with the overall survival rate of CENPE. The overall survival rate of patients with high expression of CENPE was poor, and the prognosis of patients with low expression of CENPE was better (P<0.05).

Conclusion

We propose high expression of CENPE in NSLCL tissue is related to the prognosis of NSCLC, which may provide important basis for the development of tumor drugs.

Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.

Disease association of human tumor suppressor genes

The multifactorial disease, cancer, frequently emerges due to perturbations in tumor suppressor genes (TSGs). However, a growing number of noncanonical target genes of TSGs and the highly interconnected nature of the human interactome reveal that the functions of TSGs are not limited to cancer-specific events. The various functions of TSGs lead to the assumption that cancer is linked with other human disorders. Therefore, a disease-gene association network of TSGs (TSDN) was constructed by integrating protein-protein interaction networks of TSGs (TSN) with Morbid Map in Online Mendelian Inheritance in Man. The TSDN revealed links between TSGs and 22 different human disorders including cancer and indicated disease-disease associations. In addition, high-density functional protein clusters in the TSN showed cohesive and overlapping disease-TSG associations, which proved the prevalent role of TSGs in various human diseases beyond cancer. The presence of overlapping disease-gene modules and disease-disease associations via the TSN demonstrated that other diseases can serve as possible roots of the life-threatening disease cancer. Therefore, a disease association map of TSGs could be a promising tool for exploring intricate relationships between cancer and other diseases for the early prediction of cancer and the understanding of disease etiology.

MiRNA-195-5p Functions as a Tumor Suppressor and a Predictive of Poor Prognosis in Non-small Cell Lung Cancer by Directly Targeting CIAPIN1

Accumulating evidence suggests that microRNAs (miRNAs) has been proven to be a critical regulator in the tumor progression, of which miR-195-5p was reported to function as tumor suppressor in prostate cancer and oral squamous cell carcinoma. However, studies on the clinical significance and biological function of miR-195-5p in non-small cell lung cancer (NSCLC) were still unavailable. Here, we reported that the expression of miR-195-5p was decreased in NSCLC tissues and cell lines. Downregulation of miR-195-5p was significantly associated with TNM stage, tumor size and lymph node metastasis. The Kaplan-Meier survival analysis demonstrated that the survival time of NSCLC patients with high expression of miR-195-5p was longer than those with low expression during the 5-year follow up period (p = 0.0410). COX regression analysis indicated that miR-195-5p expression was an independent prognostic indicator for the survival of NSCLC patients (HR = 2.45, 95% CI: 1.53–4.63; p = 0.007). Results of functional analyses revealed that overexpression of miR-195-5p in A549 cells inhibited cell proliferation, induced cell cycle G0/G1 phase arrest and apoptosis using MTT and flow cytometry analysis. Furthermore, bioinformatics and luciferase reporter assays demonstrated that cytokine-induced apoptosis inhibitor 1 (CIAPIN1), an anti-apoptotic molecule was a direct target of miR-195-5p in NSCLC cells. Meta-analysis based on Oncomine database showed CIAPIN1 was significantly up-regulated in human lung cancer tissues. Consistently, knockdown of CIAPIN1 phenocopied the inhibitory effects of miR-195-5p overexpression in NSCLC cell function. These findings suggest that miR-195-5p could be used as a potential prognostic predictor and tumor suppressor in NSCLC.

Inferring Genome-Wide Interaction Networks Using the Phi-Mixing Coefficient, and Applications to Lung and Breast Cancer (Invited Paper)

Constructing gene interaction networks (GINs) from high-throughput gene expression data is an important and challenging problem in systems biology. Existing algorithms produce networks that either have undirected and unweighted edges, or else are constrained to contain no cycles, both of which are biologically unrealistic. In the present paper we propose a new algorithm, based on a concept from probability theory known as the ϕ-mixing coefficient, that produces networks whose edges are weighted and directed, and are permitted to contain cycles. Specifically, we inferred networks for two subtypes of lung cancer small cell (SCLC) and non-small cell (NSCLC) as well as normal lung tissue. Then we compared with the outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and found that the higher the degree of a gene in the inferred network, the more essential it is to the survival of a cell. We also analyzed data from a ChIP-Seq experiment to determine putative downstream targets of ASCL1. The SCLC network was enriched for ChIP-seq neighbors of this oncogenic transcription factor, but not in the NSCLC network. We also reverse-engineered whole-genome interaction networks for two distinct subtypes of breast cancer, namely Luminal-A and Basal (also known as triple negative).

Glucose Transporter 1 Gene Variants Predict the Prognosis of Patients with Early-Stage Non-small Cell Lung Cancer

BACKGROUND

This study was conducted to investigate whether polymorphisms of glucose transporter 1 (GLUT1) gene are associated with the prognosis of patients with non-small cell lung cancer (NSCLC) after surgical resection.

METHODS

Five single nucleotide polymorphisms (SNPs) in GLUT1 were investigated in a total of 354 patients with NSCLC who underwent curative surgery. The association of the SNPs with patients' survival was analyzed.

RESULTS

Among the five SNPs investigated, two SNPs (GLUT1 rs3820589T > A and rs4658G > C) were significantly associated with OS in multivariate analyses. GLUT1 rs3820589T > A was associated with significantly better OS (adjusted hazard ratio [aHR] = 0.57, 95% confidence interval [CI] = 0.34-0.94, P = 0.03, under dominant model), and rs4658G > C was associated with significantly worse OS (aHR = 1.91, 95% CI = 1.09-3.33, P = 0.02, under recessive model). In the stratified analysis by tumor histology, the effect of these SNPs on OS was only significant in squamous cell carcinoma but not in adenocarcinoma. When the two SNPs were combined, OS decreased as the number of bad genotypes increased (Ptrend = 4 × 10^-3).

