Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

A deep-learning-based genomic status estimating framework for homologous recombination deficiency detection from low-pass whole genome sequencing

Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10-4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10-5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment.

Breast Tumor Metastasis and Its Microenvironment: It Takes Both Seed and Soil to Grow a Tumor and Target It for Treatment

Metastasis remains a major challenge in treating breast cancer. Breast tumors metastasize to organ-specific locations such as the brain, lungs, and bone, but why some organs are favored over others remains unclear. Breast tumors also show heterogeneity, plasticity, and distinct microenvironments. This contributes to treatment failure and relapse. The interaction of breast cancer cells with their metastatic microenvironment has led to the concept that primary breast cancer cells act as seeds, whereas the metastatic tissue microenvironment (TME) is the soil. Improving our understanding of this interaction could lead to better treatment strategies for metastatic breast cancer. Targeted treatments for different subtypes of breast cancers have improved overall patient survival, even with metastasis. However, these targeted treatments are based upon the biology of the primary tumor and often these patients' relapse, after therapy, with metastatic tumors. The advent of immunotherapy allowed the immune system to target metastatic tumors. Unfortunately, immunotherapy has not been as effective in metastatic breast cancer relative to other cancers with metastases, such as melanoma. This review will describe the heterogeneic nature of breast cancer cells and their microenvironments. The distinct properties of metastatic breast cancer cells and their microenvironments that allow interactions, especially in bone and brain metastasis, will also be described. Finally, we will review immunotherapy approaches to treat metastatic breast tumors and discuss future therapeutic approaches to improve treatments for metastatic breast cancer.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

Evaluating the relevance of surgical margins. Part one: The problems with current methodology

The goal of cancer surgery is to achieve a "clean" microscopic resection, with no residual tumour remaining in the wound. To achieve that goal, the surgeon typically incorporates a measured buffer of grossly normal tissue about the entire circumference of the tumour. Microscopic analysis of the resection boundaries is then performed to determine if all traces of the tumour have been completely removed. This analysis is thought to provide a surrogate indication as to the likelihood for that tumour to recur after surgery. However, it is recognised that tumour recurrence may not occur even when microscopic evidence of tumour has been identified at the resection margins, and recurrence can also occur when conventional histology has considered the tumour to have been completely removed. The explanations for this dichotomy are numerous and include technical and practical limitations of the processing methodology, and also several surgeon-related and tumour-related reasons. Ultimately, the inability to confidently determine when a tumour has been removed sufficiently to prevent recurrence can impact on the ability to provide owners with confident treatment advice. In this article, the authors describe the challenges with defining the true extent of the tumour margin from the perspective of the surgeon, the pathologist and the tumour. The authors also provide an analysis of why our current efforts to ensure that all traces of the local tumour have been successfully removed may provide an imperfect assessment of the risk of recurrence.

Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk

Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.

Progresses, Challenges, and Prospects of CRISPR/Cas9 Gene-Editing in Glioma Studies

Glioma refers to a tumor that is derived from brain glial stem cells or progenitor cells and is the most common primary intracranial tumor. Due to its complex cellular components, as well as the aggressiveness and specificity of the pathogenic site of glioma, most patients with malignant glioma have poor prognoses following surgeries, radiotherapies, and chemotherapies. In recent years, an increasing amount of research has focused on the use of CRISPR/Cas9 gene-editing technology in the treatment of glioma. As an emerging gene-editing technology, CRISPR/Cas9 utilizes the expression of certain functional proteins to repair tissues or treat gene-deficient diseases and could be applied to immunotherapies through the expression of antigens, antibodies, or receptors. In addition, some research also utilized CRISPR/Cas9 to establish tumor models so as to study tumor pathogenesis and screen tumor prognostic targets. This paper mainly discusses the roles of CRISPR/Cas9 in the treatment of glioma patients, the exploration of the pathogenesis of neuroglioma, and the screening targets for clinical prognosis. This paper also raises the future research prospects of CRISPR/Cas9 in glioma, as well as the opportunities and challenges that it will face in clinical treatment in the future.

