A network medicine approach to build a comprehensive atlas for the prognosis of human cancer

A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

The Immune-related ceRNA Network in Prognosis of Cervical Cancer

BACKGROUND

Immunotherapy is gaining attention and it is being included as one of the treatment strategies for cancer patients. However, the molecular mechanisms of immune-related genes and their affinity for cervical cancer progression remain unclear. In this study, we have developed an immune-related competing endogenous RNA [ceRNA] network and assessed the tumour infiltrating immune cells towards the prognosis of cervical cancer.

METHODS

Differential RNA expression pattern between stages I and II-IV of cervical cancer patients from The Cancer Genome Atlas [TCGA] was analyzed. Immune-related ceRNA network based on the immune gene signatures were retrieved and their targets were predicted using miRwalk 3.0. CIBERSORT was employed to identify the immune cell types based on their respective transcripts. The prognostic significance of RNAs in the ceRNA network and immune cell subsets was analyzed.

RESULTS

Significant differences in 22 long non-coding RNAs [lncRNAs], 15 microRNAs [miRNAs], and 252 messenger RNAs [mRNAs] between stages I and II-IV of cervical cancer were observed. Further, we shortlisted the 49 immune-related mRNAs based on immune gene signature and predicted their target miRNAs and lncRNAs. A potential ceRNA network of 4 lncRNAs, 10 miRNAs, and 11 mRNAs had a strong correlation for prognosis. Out of 11 protein-coding immune mRNAs, IRF4 and AZGP1 had high degrees of interaction. In addition, the evaluation of immune cell subsets showed increased infiltration of M1 macrophages had better survival outcome.

CONCLUSIONS

We have identified an immune-related ceRNA network based on differentially expressed transcripts between stages I and II-IV which may help predict the prognosis of cervical cancer.

Identifying Molecular Biomarkers for Diseases With Machine Learning Based on Integrative Omics

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular 'omics' data, e.g., transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen biomarkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarkers with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we further discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Study on the Prognostic Values of Dynactin Genes in Low-Grade Glioma

OBJECTIVE

This present study aims to investigate the potential prognostic values of dynactin genes (DCTN) for predicting the overall survival (OS) in low-grade glioma (LGG) patients.

METHODS

The DCTN mRNA expression data were downloaded from The Cancer Genome Atlas database containing 518 patients with LGG. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses for DCTN genes were performed by using Database for Annotation, Visualization, and Integrated Discovery platform, and their enrichment results were verified by using the Biological Networks Gene Ontology tool. Next, the correlations between DCTN genes and LGG were identified by Pearson correlation coefficient analysis. The OS was estimated by Kaplan-Meier survival analysis. The cBio Cancer Genomics Portal was used to analyze the mutations of DCTN genes and their effects on the prognosis of LGG. The correlation between the abundance of immune infiltration and tumor purity of DCTN genes were predicted by The Tumor Immune Estimation Resource.

RESULTS

Our research showed that the mRNA expression of DCTN4 in tumor tissues was much higher (P < 0.01) than that in normal tissues. Meanwhile, there was a certain correlation between the DCTN genes. Survival analysis showed that the high expression of DCTN1, DCTN3, DCTN4, DCTN6, and their co-expression were significantly correlated with favorable OS in LGG patients (P < 0.05). In DCTN2, a high mutation rate was observed. Further research showed that the genetic alteration in DCTN genes was related to a poor OS and progression-free survival of LGG patients. The expression of DCTN genes had a certain correlation with immune infiltrating cells.

CONCLUSION

Our study showed that the high expressions of DCTN1, DCTN3, DCTN4, and DCTN6 were associated with a favorable OS of LGG patients, indicating that these DCTN genes are potential biomarkers for evaluating the prognosis of LGG patients.

Amino Acid Conjugates of Aminothiazole and Aminopyridine as Potential Anticancer Agents: Synthesis, Molecular Docking and in vitro Evaluation

PURPOSE

The development of resistance to available anticancer drugs is increasingly becoming a major challenge and new chemical entities could be unveiled to compensate this therapeutic failure. The current study demonstrated the synthesis of 2-aminothiazole [S3(a-d) and S5(a-d)] and 2-aminopyridine [S4(a-d) and S6(a-d)] derivatives that can target multiple cellular networks implicated in cancer development.