CONCLUSIONS

This study suggests that genetic variation in GLUT1 may be useful in predicting survival of patients with early stage NSCLC.

Bayesian inference of hub nodes across multiple networks

Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive interpretation of the resulting networks and clearer guidance on potential targets for treatment, but also improves power for identifying the edges of highly connected nodes. Through simulations, we demonstrate the utility of our method and compare its performance to current popular methods that do not borrow information regarding hub nodes across networks. We illustrate the applicability of our method to inference of co-expression networks from The Cancer Genome Atlas ovarian carcinoma dataset.

[Expression of KIF23 and Its Prognostic Role in Non-small Cell Lung Cancer: Analysis Based on the Data-mining of Oncomine]

背景与目的

非小细胞肺癌（non-small cell lung cancer, NSCLC）是全球引起死亡的最重要原因之一。大多数患者发现时处于中晚期，预后较差。本研究拟探讨驱动蛋白超家族23（kinesin family member 23, KIF23）在NSCLC中的表达及意义。

方法

收集Oncomine数据库中关于KIF23的信息，并对目前数据库中资料进行二次分析，对其在NSCLC中的作用进行荟萃分析。利用Kaplan-Meier Plotter进行患者生存周期分析。

结果

Oncomine数据库中共收集了447项不同类型的研究结果，其中关于KIF23表达有统计学差异的研究结果有67个，KIF23表达增高的研究有64项、表达降低的研究有3项。共有16项研究涉及KIF23在NSCLC癌组织和正常组织中的表达，共包括1, 189个样本，与对照组相比，KIF23在NSCLC细胞癌中高表达（P＜0.05）。不仅如此，KIF23表达量与NSCLC总体生存率存在相关性，高表达KIF23的患者总体生存率较差，低表达KIF23的患者预后较好（P＜0.05）。进一步亚组分析发现，KIF23表达水平对肺腺癌患者预后有显著影响，而在鳞癌患者中，其表达水平对预后无显著影响。

结论

我们通过对Oncomine基因芯片数据库中肿瘤相关基因信息的深入挖掘，提出KIF23在NSLCL组织中高表达，且与NSCLC预后有关，可能为肿瘤药物的开发提供重要理论依据。

Evaluation of the HOXA11 level in patients with lung squamous cancer and insights into potential molecular pathways via bioinformatics analysis

Background

This study was carried out to discover the underlying role that HOXA11 plays in lung squamous cancer (LUSC) and uncover the potential corresponding molecular mechanisms and functions of HOXA11-related genes.

Methods

Twenty-three clinical paired LUSC and non-LUSC samples were utilized to examine the level of HOXA11 using quantitative real-time polymerase chain reaction (qRT-PCR). The clinical significance of HOXA11 was systematically analyzed based on 475 LUSC and 18 non-cancerous adjacent tissues from The Cancer Genome Atlas (TCGA) database. A total of 102 LUSC tissues and 121 non-cancerous tissues were available from Oncomine to explore the expressing profiles of HOXA11 in LUSC. A meta-analysis was carried out to further assess the differential expression of HOXA11 in LUSC, including in-house qRT-PCR data, expressing data extracted from TCGA and Oncomine databases. Moreover, the enrichment analysis and potential pathway annotations of HOXA11 in LUSC were accomplished via Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of hub genes and according correlations with HOXA11 were assessed to further explore the biological role of HOXA11 in LUSC.

Results

HOXA11 expression in LUSC had a tendency to be upregulated in comparison to adjacent non-cancerous tissues by qRT-PCR. TCGA data displayed that HOXA11 was remarkably over-expressed in LUSC compared with that in non-LUSC samples, and the area under curves (AUC) was 0.955 (P < 0.001). A total of 1523 co-expressed genes were sifted for further analysis. The most significant term enriched in the KEGG pathway was focal adhesion. Among the six hub genes of HOXA11, including PARVA, ILK, COL4A1, COL4A2, ITGB1, and ITGA5, five (with the exception of COL4A1) were significantly decreased compared with the normal lung tissues. Moreover, the expression of ILK was negatively related to HOXA11 (r = − 0.141, P = 0.002).

Conclusion

High HOXA11 expression may lead to carcinogenesis and the development of LUSC. Furthermore, co-expressed genes might affect the prognosis of LUSC.

Inhibition of glutamate oxaloacetate transaminase 1 in cancer cell lines results in altered metabolism with increased dependency of glucose

BACKGROUND

Glutamate oxaloacetate transaminase 1 (GOT1) regulates cellular metabolism through coordinating the utilization of carbohydrates and amino acids to meet nutrient requirements. KRAS mutated cancer cells were recently shown to rely on GOT1 to support long-term cell proliferation. The aim of the present study was to address the role of GOT1 in the metabolic adaption of cancer cells.

METHODS

GOT1-null and knockdown cell lines were established through CRISPR/Cas9 and shRNA techniques. The growth properties, colony formation ability, autophagy and selected gene expression profiles were analysed. Glucose deprivation decreased the viability of the GOT1-null cells and rescue experiments were conducted with selected intermediates. The redox NADH/NAD⁺ homeostasis as well as lactate secretion were determined. GOT1 expression levels and correlation with survival rates were analysed in selected tumor databases.

RESULTS

Inhibition of GOT1 sensitized the cancer cells to glucose deprivation, which was partially counteracted by oxaloacetate and phosphoenol pyruvate, metabolic intermediates downstream of GOT1. Moreover, GOT1-null cells accumulated NADH and displayed a decreased ratio of NADH/NAD⁺ with nutrient depletion. The relevance of GOT1 as a potential target in cancer therapy was supported by a lung adenocarcinoma RNA-seq data set as well as the GEO:GSE database of metastatic melanoma where GOT1 expression was increased. High levels of GOT1 were further linked to poor survival as analysed by the GEPIA web tool, in thyroid and breast carcinoma and in lung adenocarcinoma.