NF-kB in Signaling Patterns and Its Temporal Dynamics Encode/Decode Human Diseases

Defects in signaling pathways are the root cause of many disorders. These malformations come in a wide variety of types, and their causes are also very diverse. Some of these flaws can be brought on by pathogenic organisms and viruses, many of which can obstruct signaling processes. Other illnesses are linked to malfunctions in the way that cell signaling pathways work. When thinking about how errors in signaling pathways might cause disease, the idea of signalosome remodeling is helpful. The signalosome may be conveniently divided into two types of defects: phenotypic remodeling and genotypic remodeling. The majority of significant illnesses that affect people, including high blood pressure, heart disease, diabetes, and many types of mental illness, appear to be caused by minute phenotypic changes in signaling pathways. Such phenotypic remodeling modifies cell behavior and subverts normal cellular processes, resulting in illness. There has not been much progress in creating efficient therapies since it has been challenging to definitively confirm this connection between signalosome remodeling and illness. The considerable redundancy included into cell signaling systems presents several potential for developing novel treatments for various disease conditions. One of the most important pathways, NF-κB, controls several aspects of innate and adaptive immune responses, is a key modulator of inflammatory reactions, and has been widely studied both from experimental and theoretical perspectives. NF-κB contributes to the control of inflammasomes and stimulates the expression of a number of pro-inflammatory genes, including those that produce cytokines and chemokines. Additionally, NF-κB is essential for controlling innate immune cells and inflammatory T cells' survival, activation, and differentiation. As a result, aberrant NF-κB activation plays a role in the pathogenesis of several inflammatory illnesses. The activation and function of NF-κB in relation to inflammatory illnesses was covered here, and the advancement of treatment approaches based on NF-κB inhibition will be highlighted. This review presents the temporal behavior of NF-κB and its potential relevance in different human diseases which will be helpful not only for theoretical but also for experimental perspectives.

Cells and material-based strategies for regenerative endodontics

<p class = "Abstract" style = "margin: 0 cm; line-height: 32px; font-size: 12 pt; font-family: "Times New Roman", serif; color: rgb(0, 0, 0); "><span lang = "EN-US">The carious process leads to inflammation of pulp tissue. Current care options include root canal treatment or apexification. These procedures, however, result in the loss of tooth vitality, sensitivity, and healing. Pulp capping and dental pulp regeneration are continually evolving techniques to regenerate pulp tissue, avoiding necrosis and loss of vitality. Many studies have successfully employed stem/progenitor cell populations, revascularization approaches, scaffolds or material-based strategies for pulp regeneration. Here we outline advantages and disadvantages of different methods and techniques which are currently being used in the field of regenerative endodontics. We also summarize recent findings on efficacious peptide-based materials which target the dental niche.<o:p></o:p></span></p>

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Pulp infection necessitates removal of necrotic, inflamed and infected tissue.

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Materials used clinically are inert (such as gutta percha, mineral trioxide aggregate).

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Recent developments in materials (angiogenic hydrogels, stem cell composites) have tuneable bioactivity.

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Dental pulp regeneration may now be possible through the use of bioactive systems, that guide regeneration.

Effects of microRNAs and long non-coding RNAs on chemotherapy response in glioma

Glioma is the most prevalent invasive primary tumor of the central nervous system. Glioma cells can spread and infiltrate into normal surrounding brain tissues. Despite the standard use of chemotherapy and radiotherapy after surgery in glioma patients, treatment resistance is still a problem, as the underlying mechanisms are still not fully understood. Non-coding RNAs are widely involved in tumor progression and treatment resistance mechanisms. In the present review, we discuss the pathways by which microRNAs and long non-coding RNAs can affect resistance to chemotherapy and radiotherapy, as well as offer potential therapeutic options for future glioma treatment.

Liquid biopsy: early and accurate diagnosis of brain tumor

Noninvasive examination is an emerging area in the field of neuro-oncology. Liquid biopsy captures the landscape of genomic alterations of brain tumors and revolutionizes the traditional diagnosis approaches. Rapidly changing sequencing technologies and more affordable prices put the screws on more application of liquid biopsy in clinical settings. In the past few years, extensive application of liquid biopsy has been seen throughout the whole diagnosis and treatment process of brain tumors, including early and accurate detection, characterization and dynamic monitoring. Here, we summarized and compared the most advanced techniques and target molecules or macrostructures related to brain tumor liquid biopsy. We further reviewed and emphasized recent progression in different clinical settings for brain tumors in blood and CSF. The preferred protocol, potential novel biomarkers and future development are discussed in the last part.