METHODS

Biological assays were performed to investigate the antioxidant and anticancer potential of synthesized compounds. Redox imbalance and oxidative stress are hallmarks of cancer, therefore, synthesized compounds were preliminarily screened for their antioxidant activity using DPPH assay, and further five derivatives S3b, S3c, S4c, S5b, and S6c, with significant antioxidant potential, were selected for investigation of in vitro anticancer potential. The cytotoxic activities were evaluated against the parent (A2780) and cisplatin-resistant (A2780CISR) ovarian cancer cell lines. Further, Molecular docking studies of active compounds were performed to determine binding affinities.

RESULTS

Results revealed that S3c, S5b, and S6c displayed promising inhibition in cisplatin-resistant cell lines in comparison to parent cells in terms of both resistance factor (RF) and IC50 values. Moreover, S3c proved to be most active compound in both parent and resistant cell lines with IC50 values 15.57 µM and 11.52 µM respectively. Our docking studies demonstrated that compounds S3c, S5b, and S6c exhibited significant binding affinity with multiple protein targets of the signaling cascade.

CONCLUSION

Anticancer activities of compounds S3c, S5b, and S6c in cisplatin-resistant cell lines suggested that these ligands may contribute as lead compounds for the development of new anticancer drugs.

A 7-lncRNA signature associated with the prognosis of colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients.

Methods

The samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs.

Results

The prognostic model was able to calculate each COAD patient’s risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways.

Conclusion

Thus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.

Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data

Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a “Score.” Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.

A census of pathway maps in cancer systems biology

A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.

DNA methylation of SOCS1/2/3 predicts hepatocellular carcinoma recurrence after liver transplantation

DNA methylation status of SOCS1/SOCS2/SOCS3 is intensely involved in the development and progression of hepatocellular carcinoma (HCC). This study explored its prognostic value for HCC recurrence after liver transplantation (LT). Clinical data from 62 HCC patients who underwent LT at our centre were retrospectively collected. The SOCS1/2/3 methylation level were determined using next generation sequencing. Overall, 244 methylated sites at the SOCS1/2/3 promoter were identified. Multivariate analysis yielded the methylated sites SOCS2-1-90 (Chromosome 12, Position 93963982; HR 0.386, 95% CI 0.149-0.998) and SOCS1-1-68 (Chromosome 16, Position 11350699; HR 4.376, 95% CI 1.324-14.459) as independent predictors of post-LT HCC recurrence. Patients were divided into highly- and lowly methylated groups according to the median SOCS1-1-68 (0.95%) and SOCS2-1-90 (1.05%) methylation levels. Highly methylated SOCS2-1-90 was associated with significantly lower AFP levels (P = 0.008), decreased proportion of maximal tumour size > 8 cm (P = 0.02), and better pathological grading (P = 0.06). Conversely, patients in the highly methylated SOCS1-1-68 group had higher AFP levels (P = 0.043). Kaplan-Meier analyses revealed that patients with highly methylated SOCS2-1-90 had increased recurrence free survival (RFS) and overall survival (OS) rates when compared with those with lowly methylated SOCS2-1-90 (P = 0.0041 and 0.012, respectively). Nevertheless, the correlation between methylated SOCS1-1-68 and cumulative recurrence rates was less pronounced (P = 0.098). Subgroup analyses demonstrated that patients meeting the Milan criteria, UCSF criteria, Metroticket 2.0 Model or Hangzhou criteria with highly methylated SOCS2-1-90 had the best RFS rates. DNA methylation of SOCS2-1-90 is a novel biomarker for predicting post-transplant HCC recurrence.

Cancer Biomarker Discovery for Precision Medicine: New Progress

BACKGROUND

Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued.

OBJECTIVE

This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine.

RESULTS

The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients' survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients' survival.

CONCLUSION

Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data

Background

Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.

Methods

In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.

Results

Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP (http://bio-bigdata.hrbmu.edu.cn/CancerDAP/) available to explore 2751 subpathways relevant with 191 anticancer drugs response.

Conclusions

Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-2010-4) contains supplementary material, which is available to authorized users.

Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Recurrence-Associated Long Non-coding RNA Signature for Determining the Risk of Recurrence in Patients with Colon Cancer

Patients with colon cancer are often faced a high risk of disease recurrence within 5 years of treatment that is the major cause of cancer mortality. Reliable molecular markers were required to improve the most effective personalized therapy. Here, we identified a recurrence-associated six-lncRNA (long non-coding RNA) signature (LINC0184, AC105243.1, LOC101928168, ILF3-AS1, MIR31HG, and AC006329.1) that can effectively distinguish between high and low risk of cancer recurrence from 389 patients of a discovery dataset, and validated its robust performance in four independent datasets comprising a total of 906 colon cancer patients. We found that the six-lncRNA signature was an independent predictive factor of disease recurrence in multivariate analysis and was superior to the performance of clinical factors and known gene signature. Furthermore, in silico functional analysis showed that the six-lncRNA-signature-associated coding genes are significantly enriched in proliferation and angiogenesis, cell death, as well as critical cancer pathways that could play important roles in colon cancer recurrence. Together, the six-lncRNA signature holds great potential for recurrence risk assessment and personalized management of colon cancer patients.

Construction of differential mRNA-lncRNA crosstalk networks based on ceRNA hypothesis uncover key roles of lncRNAs implicated in esophageal squamous cell carcinoma

Increasing evidence has indicated that lncRNAs acting as competing endogenous RNAs (ceRNAs) play crucial roles in tumorigenesis, metastasis and diagnosis of cancer. However, the function of lncRNAs as ceRNAs involved in esophageal squamous cell carcinoma (ESCC) is still largely unknown. In this study, clinical implications of two intrinsic subtypes of ESCC were identified based on expression profiles of lncRNA and mRNA. ESCC subtype-specific differential co-expression networks between mRNAs and lncRNAs were constructed to reveal dynamic changes of their crosstalks mediated by miRNAs during tumorigenesis. Several well-known cancer-associated lncRNAs as the hubs of the two networks were firstly proposed in ESCC. Based on the ceRNA mechanism, we illustrated that the"loss" of miR-186-mediated PVT1-mRNA and miR-26b-mediated LINC00240-mRNA crosstalks were related to the two ESCC subtypes respectively. In addition, crosstalks between LINC00152 and EGFR, LINC00240 and LOX gene family were identified, which were associated with the function of "response to wounding" and "extracellular matrix-receptor interaction". Furthermore, functional cooperation of multiple lncRNAs was discovered in the two differential mRNA-lncRNA crosstalk networks. These together systematically uncovered the roles of lncRNAs as ceRNAs implicated in ESCC.

Identification of novel prognostic indicators for triple-negative breast cancer patients through integrative analysis of cancer genomics data and protein interactome data

Triple negative breast cancers (TNBCs) are highly heterogeneous and aggressive without targeted treatment. Here, we aim to systematically dissect TNBCs from a prognosis point of view by building a subnetwork atlas for TNBC prognosis through integrating multi-dimensional cancer genomics data from The Cancer Genome Atlas (TCGA) project and the interactome data from three different interaction networks. The subnetworks are represented as the protein-protein interaction modules perturbed by multiple genetic and epigenetic interacting mechanisms contributing to patient survival. Predictive power of these subnetwork-derived prognostic models is evaluated using Monte Carlo cross-validation and the concordance index (C-index). We uncover subnetwork biomarkers of low oncogenic GTPase activity, low ubiquitin/proteasome degradation, effective protection from oxidative damage, and tightly immune response are linked to better prognosis. Such a systematic approach to integrate massive amount of cancer genomics data into clinical practice for TNBC prognosis can effectively dissect the molecular mechanisms underlying TNBC patient outcomes and provide potential opportunities to optimize treatment and develop therapeutics.

Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer

Triple negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Currently, there is increasing focus on long non-coding RNAs (lncRNAs), which can act as competing endogenous RNAs (ceR-NAs) and suppress miRNA functions involved in post-transcriptional regulatory networks in the tumor. Therefore, to investigate specific mechanisms of TNBC carcinogenesis and improve treatment efficiency, we comprehensively integrated expression profiles, including data on mRNAs, lncRNAs and miRNAs obtained from 116 TNBC tissues and 11 normal tissues from The Cancer Genome Atlas. As a result, we selected the threshold with |log2FC|>2.0 and an adjusted p-value >0.05 to obtain the differentially expressed mRNAs, miRNAs and lncRNAs. Hereafter, weighted gene co-expression network analysis was performed to identify the expression characteristics of dysregulated genes. We obtained five co-expression modules and related clinical feature. By means of correlating gene modules with protein-protein interaction network analysis that had identified 22 hub mRNAs which could as hub target genes. Eleven key dysregulated differentially expressed micro RNAs (DEmiRNAs) were identified that were significantly associated with the 22 hub potential target genes. Moreover, we found that 14 key differentially expressed lncRNAs could interact with the key DEmiRNAs. Then, the ceRNA crosstalk network of TNBC was constructed by utilizing key lncRNAs, key miRNAs, and hub mRNAs in Cytoscape software. We analyzed and described the potential characteristics of biological function and pathological roles of the TNBC ceRNA co-regulatory network; also, the survival analysis was performed for each molecule. These findings revealed that ceRNA crosstalk network could play an important role in the development and progression for TNBC. In addition, we also identified that some molecules in the ceRNA network possess clinical correlation and prognosis.

DisSetSim: an online system for calculating similarity between disease sets

Background

Functional similarity between molecules results in similar phenotypes, such as diseases. Therefore, it is an effective way to reveal the function of molecules based on their induced diseases. However, the lack of a tool for obtaining the similarity score of pair-wise disease sets (SSDS) limits this type of application.

Results

Here, we introduce DisSetSim, an online system to solve this problem in this article. Five state-of-the-art methods involving Resnik’s, Lin’s, Wang’s, PSB, and SemFunSim methods were implemented to measure the similarity score of pair-wise diseases (SSD) first. And then “pair-wise-best pairs-average” (PWBPA) method was implemented to calculated the SSDS by the SSD. The system was applied for calculating the functional similarity of miRNAs based on their induced disease sets. The results were further used to predict potential disease-miRNA relationships.

Conclusions

The high area under the receiver operating characteristic curve AUC (0.9296) based on leave-one-out cross validation shows that the PWBPA method achieves a high true positive rate and a low false positive rate. The system can be accessed from http://www.bio-annotation.cn:8080/DisSetSim/.

A long non-coding RNA expression signature to predict survival of patients with colon adenocarcinoma

Colon cancer is the most common type of gastrointestinal cancer and is still the leading cause of cancer-related mortality worldwide. Long non-coding RNAs (lncRNAs) have been proved to be superior biomarkers in cancer diagnosis and prognosis than miRNAs and protein-coding genes. In the current study, our objective was to detect novel lncRNA biomarkers by analyzing lncRNA expression profiles and clinical data in a large cohort of patients with colon patients from The Cancer Genome Atlas (TCGA). By using Cox regression analysis, we identified two lncRNAs (SNHG6 and CTD-2354A18.1) which could be independent prognostic factors for predicting clinical outcome in colon adenocarcinoma. Then a linear combination of these two lncRNA biomarkers (SNHG6 and CTD-2354A18.1), termed two-lncRNA signature, was developed in the training set as a predictor for OS in patients with colon adenocarcinoma, and validated in the testing set and the entire patient set. This two-lncRNA signature demonstrated significant prognostic performance in both the testing set and the entire patient set which classified the patients into two groups with significantly different OS. The multivariate and stratified analysis suggested that the prognostic value of the two-lncRNA signature was independent of other traditional clinical variables. Functional analysis suggested that these two lncRNA biomarkers might be mainly involved in transcription/translation-related or DNA repair-related biological processes. In summary, our results warrant further studies on these lncRNAs that will improve our understanding of the mechanisms associated with pathogenesis and progression of colon adenocarcinoma.

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

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

Hedgehog signaling pathway in colorectal cancer: function, mechanism, and therapy

Colorectal cancer (CRC) is one of the most common gastrointestinal cancers worldwide. It is a complicated and often fatal cancer, and is related to a high disease-related mortality. Around 90% of mortalities are caused by the metastasis of CRC. Current treatment statistics shows a less than 5% 5-year survival for patients with metastatic disease. The development and metastasis of CRC involve multiple factors and mechanisms. The Hedgehog (Hh) signaling plays an important role in embryogenesis and somatic development. Abnormal activation of the Hh pathway has been proven to be related to several types of human cancers. The role of Hh signaling in CRC, however, remains controversial. In this review, we will go through previous literature on the Hh signaling and its functions in the formation, proliferation, and metastasis of CRC. We will also discuss the potential of targeting Hh signaling pathway in the treatment, prognosis, and prevention of CRC.