CONCLUSIONS

Our study suggests an important role of GOT1 to coordinate the glycolytic and the oxidative phosphorylation pathways in KRAS mutated cancer cells. GOT1 is crucial to provide oxaloacetate at low glucose levels, likely to maintain the redox homeostasis. Our data suggest GOT1 as a possible target in cancer therapy.

Clinical verification of plasma messenger RNA as novel noninvasive biomarker identified through bioinformatics analysis for lung cancer

Lung cancer (LC) remains associated with significant mortality worldwide. The lack of reliable noninvasive biomarkers and targeted therapies contributes to poor survival rate. Herein, we initially took advantage of the public microarray data from Oncomine database to filter messenger RNAs (mRNAs) as potential biomarkers. Subsequently, clinical validation was applied to identify candidate noninvasive biomarkers in plasma from patients with LC. Through comprehensive analysis of transcriptional expression profiles across 12 studies, top 6 over- and underexpressed mRNAs were generated. Then, a pair of matched plasma samples from LC patient and normal control was detected by RT-PCR, and three genes with positive bands were selected for further validation. Finally, qPCR was conducted to further assess values of the three identified genes. We displayed with high confidence that two cell-free mRNAs (HJURP and ADAMTS8) were expressed at significantly levels compared to normal controls. Receiver-operating characteristic (ROC) curves on the diagnostic efficacy of plasma HJURP and ADAMTS8 mRNAs in LC diagnosis showed that the area under the ROC (AUC) was 0.6960 and 0.6877; sensitivity was 66.0% and 83.7%; specificity was 78.6% and 71.4%, respectively. Combined ROC analyses using these two biomarkers revealed an elevated AUC of 0.75. Furthermore, the higher HJURP level could be associated with early-stage LC while lower ADAMTS8 level could be correlated with non-small cell lung cancer. Collectively, circulating HJURP and ADAMTS8 mRNAs are promising noninvasive biomarkers for LC diagnosis. Our integrative strategy provides new insights into novel noninvasive biomarker identification for other types of cancer.

Bioinformatic analysis of prognostic value of ZW10 interacting protein in lung cancer

Background

ZWINT is a crucial component of the mitotic checkpoint. However, its possible role in lung cancer is unclear. In this study, we determined its correlation with lung cancer.

Methods

Real-time PCR and immunohistochemistry (IHC) were used to determine 40 collected clinical lung cancer samples. Chi-square test was used to examine possible correlations between ZWINT expression and clinicopathological factors. The prognostic significance of mRNA expression of ZWINT in lung cancer was evaluated using the Kaplan–Meier plotter. Univariate and multivariate Cox proportional hazards regression analysis were performed to determine whether ZWINT is an independent risk factor for overall survival (OS) and disease-free survival (DFS) of lung cancer patients. Additionally, STRING database was used to analyze protein-protein interactions.

Results

In this study, we screened 13 GSE datasets and detected that ZWINT is highly expressed in multiple carcinomas including lung, melanoma, prostate, nasopharyngeal, gastric, pancreatic, colon, esophageal, ovarian, renal, breast and liver cancer. Real-time PCR and IHC results of collected clinical lung cancer samples confirmed that ZWINT is highly expressed in tumor tissues compared with adjacent non-tumor tissues. Additionally, high expression of ZWINT might predict poor OS and DFS in lung cancer patients. Moreover, disease stage and expression level of ZWINT were correlated with recurrence-free survival and OS in lung cancer. Analysis of protein-protein interaction based on STRING database gained 8 top genes which could interact with ZWINT, including PMF1, MIS12, DSN1, ZW10, BUB1, BUB1B, CASC5, NDC80, NSL1 and NUF2.

Conclusion

ZWINT is aberrantly highly expressed in lung tumor tissues and might be involved in the pathogenesis of lung cancer.

Expression of TARBP1 protein in human non-small-cell lung cancer and its prognostic significance

The aim of the present study was to investigate the expression of transactivation response RNA-binding protein (TARBP)1 and its clinical significance in human non-small-cell lung cancer (NSCLC). TARBP1 expression at the mRNA level was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in 10 NSCLC tissues and paired adjacent normal tissues. TARBP1 protein expression was analyzed in 90 paraffin-embedded NSCLC tissue samples and paired adjacent normal tissues by immunohistochemistry. Statistical analyses were performed to assess the clinicopathological significance of TARBP1 expression. The expression of TARBP1 mRNA was higher in the 10 NSCLC samples than in the paired adjacent non-tumor tissues (P=0.0017). In the paraffin-embedded tissue samples, the expression level of TARBP1 was higher in the cancer tissues than in the adjacent non-cancerous tissues. TARBP1 expression was detected in 76.67% (69/90) of the NSCLC samples and in 22.22% (20/90) of the adjacent normal lung tissues (P<0.001). The expression of TARBP1 was significantly associated with histological grade (P<0.001), clinical stage (P=0.024) and pathological type (P<0.001), along with a decreased overall survival (OS) rate (P<0.001). On multivariate analysis, the expression of TARBP1 was an independent prognostic factor for hazard ratio (OS, 2.729; 95% confidence interval, 1.471-5.061; P=0.003). TARBP1 is overexpressed in NSCLC, and the expression of TARBP1 is associated with pathological grade, clinical stage and pathological type. Thus, TARBP1 may be an independent prognostic marker in patients with NSCLC.