NIMCE: A Gene Regulatory Network Inference Approach Based on Multi Time Delays Causal Entropy

Gene regulatory networks (GRNs)are involved in various biological processes, such as cell cycle, differentiation and apoptosis. The existing large amount of expression data, especially the time-series expression data, provide a chance to infer GRNs by computational methods. These data can reveal the dynamics of gene expression and imply the regulatory relationships among genes. However, identify the indirect regulatory links is still a big challenge as most studies treat time points as independent observations, while ignoring the influences of time delays. In this study, we propose a GRN inference method based on information-theory measure, called NIMCE. NIMCE incorporates the transfer entropy to measure the regulatory links between each pair of genes, then applies the causation entropy to filter indirect relationships. In addition, NIMCE applies multi time delays to identify indirect regulatory relationships from candidate genes. Experiments on simulated and colorectal cancer data show NIMCE outperforms than other competing methods. All data and codes used in this study are publicly available at https://github.com/CSUBioGroup/NIMCE.

PMCE: efficient inference of expressive models of cancer evolution with high prognostic power

MOTIVATION

Driver (epi)genomic alterations underlie the positive selection of cancer subpopulations, which promotes drug resistance and relapse. Even though substantial heterogeneity is witnessed in most cancer types, mutation accumulation patterns can be regularly found and can be exploited to reconstruct predictive models of cancer evolution. Yet, available methods can not infer logical formulas connecting events to represent alternative evolutionary routes or convergent evolution.

RESULTS

We introduce PMCE, an expressive framework that leverages mutational profiles from cross-sectional sequencing data to infer probabilistic graphical models of cancer evolution including arbitrary logical formulas, and which outperforms the state-of-the-art in terms of accuracy and robustness to noise, on simulations. The application of PMCE to 7866 samples from the TCGA database allows us to identify a highly significant correlation between the predicted evolutionary paths and the overall survival in 7 tumor types, proving that our approach can effectively stratify cancer patients in reliable risk groups.

AVAILABILITY AND IMPLEMENTATION

PMCE is freely available at https://github.com/BIMIB-DISCo/PMCE, in addition to the code to replicate all the analyses presented in the manuscript.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DCMP: database of cancer mutant protein domains

Protein domains are functional and structural units of proteins. They are responsible for a particular function that contributes to protein's overall role. Because of this essential role, the majority of the genetic variants occur in the domains. In this study, the somatic mutations across 21 cancer types were mapped to the individual protein domains. To map the mutations to the domains, we employed the whole human proteome to predict the domains in each protein sequence and recognized about 149 668 domains. A novel Perl-API program was developed to convert the protein domain positions into genomic positions, and users can freely access them through GitHub. We determined the distribution of protein domains across 23 chromosomes with the help of these genomic positions. Interestingly, chromosome 19 has more number of protein domains in comparison with other chromosomes. Then, we mapped the cancer mutations to all the protein domains. Around 46-65% of mutations were mapped to their corresponding protein domains, and significantly mutated domains for all the cancer types were determined using the local false discovery ratio (locfdr). The chromosome positions for all the protein domains can be verified using the cross-reference ensemble database. Database URL: https://dcmp.vit.ac.in/.

Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization

Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.

Integrative analyses identified ion channel genes GJB2 and SCNN1B as prognostic biomarkers and therapeutic targets for lung adenocarcinoma

OBJECTIVES

Abnormal expressions of ion channel genes are associated with the occurrence and progression of tumors. At present, their roles in the carcinogenesis of lung adenocarcinoma (LUAD) are not clear.