LncRNAs2Pathways: Identifying the pathways influenced by a set of lncRNAs of interest based on a global network propagation method

Long non-coding RNAs (lncRNAs) have been demonstrated to play essential roles in diverse cellular processes and biological functions. Exploring the functions associated with lncRNAs may help provide insight into their underlying biological mechanisms. The current methods primarily focus on investigating the functions of individual lncRNAs; however, essential biological functions may be affected by the combinatorial effects of multiple lncRNAs. Here, we have developed a novel computational method, LncRNAs2Pathways, to identify the functional pathways influenced by the combinatorial effects of a set of lncRNAs of interest based on a global network propagation algorithm. A new Kolmogorov–Smirnov-like statistical measure weighted by the network propagation score, which considers the expression correlation among lncRNAs and coding genes, was used to evaluate the biological pathways influenced by the lncRNAs of interest. We have described the LncRNAs2Pathways methodology and illustrated its effectiveness by analyzing three lncRNA sets associated with glioma, prostate and pancreatic cancers. We further analyzed the reproducibility and robustness and compared our results with those of two other methods. Based on these analyses, we showed that LncRNAs2Pathways can effectively identify the functional pathways associated with lncRNA sets. Finally, we implemented this method as a freely available R-based tool.

Introducing crucial protein panel of gastric adenocarcinoma disease

AIM

Since interactome analysis of diseases can provide candidate biomarker panel related to the diseases, in this research, protein-protein interaction (PPI) network analysis is used to introduce the involved crucial proteins in Gastric adenocarcinoma (GA).

BACKGROUND

Gastric adenocarcinoma (GA) is the most common type of stomach cancer. There is no efficient diagnostic molecular method for GA.

METHOD

Applying Cytoscape software 3.4.0 and String Database, the PPI network was constructed for 200 genes. Based on centrality parameters, the critical nodes were screened. Gene ontology of the key proteins for pathway analysis and molecular function processing were done and the highlighted pathways and activities were discussed.

RESULTS

Among 200 initial genes, 141 genes were included in a main connected network. Seven crucial proteins, including tumor protein p53, epidermal growth factor receptor, albumin, v-erb-b2 erythroblastic leukemia viral oncogene homolog 2, neuro/glioblastoma derived oncogene homolog (avian), v-akt murine thymoma viral oncogene homolog 1, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) and catenin (cadherin-associated protein), beta 1, 88kDa, and Myogenic differentiation 1, were introduced as key nodes of the network. These identified proteins are mostly involved in pathways and activities related to cancer.

CONCLUSION

In conclusion, the finding is corresponding to the significant roles of these introduced proteins in GA disease. This protein panel may be a useful probe in the management of GA.

OAHG: an integrated resource for annotating human genes with multi-level ontologies

OAHG, an integrated resource, aims to establish a comprehensive functional annotation resource for human protein-coding genes (PCGs), miRNAs, and lncRNAs by multi-level ontologies involving Gene Ontology (GO), Disease Ontology (DO), and Human Phenotype Ontology (HPO). Many previous studies have focused on inferring putative properties and biological functions of PCGs and non-coding RNA genes from different perspectives. During the past several decades, a few of databases have been designed to annotate the functions of PCGs, miRNAs, and lncRNAs, respectively. A part of functional descriptions in these databases were mapped to standardize terminologies, such as GO, which could be helpful to do further analysis. Despite these developments, there is no comprehensive resource recording the function of these three important types of genes. The current version of OAHG, release 1.0 (Jun 2016), integrates three ontologies involving GO, DO, and HPO, six gene functional databases and two interaction databases. Currently, OAHG contains 1,434,694 entries involving 16,929 PCGs, 637 miRNAs, 193 lncRNAs, and 24,894 terms of ontologies. During the performance evaluation, OAHG shows the consistencies with existing gene interactions and the structure of ontology. For example, terms with more similar structure could be associated with more associated genes (Pearson correlation γ² = 0.2428, p < 2.2e-16).