Targeted depletion of PIK3R2 induces regression of lung squamous cell carcinoma

Oncogenic mutations in the PI3K/AKT pathway are present in nearly half of human tumors. Nonetheless, inhibitory compounds of the pathway often induce pathway rebound and tumor resistance. We find that lung squamous cell carcinoma (SQCC), which accounts for ~20% of lung cancer, exhibits increased expression of the PI3K subunit PIK3R2, which is at low expression levels in normal tissues. We tested a new approach to interfere with PI3K/AKT pathway activation in lung SQCC. We generated tumor xenografts of SQCC cell lines and examined the consequences of targeting PIK3R2 expression. In tumors with high PIK3R2 expression, and independently of PIK3CA, KRAS, or PTEN mutations, PIK3R2 depletion induced lung SQCC xenograft regression without triggering PI3K/AKT pathway rebound. These results validate the use PIK3R2 interfering tools for the treatment of lung squamous cell carcinoma.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Network analysis of DEGs and verification experiments reveal the notable roles of PTTG1 and MMP9 in lung cancer

Lung cancer, a malignant tumor, is the most frequently fatal cancer, with poor survival rates in the advanced stages. In order to improve the understanding of this disease, and to improve the outcomes of patients, additional studies are required. In the present study, differentially expressed genes (DEGs) in patients with lung cancer compared with controls were identified. To understand how these DEGs act together to account for the initiation of lung cancer, a protein interaction network and a transcriptional regulatory network were constructed to explore the clusters and pathways in lung cancer, and the results indicated that PTTG1 and MMP9 served major roles in the development of lung cancer in the regulatory system. Consistent with this, mRNA and protein expression levels of PTTG1 and MMP9 were significantly upregulated in lung cancer tissues compared with normal lung tissues. The overexpression of PTTG1 or MMP9 was induced in the human bronchial epithelial BEAS-2B cell line, indicating that increased PTTG1 or MMP9 alone may not only facilitate cell migration, proliferation and induce colony formation, but also suppress cell apoptosis. In summary, PTTG1 and MMP9 were identified as potential targets for therapeutic intervention through gene therapy in lung cancer.

Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells

Cancer causes millions of deaths annually and microtubule-targeting agents (MTAs) are the most commonly-used anti-cancer drugs. However, the high toxicity of MTAs on normal cells raises great concern. Due to the non-selectivity of MTA targets, we analyzed the interaction network in a non-cancerous human cell. Subnetworks of fourteen MTAs were reconstructed and the merged network was compared against a randomized network to evaluate the functional richness. We found that 71.4% of the MTA interactome nodes are shared, which affects cellular processes such as apoptosis, cell differentiation, cell cycle control, stress response, and regulation of energy metabolism. Additionally, possible secondary targets were identified as client proteins of interphase microtubules. MTAs affect apoptosis signaling pathways by interacting with client proteins of interphase microtubules, suggesting that their primary targets are non-tumor cells. The paclitaxel and doxorubicin networks share essential topological axes, suggesting synergistic effects. This may explain the exacerbated toxicity observed when paclitaxel and doxorubicin are used in combination for cancer treatment.

Distinct lymphocyte antigens 6 (Ly6) family members Ly6D, Ly6E, Ly6K and Ly6H drive tumorigenesis and clinical outcome

Stem cell antigen-1 (Sca-1) is used to isolate and characterize tumor initiating cell populations from tumors of various murine models [1]. Sca-1 induced disruption of TGF-β signaling is required in vivo tumorigenesis in breast cancer models [2, 3-5]. The role of human Ly6 gene family is only beginning to be appreciated in recent literature [6-9]. To study the significance of Ly6 gene family members, we have visualized one hundred thirty gene expression omnibus (GEO) dataset using Oncomine (Invitrogen) and Georgetown Database of Cancer (G-DOC). This analysis showed that four different members Ly6D, Ly6E, Ly6H or Ly6K have increased gene expressed in bladder, brain and CNS, breast, colorectal, cervical, ovarian, lung, head and neck, pancreatic and prostate cancer than their normal counter part tissues. Increased expression of Ly6D, Ly6E, Ly6H or Ly6K was observed in sub-set of cancer type. The increased expression of Ly6D, Ly6E, Ly6H and Ly6K was found to be associated with poor outcome in ovarian, colorectal, gastric, breast, lung, bladder or brain and CNS as observed by KM plotter and PROGgeneV2 platform. The remarkable findings of increased expression of Ly6 family members and its positive correlation with poor outcome on patient survival in multiple cancer type indicate that Ly6 family members Ly6D, Ly6E, Ly6K and Ly6H will be an important targets in clinical practice as marker of poor prognosis and for developing novel therapeutics in multiple cancer type.

Identification of ubiquinol cytochrome c reductase hinge (UQCRH) as a potential diagnostic biomarker for lung adenocarcinoma

Ubiquinol cytochrome c reductase hinge (UQCRH) is a novel protein that localizes in the mitochondrial membrane and induces mitochondrial reactive oxygen species (ROS) generation. It had a high expression rate of 87.10% (108/124) in lung adenocarcinoma. Moreover, serum UQCRH level in patients with lung adenocarcinoma was significantly increased compared with that of pneumonia patients (p < 0.0001) and normal control subjects (p < 0.0001). Receiver operating characteristic curve analysis using an optimal cut-off value of 162.65 pg ml⁻¹ revealed sensitivity and specificity for the diagnosis of lung adenocarcinoma of 88.7% and 85.7%, respectively, with an area under the curve of 0.927 (95% CI: 0.892 to 0.962, p < 0.0001). Serum UQCRH discriminates lung adenocarcinoma patients from the population without cancer with considerable sensitivity and specificity, but it does not distinguish between heavy smokers and lung adenocarcinoma patients. Serum UQCRH could be a potential diagnostic biomarker for lung adenocarcinoma.