MATERIALS AND METHODS

Differentially expressed (DE) genes in the tumorigenesis were identified from 328 ion channel genes in 102 LUAD and paired adjacent normal samples. Similar analyses were performed between 177 metastatic and 286 non-metastatic LUAD samples to identify DE ion channel genes in the progression of LUAD. Independent prognostic factors selected from DE ion channel genes were used to construct a prognostic model. Correlation analysis and drugs-drug targets interaction network were used to screen the potential drugs for LUAD patients stratified by GJB2 or SCNN1B.

RESULTS

Six ion channel genes (GJB2, CACNA1D, KCNQ1, SCNN1B, SCNN1G and TRPV6) were continuous differentially expressed in the tumorigenesis and progression of LUAD. The survival analysis in four datasets with 522 LUAD samples showed that GJB2 and SCNN1B were independent prognostic biomarkers. Patients with overexpression of GJB2 or underexpression of SCNN1B had shorter overall survival. Moreover, multi-omics analysis showed that hypomethylation of GJB2 and hypermethylation of SCNN1B in the promoter region may contribute to their aberrant expressions. KEGG enrichment analysis showed that the overexpressed genes in the group with high GJB2 or low SCNN1B were enriched in cancer-related pathways, while the underexpressed genes were enriched in metabolism-related pathways. The prognostic model with GJB2 and SCNN1B can stratify all LUAD patients into two groups with significantly different survival. Correlation analysis and drugs-drug targets interaction network suggested that GJB2 and SCNN1B expression might have indicative therapeutic values for LUAD patients. Finally, pan-cancer analysis in other eight cancer types showed that GJB2 and SCNN1B might be also potential prognostic factors for KIRC.

CONCLUSIONS

GJB2 and SCNN1B were identified as prognostic biomarkers and therapeutic targets for LUAD.

Pan-cancer characterization of long non-coding RNA and DNA methylation mediated transcriptional dysregulation

BACKGROUND

Disruption of DNA methylation (DNAm) is one of the key signatures of cancer, however, detailed mechanisms that alter the DNA methylome in cancer remain to be elucidated.

METHODS

Here we present a novel integrative analysis framework, called MeLncTRN (Methylation mediated LncRNA Transcriptional Regulatory Network), that integrates genome-wide transcriptome, DNA methylome and copy number variation profiles, to systematically identify the epigenetically-driven lncRNA-gene regulation circuits across 18 cancer types.

FINDING

We show that a significant fraction of the aberrant DNAm and gene expression landscape in cancer is associated with long noncoding RNAs (lncRNAs). We reveal distinct types of regulation between lncRNA modulators and target genes that are operative in either only specific cancers or across cancers. Functional studies identified a common theme of cancer hallmarks that lncRNA modulators may participate in. The coupled lncRNA gene interactions via DNAm also serve as markers for classifications of cancer subtypes with different prognoses.

INTERPRETATION

Our study reveals a vital layer of DNAm and associated expression regulation for many cancer-related genes and we also provide a valuable database resource for interrogating epigenetically mediated lncRNA-gene interactions in cancer.

FUNDING

National Natural Science Foundation of China [91959106, 31871255].

Multifaceted Roles of Long Non-coding RNAs in Head and Neck Cancer

The majority of RNA transcripts are non-coding RNA (ncRNA) transcripts with lengths exceeding 200 nucleotides that are not translated into protein. Unlike microRNAs (miRNAs), long ncRNAs (lncRNAs) are not confined to a single mechanism of action but have a large and diverse role in biological processes as they can function as transcription regulators, decoys, scaffolds, and enhancer RNAs. Currently, many lncRNA molecules are under investigation for their role in tumorigenesis, metastasis, and prognosis in different types of cancer. This review not only summarizes the characteristics and functions of lncRNAs but also discusses the therapeutic implications and applications of lncRNAs with roles associated with head and neck cancer. Our aim is to pinpoint the potential way to perturb specific lncRNAs for future therapeutic use.

Comprehensive characterization of a drug-resistance-related ceRNA network across 15 anti-cancer drug categories

Cancer is still a major health problem around the world. The treatment failure of cancer has largely been attributed to drug resistance. Competitive endogenous RNAs (ceRNAs) are involved in various biological processes and thus influence the drug sensitivity of cancers. However, a comprehensive characterization of drug-sensitivity-related ceRNAs has not yet been performed. In the present study, we constructed 15 ceRNA networks across 15 anti-cancer drug categories, involving 217 long noncoding RNAs (lncRNAs), 158 microRNAs (miRNAs), and 1,389 protein coding genes (PCGs). We found that these ceRNAs were involved in hallmark processes such as “self-sufficiency in growth signals,” “insensitivity to antigrowth signals,” and so on. We then identified an intersection ceRNA network (ICN) across the 15 anti-cancer drug categories. We further identified interactions between genes in the ICN and clinically actionable genes (CAGs) by analyzing the co-expressions, protein-protein interactions, and transcription factor-target gene interactions. We found that certain genes in the ICN are correlated with CAGs. Finally, we found that genes in the ICN were aberrantly expressed in tumors, and some were associated with patient survival time and cancer stage.

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BACKGROUND

Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions.

RESULTS

In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients' survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients' survival time.

CONCLUSION

The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients' survival by integrating multi-omics data and clinical factors.

Identification of Genes Universally Differentially Expressed in Gastric Cancer

Owing to the remarkable heterogeneity of gastric cancer (GC), population-level differentially expressed genes (DEGs) identified using case-control comparison cannot indicate the dysregulated frequency of each DEG in GC. In this work, first, the individual-level DEGs were identified for 1,090 GC tissues without paired normal tissues using the RankComp method. Second, we directly compared the gene expression in a cancer tissue to that in paired normal tissue to identify individual-level DEGs among 448 paired cancer-normal gastric tissues. We found 25 DEGs to be dysregulated in more than 90% of 1,090 GC tissues and also in more than 90% of 448 GC tissues with paired normal tissues. The 25 genes were defined as universal DEGs for GC. Then, we measured 24 paired cancer-normal gastric tissues by RNA-seq to validate them further. Among the universal DEGs, 4 upregulated genes (BGN, E2F3, PLAU, and SPP1) and 1 downregulated gene (UBL3) were found to be cancer genes already documented in the COSMIC or F-Census databases. By analyzing protein-protein interaction networks, we found 12 universally upregulated genes, and we found that their 284 direct neighbor genes were significantly enriched with cancer genes and key biological pathways related to cancer, such as the MAPK signaling pathway, cell cycle, and focal adhesion. The 13 universally downregulated genes and 16 direct neighbor genes were also significantly enriched with cancer genes and pathways related to gastric acid secretion. These universal DEGs may be of special importance to GC diagnosis and treatment targets, and they may make it easier to study the molecular mechanisms underlying GC.

Development of cancer prognostic signature based on pan-cancer proteomics

Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children’s brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model

Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and time-consuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell types which lack long-range contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. Moreover, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type–specific enhancer-mediated loops, and promoter–promoter interactions, with a modest feature input requirement. Comparable to experimentally generated H3K27ac HiChIP data, we found that LoopPredictor was able to identify functional enhancer loops. Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs were highly conserved. LoopPredictor enables the dissection of cell type–specific long-range gene regulation and can accelerate the identification of distal disease-associated risk variants.

Revealing Prognosis-Related Pathways at the Individual Level by a Comprehensive Analysis of Different Cancer Transcription Data

Identifying perturbed pathways at an individual level is important to discover the causes of cancer and develop individualized custom therapeutic strategies. Though prognostic gene lists have had success in prognosis prediction, using single genes that are related to the relevant system or specific network cannot fully reveal the process of tumorigenesis. We hypothesize that in individual samples, the disruption of transcription homeostasis can influence the occurrence, development, and metastasis of tumors and has implications for patient survival outcomes. Here, we introduced the individual-level pathway score, which can measure the correlation perturbation of the pathways in a single sample well. We applied this method to the expression data of 16 different cancer types from The Cancer Genome Atlas (TCGA) database. Our results indicate that different cancer types as well as their tumor-adjacent tissues can be clearly distinguished by the individual-level pathway score. Additionally, we found that there was strong heterogeneity among different cancer types and the percentage of perturbed pathways as well as the perturbation proportions of tumor samples in each pathway were significantly different. Finally, the prognosis-related pathways of different cancer types were obtained by survival analysis. We demonstrated that the individual-level pathway score (iPS) is capable of classifying cancer types and identifying some key prognosis-related pathways.