Metabolic reprogramming induced by inhibition of SLC2A1 suppresses tumor progression in lung adenocarcinoma

Lung adenocarcinoma (LAC) is one of the common reasons of cancer-related death with few biomarkers for diagnosis and prognosis. Solute carrier family 2 member 1 protein, SLC2A1, has been associated with tumor progression, metastasis, and poor prognosis in many human solid tumors. However, little is reported about its biological functions in lung adenocarcinoma. Here we observed a strong up-regulation of SLC2A1 in patients with LAC and found that SLC2A1 was significantly correlated with prognosis. Knockdown of SLC2A1 in LAC cells inhibits cellular proliferation and plate clone formation in vitro as well as suppression of glucose utilization. Meanwhile, silencing of SLC2A1 also suppresses tumor metastasis in vitro. Mechanistically, GSEA showed that genes in cell cycle pathway were prominently enriched in the higher SLC2A1 group. By a large-scale proteomic analysis, we revealed that cell cycle protein level was significantly increased in SLC2A1-high group. Collectively, our findings indicate that elevated SLC2A1 is a critical modulator in lung adenocarcinoma progression by altering glucose metabolism and the cell cycle pathway, and also suggest SLC2A1 as a promising target for lung adenocarcinoma therapy.

Bioinformatics Tools for Genome-Wide Epigenetic Research

Epigenetics play a central role in the regulation of many important cellular processes, and dysregulations at the epigenetic level could be the source of serious pathologies, such as neurological disorders affecting brain development, neurodegeneration, and intellectual disability. Despite significant technological advances for epigenetic profiling, there is still a need for a systematic understanding of how epigenetics shapes cellular circuitry, and disease pathogenesis. The development of accurate computational approaches for analyzing complex epigenetic profiles is essential for disentangling the mechanisms underlying cellular development, and the intricate interaction networks determining and sensing chromatin modifications and DNA methylation to control gene expression. In this chapter, we review the recent advances in the field of "computational epigenetics," including computational methods for processing different types of epigenetic data, prediction of chromatin states, and study of protein dynamics. We also discuss how "computational epigenetics" has complemented the fast growth in the generation of epigenetic data for uncovering the main differences and similarities at the epigenetic level between individuals and the mechanisms underlying disease onset and progression.

Expression and prognosis analyses of the Tob/BTG antiproliferative (APRO) protein family in human cancers

Background

Despite advances in early diagnosis and treatment, cancer remains the major cause of mortality in the world. The Tob/BTG antiproliferative (APRO) protein family is reported to participate in diverse human diseases. However, there’s little known about their expression and prognostic values in most human cancers.

Methods

We performed a detailed cancer vs. normal analysis. The mRNA expression levels of APRO family in various cancers were analyzed via the Oncomine database. Moreover, the Kaplan-Meier Plotter and PrognScan databases were used to evaluate the prognostic values.

Results

We observed that the mRNA expression levels of TOB1-2 and BTG2 were decreased in most cancers compared with normal tissues, while BTG3 was upregulated in most cancers. In survival analyses based on Kaplan-Meier Plotter, TOB1, BTG1 and BTG4 showed significant associations with survival outcome of different subtypes of breast cancer. Decreased BTG2 was related with poor relapse free survival (RFS) in all subtypes of breast cancer. Especially, besides RFS, reduced BTG2 also indicated worse overall survival and distant metastasis free survival in breast cancer patients who were classified as luminal A. Significant prognostic effects of the whole APRO family were also found in lung adenocarcinoma, but not in squamous cell lung carcinoma. In addition, potential correlations between some APRO family members and survival outcomes were also observed in ovarian, colorectal and brain cancer.

Conclusions

Some members of APRO family showed significant expression differences between cancer and normal tissues, and could be prognostic biomarkers for defined cancer types.

Identification of novel gene expression signature in lung adenocarcinoma by using next-generation sequencing data and bioinformatics analysis

Lung adenocarcinoma is one of the leading causes of cancer-related death worldwide. We showed transcriptomic profiles in three pairs of tumors and adjacent non-tumor lung tissues using next-generation sequencing (NGS) to screen protein-coding RNAs and microRNAs. Combined with meta-analysis from the Oncomine and Gene Expression Omnibus (GEO) databases, we identified a representative genetic expression signature in lung adenocarcinoma. There were 9 upregulated genes, and 8 downregulated genes in lung adenocarcinoma. The analysis of the effects from each gene expression on survival outcome indicated that 6 genes (AGR2, SPDEF, CDKN2A, CLDN3, SFN, and PHLDA2) play oncogenic roles, and 7 genes (PDK4, FMO2, CPED1, GNG11, IL33, BTNL9, and FABP4) act as tumor suppressors in lung adenocarcinoma. In addition, we also identified putative genetic interactions, in which there were 5 upregulated microRNAs with specific targets - hsa-miR-183-5p-BTNL9, hsa-miR-33b-5p-CPED1, hsa-miR-429-CPED1, hsa-miR-182-5p-FMO2, and hsa-miR-130b-5p-IL33. These 5 microRNAs have been shown to be associated with tumorigenesis in lung cancer. Our findings suggest that these genetic interactions play important roles in the progression of lung adenocarcinoma. We propose that this molecular change of genetic expression may represent a novel signature in lung adenocarcinoma, which may be developed for diagnostic and therapeutic strategies in the future.

Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures

Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.