A qualitative classification signature for post-surgery 5-fluorouracil-based adjuvant chemoradiotherapy in gastric cancer

BACKGROUND AND PURPOSE

Currently, 5-fluorouracil (5-FU)-based adjuvant chemoradiotherapy (ACRT) is a preferred regimen for post-surgery gastric cancer (GC). However, the survival outcome of 5-FU-based ACRT varies greatly among different GC patients. Thus, it is necessary to classify which patients may benefit from 5-FU-based ACRT.

MATERIALS AND METHODS

We collected 577 GC and 84 adjacent normal samples for training and 675 GC samples for validation. Based on the within-sample relative expression orderings (REOs) of gene expression levels, reversal gene pairs were selected, and the pairs correlating with overall survival (OS) of GC patients receiving 5-FU-based ACRT were identified as candidates. Finally, an optimized set of candidate gene pairs was selected as a classification signature in training data and validated in validation data.

RESULTS

A signature consisting of 34 gene pairs was identified in training data and validated in three independent datasets. The classified low-risk group had better OS than the classified high-risk group. We also analyzed the recurrent free survival or disease free survival (RFS/DFS) of the validation datasets, and the similar results were shown. Furthermore, although the signature was identified based on the OS of GC patients receiving ACRT, it was not a prognostic signature for patients treated with surgery alone, but may be a potential signature for 5-FU-based chemotherapy alone.

CONCLUSIONS

The signature can accurately classify GC patients who may benefit from 5-FU-based ACRT, which could aid clinicians in tailoring more effective GC treatments.

Cancer classification of single-cell gene expression data by neural network

MOTIVATION

Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq).

RESULTS

We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples.

AVAILABILITY AND IMPLEMENTATION

Cancer classification by neural network.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.

Methylation-Based Signatures for Gastroesophageal Tumor Classification

Contention exists within the field of oncology with regards to gastroesophageal junction (GEJ) tumors, as in the past, they have been classified as gastric cancer, esophageal cancer, or a combination of both. Misclassifications of GEJ tumors ultimately influence treatment options, which may be rendered ineffective if treating for the wrong cancer attributes. It has been suggested that misclassification rates were as high as 45%, which is greater than reported for junctional cancer occurrences. Here, we aimed to use the methylation profiles of GEJ tumors to improve classifications of GEJ tumors. Four cohorts of DNA methylation profiles, containing ~27,000 (27k) methylation sites per sample, were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Tumor samples were assigned into discovery (nEC = 185, nGC = 395; EC, esophageal cancer; GC gastric cancer) and validation (nEC = 179, nGC = 369) sets. The optimized Multi-Survival Screening (MSS) algorithm was used to identify methylation biomarkers capable of distinguishing GEJ tumors. Three methylation signatures were identified: They were associated with protein binding, gene expression, and cellular component organization cellular processes, and achieved precision and recall rates of 94.7% and 99.2%, 97.6% and 96.8%, and 96.8% and 97.6%, respectively, in the validation dataset. Interestingly, the methylation sites of the signatures were very close (i.e., 170-270 base pairs) to their downstream transcription start sites (TSSs), suggesting that the methylations near TSSs play much more important roles in tumorigenesis. Here we presented the first set of methylation signatures with a higher predictive power for characterizing gastroesophageal tumors. Thus, they could improve the diagnosis and treatment of gastroesophageal tumors.

Artificial intelligence in drug combination therapy

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.

The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors

PURPOSE

There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest). The NETest represents a transcriptomic signature of neuroendocrine tumor (NETs) that captures tumor biology and disease activity. The data have direct clinical application in terms of identifying residual disease, disease progress and the efficacy of treatment. In this overview we assess the available published information on the metrics and clinical efficacy of the NETest.

MATERIAL AND METHODS

Published data on the NETest have been collated and analyzed to understand the clinical application of this multianalyte biomarker in NETs.

RESULTS

NETest assay has been validated as a standardized and reproducible clinical laboratory measurement. It is not affected by demographic characteristics, or acid suppressive medication. Clinical utility of the NETest has been documented in gastroenteropancreatic, bronchopulmonary NETs, in paragangliomas and pheochromocytomas. The test facilitates accurate diagnosis of a NET disease, and real-time monitoring of the disease status (stable/progressive disease). It predicts aggressive tumor behavior, identifies operative tumor resection, and efficacy of the medical treatment (e.g. somatostatin analogues), or peptide receptor radionuclide therapy (PRRT). NETest metrics and clinical applications out-perform standard biomarkers like chromogranin A.

CONCLUSIONS

The NETest exhibits clinically competent metrics as an effective biomarker for neuroendocrine tumors. Measurement of NET transcripts in blood is a significant advance in neuroendocrine tumor management and demonstrates that blood provides a viable source to identify and monitor tumor status.

CHG: A Systematically Integrated Database of Cancer Hallmark Genes

Background

The analysis of cancer diversity based on a logical framework of hallmarks has greatly improved our understanding of the occurrence, development and metastasis of various cancers.

Methods

We designed Cancer Hallmark Genes (CHG) database which focuses on integrating hallmark genes in a systematic, standard way and annotates the potential roles of the hallmark genes in cancer processes. Following the conceptual criteria description of hallmark function the keywords for each hallmark were manually selected from the literature. Candidate hallmark genes collected were derived from 301 pathways of KEGG database by Lucene and manually corrected.

Results

Based on the variation data, we finally identified the hallmark genes of various types of cancer and constructed CHG. And we also analyzed the relationships among hallmarks and potential characteristics and relationships of hallmark genes based on the topological structures of their networks. We manually confirm the hallmark gene identified by CHG based on literature and database. We also predicted the prognosis of breast cancer, glioblastoma multiforme and kidney papillary cell carcinoma patients based on CHG data.

Conclusions

In summary, CHG, which was constructed based on a hallmark feature set, provides a new perspective for analyzing the diversity and development of cancers.

NExUS: Bayesian simultaneous network estimation across unequal sample sizes

MOTIVATION

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.

RESULTS

We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.

AVAILABILITY AND IMPLEMENTATION

The NExUS source code is freely available for download at https://github.com/priyamdas2/NExUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

A Hallmark-Based Six-Gene Expression Signature to Assess Colorectal Cancer and Its Recurrence Risk

Purpose: As part of the effort to establish a general profile for solid tumors, the aim of this study was to develop a real-time polymerase chain reaction (RT-PCR)-based assay to assess colorectal cancer (CRC) and its recurrence risk utilizing the limited amounts of tissues available from biopsies through colonoscopy. Materials and Methods: Six candidate genes, reflecting the hallmarks of cancer cells, were identified by analyzing the gene expression profiles of primary invasive tumors in the public database. The expression of these genes in CRC and noncancerous colon tissues was quantified by RT-quantitative PCR. Classifiers were then generated to distinguish the tumors from the normal colon tissues, and to assess the risk of CRC recurrence based on the disease-free survival time, overall survival time, and metastatic status of the patients. Results: The expression profile of a five-gene panel was utilized to build a model that is capable of distinguishing CRC cancer tissues from noncancerous colorectal tissues (p < 0.0001). A classifier based on the expression signature of four genes, three of which were included in the five-gene panel, was then developed for assessing the tumor recurrence risk. This classifier could correctly identify those with a poor likelihood of survival (high risk of recurrence) >80% of time. There was a significant difference in disease-free survival time between patients in the low recurrence group and those in the high-risk group. Conclusion: The expression signatures of the six genes that reflect the genetic hallmarks of cancer cells could serve as a biomarker for identifying CRC and assessing the risk of recurrence with high sensitivity and specificity.