The clinical and prognostic value of polo-like kinase 1 in lung squamous cell carcinoma patients: immunohistochemical analysis

Polo-like kinase 1 (PLK1) has been suggested to serve as an oncogene in most human cancers. The aim of our study is to present more evidence about the clinical and prognostic value of PLK1 in lung squamous cell carcinoma patients. The status of PLK1 was observed in lung adenocarcinoma, lung squamous cell carcinoma, and normal lung tissues through analyzing microarray dataset (GEO accession numbers: GSE1213 and GSE 3627). PLK1 mRNA and protein expressions were detected in lung squamous cell carcinoma and normal lung tissues by using quantitative real-time PCR (qRT-PCR) and immunohistochemistry. In our results, the levels of PLK1 in lung squamous cell carcinoma tissues were higher than that in lung adenocarcinoma tissues. Compared with paired adjacent normal lung tissues, the PLK1 expression was increased in lung squamous cell carcinoma tissues. Furthermore, high expression of PLK1 protein was correlated with differentiated degree, clinical stage, tumor size, lymph node metastasis, and distant metastasis. The univariate and multivariate analyses showed PLK1 protein high expression was an unfavorable prognostic biomarker for lung squamous cell carcinoma patients. In conclusion, high expression of PLK1 is associated with the aggressive progression and poor prognosis in lung squamous cell carcinoma patients.

Genome wide host gene expression analysis in mice experimentally infected with Pasteurella multocida

Pasteurella multocida causes acute septicemic and respiratory diseases, including haemorrhagic septicaemia, in cattle and buffalo with case fatality of 100%. In the present study, mice were infected with P. multocida (1.6 × 10³ cfu, intraperitoneal) to evaluate host gene expression profile at early and late stages of infection using high throughput microarray transcriptome analyses. Several differentially expressed genes (DEGs) at both the time points were identified in P.multocida infected spleen, liver and lungs. Functional annotation of these DEGs showed enrichment of key pathways such as TLR, NF-κB, MAPK, TNF, JAK-STAT and NOD like receptor signaling pathways. Several DEGs overlapped across different KEGG pathways indicating a crosstalk between them. The predicted protein—protein interaction among these DEGs suggested, that the recognition of P. multocida LPS or outer membrane components by TLR4 and CD14, results in intracellular signaling via MyD88, IRAKs and/or TRAF6 leading to activation of NFκB and MAPK pathways and associated cytokines.

Upregulation of TSPAN12 is associated with the colorectal cancer growth and metastasis

Tetraspanin 12 (TSPAN12), as an earliest member of tetraspanin family, has been recently shown to be highly expressed in several malignant tumors, such as lung cancer and breast cancer, which plays an important role in regulating cell proliferation, migration and invasion. However, the functional roles of TSPAN12 in colorectal cancer (CRC) remain largely unclear. In this study, the expression of TSPAN12 was up-regulated compared to that in paracarcinoma tissues. Higher TSPAN12 expression was significantly correlated with TNM stage, tumor size and lymph node metastasis. The vitro functional analysis, including MTT, colony formation, flow cytometry and transwell assays indicated that lentivirus-mediated TSPAN12 knockdown significantly suppressed cell proliferation, migration and invasion, induced cell apoptosis of CRC cells. In addition, knockdown of TSPAN12 remarkably decreased the growth of subcutaneously inoculated tumors in nude mice. Our findings for the first time supported that TSPAN12 might play a positive role in the regulation of CRC cell proliferation, migration and invasion. The inhibition of TSPAN12 may serve as a novel promising therapeutic strategy against human CRC.

Vitiligo blood transcriptomics provides new insights into disease mechanisms and identifies potential novel therapeutic targets

BACKGROUND

Significant gaps remain regarding the pathomechanisms underlying the autoimmune response in vitiligo (VL), where the loss of self-tolerance leads to the targeted killing of melanocytes. Specifically, there is incomplete information regarding alterations in the systemic environment that are relevant to the disease state.

METHODS

We undertook a genome-wide profiling approach to examine gene expression in the peripheral blood of VL patients and healthy controls in the context of our previously published VL-skin gene expression profile. We used several in silico bioinformatics-based analyses to provide new insights into disease mechanisms and suggest novel targets for future therapy.

RESULTS

Unsupervised clustering methods of the VL-blood dataset demonstrate a "disease-state"-specific set of co-expressed genes. Ontology enrichment analysis of 99 differentially expressed genes (DEGs) uncovers a down-regulated immune/inflammatory response, B-Cell antigen receptor (BCR) pathways, apoptosis and catabolic processes in VL-blood. There is evidence for both type I and II interferon (IFN) playing a role in VL pathogenesis. We used interactome analysis to identify several key blood associated transcriptional factors (TFs) from within (STAT1, STAT6 and NF-kB), as well as "hidden" (CREB1, MYC, IRF4, IRF1, and TP53) from the dataset that potentially affect disease pathogenesis. The TFs overlap with our reported lesional-skin transcriptional circuitry, underscoring their potential importance to the disease. We also identify a shared VL-blood and -skin transcriptional "hot spot" that maps to chromosome 6, and includes three VL-blood dysregulated genes (PSMB8, PSMB9 and TAP1) described as potential VL-associated genetic susceptibility loci. Finally, we provide bioinformatics-based support for prioritizing dysregulated genes in VL-blood or skin as potential therapeutic targets.

CONCLUSIONS

We examined the VL-blood transcriptome in context with our (previously published) VL-skin transcriptional profile to address a major gap in knowledge regarding the systemic changes underlying skin-specific manifestation of vitiligo. Several transcriptional "hot spots" observed in both environments offer prioritized targets for identifying disease risk genes. Finally, within the transcriptional framework of VL, we identify five novel molecules (STAT1, PRKCD, PTPN6, MYC and FGFR2) that lend themselves to being targeted by drugs for future potential VL-therapy.

Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis

BACKGROUND

The involvement of multiple genes and missing heritability, which are dominant in complex diseases such as multiple sclerosis (MS), entail using network biology to better elucidate their molecular basis and genetic factors. We therefore aimed to integrate interactome (protein-protein interaction (PPI)) and transcriptomes data to construct and analyze PPI networks for MS disease.

METHODS

Gene expression profiles in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from MS patients, sampled in relapse or remission and controls, were analyzed. Differentially expressed genes which determined only in CSF (MS vs. control) and PBMCs (relapse vs. remission) separately integrated with PPI data to construct the Query-Query PPI (QQPPI) networks. The networks were further analyzed to investigate more central genes, functional modules and complexes involved in MS progression.

RESULTS

The networks were analyzed and high centrality genes were identified. Exploration of functional modules and complexes showed that the majority of high centrality genes incorporated in biological pathways driving MS pathogenesis. Proteasome and spliceosome were also noticeable in enriched pathways in PBMCs (relapse vs. remission) which were identified by both modularity and clique analyses. Finally, STK4, RB1, CDKN1A, CDK1, RAC1, EZH2, SDCBP genes in CSF (MS vs. control) and CDC37, MAP3K3, MYC genes in PBMCs (relapse vs. remission) were identified as potential candidate genes for MS, which were the more central genes involved in biological pathways.

DISCUSSION

This study showed that network-based analysis could explicate the complex interplay between biological processes underlying MS. Furthermore, an experimental validation of candidate genes can lead to identification of potential therapeutic targets.

Epigenetic and genetic deregulation in cancer target distinct signaling pathway domains

Cancer is characterized by both genetic and epigenetic alterations. While cancer driver mutations and copy-number alterations have been studied at a systems-level, relatively little is known about the systems-level patterns exhibited by their epigenetic counterparts. Here we perform a pan-cancer wide systems-level analysis, mapping candidate cancer-driver DNA methylation (DNAm) alterations onto a human interactome. We demonstrate that functional DNAm alterations in cancer tend to map to nodes of lower connectivity and inter-connectivity, compared to the corresponding alterations at the genomic level. We find that epigenetic alterations are relatively over-represented in extracellular and transmembrane signaling domains, whereas cancer genes undergoing amplification or deletion tend to be enriched within the intracellular domain. A pan-cancer wide meta-analysis identifies WNT and chemokine signaling, as two key pathways where epigenetic deregulation preferentially targets extracellular components. We further pinpoint specific chemokine ligands/receptors whose epigenetic deregulation associates with key epigenetic enzymes, representing potential targets for epigenetic therapy. Our results suggest that epigenetic deregulation in cancer not only targets tissue-specific transcription factors, but also modulates signaling within the extra-cellular domain, providing novel system-level insight into the potential distinctive role of genetic and epigenetic alterations in cancer.

A Network Approach of Gene Co-expression in the Zea mays/Aspergillus flavus Pathosystem to Map Host/Pathogen Interaction Pathways

A gene co-expression network (GEN) was generated using a dual RNA-seq study with the fungal pathogen Aspergillus flavus and its plant host Zea mays during the initial 3 days of infection. The analysis deciphered novel pathways and mapped genes of interest in both organisms during the infection. This network revealed a high degree of connectivity in many of the previously recognized pathways in Z. mays such as jasmonic acid, ethylene, and reactive oxygen species (ROS). For the pathogen A. flavus, a link between aflatoxin production and vesicular transport was identified within the network. There was significant interspecies correlation of expression between Z. mays and A. flavus for a subset of 104 Z. mays, and 1942 A. flavus genes. This resulted in an interspecies subnetwork enriched in multiple Z. mays genes involved in the production of ROS. In addition to the ROS from Z. mays, there was enrichment in the vesicular transport pathways and the aflatoxin pathway for A. flavus. Included in these genes, a key aflatoxin cluster regulator, AflS, was found to be co-regulated with multiple Z. mays ROS producing genes within the network, suggesting AflS may be monitoring host ROS levels. The entire GEN for both host and pathogen, and the subset of interspecies correlations, is presented as a tool for hypothesis generation and discovery for events in the early stages of fungal infection of Z. mays by A. flavus.

Global gene expression profile of peripheral blood mononuclear cells challenged with Theileria annulata in crossbred and indigenous cattle

Bovine tropical theileriosis is an important haemoprotozoan disease associated with high rates of morbidity and mortality particularly in exotic and crossbred cattle. It is one of the major constraints of the livestock development programmes in India and Southeast Asia. Indigenous cattle (Bos indicus) are reported to be comparatively less affected than exotic and crossbred cattle. However, genetic basis of resistance to tropical theileriosis in indigenous cattle is not well documented. Recent studies incited an idea that differentially expressed genes in exotic and indigenous cattle play significant role in breed specific resistance to tropical theileriosis. The present study was designed to determine the global gene expression profile in peripheral blood mononuclear cells derived from indigenous (Tharparkar) and cross-bred cattle following in vitro infection of T. annulata (Parbhani strain). Two separate microarray experiments were carried out each for cross-bred and Tharparkar cattle. The cross-bred cattle showed 1082 differentially expressed genes (DEGs). Out of total DEGs, 597 genes were down-regulated and 485 were up-regulated. Their fold change varied from 2283.93 to -4816.02. Tharparkar cattle showed 875 differentially expressed genes including 451 down-regulated and 424 up-regulated. The fold change varied from 94.93 to -19.20. A subset of genes was validated by qRT-PCR and results were correlated well with microarray data indicating that microarray results provided an accurate report of transcript level. Functional annotation study of DEGs confirmed their involvement in various pathways including response to oxidative stress, immune system regulation, cell proliferation, cytoskeletal changes, kinases activity and apoptosis. Gene network analysis of these DEGs plays an important role to understand the interaction among genes. It is therefore, hypothesized that the different susceptibility to tropical theileriosis exhibited by indigenous and crossbred cattle is due to breed-specific differences in the dealing of infected cells with other immune cells, which ultimately influence the immune response responded against T. annulata infection.

The Beta Subunit of Hemoglobin (HBB2/HBB) Suppresses Neuroblastoma Growth and Metastasis

Soluble pulmonary factors have been reported to be capable of inhibiting the viability of cancer cells that metastasize to the lung, but the molecular identity was obscure. Here we report the isolation and characterization of the beta subunit of hemoglobin as a lung-derived antimetastatic factor. Peptide mapping in the beta subunit of human hemoglobin (HBB) defined a short C-terminal region (termed Metox) as responsible for activity. In tissue culture, both HBB and murine HBB2 mediated growth arrest and apoptosis of lung-metastasizing neuroblastoma cells, along with a variety of other human cancer cell lines. Metox acted similarly and its administration in human tumor xenograft models limited the development of adrenal neuroblastoma tumors as well as spontaneous lung and bone marrow metastases. Expression studies in mice indicated that HBB2 is produced by alveolar epithelial and endothelial cells and is upregulated in mice bearing undetectable metastasis. Our work suggested a novel function for HBB as a theranostic molecule: an innate antimetastasis factor with potential utility as an anticancer drug and a biomarker signaling the presence of clinically undetectable metastasis. Cancer Res; 77(1); 14-26. ©2016 AACR.

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

Biological computational approaches: new hopes to improve (re)programming robustness, regenerative medicine and cancer therapeutics

Hundreds of transcription factors (TFs) are expressed and work in each cell type, but the identity of the cells is defined and maintained through the activity of a small number of core TFs. Existing reprogramming strategies predominantly focus on the ectopic expression of core TFs of an intended fate in a given cell type regardless of the state of native/somatic gene regulatory networks (GRNs) of the starting cells. Interestingly, an important point is that how much products of the reprogramming, transdifferentiation and differentiation (programming) are identical to their in vivo counterparts. There is evidence that shows that direct fate conversions of somatic cells are not complete, with target cell identity not fully achieved. Manipulation of core TFs provides a powerful tool for engineering cell fate in terms of extinguishment of native GRNs, the establishment of a new GRN, and preventing installation of aberrant GRNs. Conventionally, core TFs are selected to convert one cell type into another mostly based on literature and the experimental identification of genes that are differentially expressed in one cell type compared to the specific cell types. Currently, there is not a universal standard strategy for identifying candidate core TFs. Remarkably, several biological computational platforms are developed, which are capable of evaluating the fidelity of reprogramming methods and refining existing protocols. The current review discusses some deficiencies of reprogramming technologies in the production of a pure population of authentic target cells. Furthermore, it reviews the role of computational approaches (e.g. CellNet, KeyGenes, Mogrify, etc.) in improving (re)programming methods and consequently in regenerative medicine and cancer therapeutics.

Using biological networks to integrate, visualize and analyze genomics data

Network biology is a rapidly developing area of biomedical research and reflects the current view that complex phenotypes, such as disease susceptibility, are not the result of single gene mutations that act in isolation but are rather due to the perturbation of a gene’s network context. Understanding the topology of these molecular interaction networks and identifying the molecules that play central roles in their structure and regulation is a key to understanding complex systems. The falling cost of next-generation sequencing is now enabling researchers to routinely catalogue the molecular components of these networks at a genome-wide scale and over a large number of different conditions. In this review, we describe how to use publicly available bioinformatics tools to integrate genome-wide ‘omics’ data into a network of experimentally-supported molecular interactions. In addition, we describe how to visualize and analyze these networks to identify topological features of likely functional relevance, including network hubs, bottlenecks and modules. We show that network biology provides a powerful conceptual approach to integrate and find patterns in genome-wide genomic data but we also discuss the limitations and caveats of these methods, of which researchers adopting these methods must remain aware.

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC.

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.

Prediction of disease-gene-drug relationships following a differential network analysis

Great efforts are being devoted to get a deeper understanding of disease-related dysregulations, which is central for introducing novel and more effective therapeutics in the clinics. However, most human diseases are highly multifactorial at the molecular level, involving dysregulation of multiple genes and interactions in gene regulatory networks. This issue hinders the elucidation of disease mechanism, including the identification of disease-causing genes and regulatory interactions. Most of current network-based approaches for the study of disease mechanisms do not take into account significant differences in gene regulatory network topology between healthy and disease phenotypes. Moreover, these approaches are not able to efficiently guide database search for connections between drugs, genes and diseases. We propose a differential network-based methodology for identifying candidate target genes and chemical compounds for reverting disease phenotypes. Our method relies on transcriptomics data to reconstruct gene regulatory networks corresponding to healthy and disease states separately. Further, it identifies candidate genes essential for triggering the reversion of the disease phenotype based on network stability determinants underlying differential gene expression. In addition, our method selects and ranks chemical compounds targeting these genes, which could be used as therapeutic interventions for complex diseases.

Identification of potential therapeutic targets for lung cancer by bioinformatics analysis

The aim of the present study was to identify potential therapeutic targets for lung cancer and explore underlying molecular mechanisms of its development and progression. The gene expression profile datasets no. GSE3268 and GSE19804, which included five and 60 pairs of tumor and normal lung tissue specimens, respectively, were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between lung cancer and normal tissues were identified, and gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of the DEGs was performed. Furthermore, protein‑protein interaction (PPI) networks and a transcription factor (TF) regulatory network were constructed and key target genes were screened. A total of 466 DEGs were identified, and the PPI network indicated that IL‑6 and MMP9 had key roles in lung cancer. A PPI module containing 34 nodes and 547 edges was obtained, including PTTG1. The TF regulatory network indicated that TFs of FOSB and LMO2 had a key role. Furthermore, MMP9 was indicated to be the target of FOSB, while PTTG1 was the target of LMO2. In conclusion, the bioinformatics analysis of the present study indicated that IL‑6, MMP9 and PTTG1 may have key roles in the progression and development of lung cancer and may potentially be used as biomarkers or specific therapeutic targets for lung cancer.