In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion

Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

Interval breast cancer is associated with other types of tumors

Breast cancer (BC) patients diagnosed between two screenings (interval cancers) are more likely than screen-detected patients to carry rare deleterious mutations in cancer genes potentially leading to increased risk for other non-breast cancer (non-BC) tumors. In this study, we include 14,846 women diagnosed with BC of which 1,772 are interval and 13,074 screen-detected. Compared to women with screen-detected cancers, interval breast cancer patients are more likely to have a non-BC tumor before (Odds ratio (OR): 1.43 [1.19–1.70], P = 9.4 x 10⁻⁵) and after (OR: 1.28 [1.14–1.44], P = 4.70 x 10⁻⁵) breast cancer diagnosis, are more likely to report a family history of non-BC tumors and have a lower genetic risk score based on common variants for non-BC tumors. In conclusion, interval breast cancer is associated with other tumors and common cancer variants are unlikely to be responsible for this association. These findings could have implications for future screening and prevention programs.

Interval cancer patients are more likely to carry rare gene mutations than screen-detected breast cancer patients. Here, the authors report that interval cancer patients are more likely cancer survivors and are at a greater risk of developing other non-breast tumors.

Predicting miRNA-disease association based on inductive matrix completion

Motivation

It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement.

Results

This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified.

Availability and implementation

The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Overexpression of BZW1 is an independent poor prognosis marker and its down-regulation suppresses lung adenocarcinoma metastasis

The basic leucine zipper and the W2 domain-containing protein 1 (BZW1) plays a key role in the cell cycle and transcriptionally control the histone H4 gene during G1/S phase. Since cellular proliferation rates are frequently dysregulated in human cancers, we identified the characteristics of BZW1 in cancer cells and analyzed its prognostic value in lung cancer patients. By searching public databases, we found that high BZW1 expression was significantly correlated with poor survival rate in non-small cell lung cancer (NSCLC), especially in lung adenocarcinoma. Similar trends were also shown in an array comprising NSCLC patient tissue. Knockdown of BZW1 inhibited cell metastatic ability, but did not affect the cell proliferation rate of NSCLC cells. From transcriptomics data mining, we found that coordination between BZW1 and EGFR overexpression was correlated with a worse outcome for lung cancer patients. In summary, BZW1 expression serves as an independent prognostic factor of NSCLC, especially in lung adenocarcinoma. Overexpression of BZW1 in lung cancer cells revealed a novel pathway underlying the induction of lung cancer metastasis.

Construction and analysis of a diabetic nephropathy related protein-protein interaction network reveals nine critical and functionally associated genes

Diabetic nephropathy (DN) is one of the common diabetic complications, but the mechanisms are still largely unknown. In this study, we constructed a DN related protein-protein interaction network (DNPPIN) on the basis of RNA-seq analysis of renal cortices of DN and normal mice, and the STRING database. We analyzed DNPPIN in detail revealing nine critical proteins which are central in DNPPIN, and contained in one network module which is functionally enriched in ribosome, nucleic acid binding and metabolic process. Overall, this study identified nine critical and functionally associated protein-coding genes concerning DN. These genes could be a starting point of future research towards the goal of elucidating the mechanisms of DN pathogenesis and progression.

Predicting human disease-associated circRNAs based on locality-constrained linear coding

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.

Every which way? On predicting tumor evolution using cancer progression models

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

Pan-Cancer and Single-Cell Modeling of Genomic Alterations Through Gene Expression

Cancer is a disease often characterized by the presence of multiple genomic alterations, which trigger altered transcriptional patterns and gene expression, which in turn sustain the processes of tumorigenesis, tumor progression, and tumor maintenance. The links between genomic alterations and gene expression profiles can be utilized as the basis to build specific molecular tumorigenic relationships. In this study, we perform pan-cancer predictions of the presence of single somatic mutations and copy number variations using machine learning approaches on gene expression profiles. We show that gene expression can be used to predict genomic alterations in every tumor type, where some alterations are more predictable than others. We propose gene aggregation as a tool to improve the accuracy of alteration prediction models from gene expression profiles. Ultimately, we show how this principle can be beneficial in intrinsically noisy datasets, such as those based on single-cell sequencing.

RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction

Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph could effectively recover information loss occurring in the normal graph methodology, thus exclusively illustrating multiple pair-wise associations. Integrating known microbe-disease associations in the Human Microbe-Disease Association Database (HMDAD) and the Gaussian interaction profile kernel similarity for microbes, random walk was then implemented for the constructed hypergraph. Consequently, RWHMDA performed optimally in predicting the underlying disease-associated microbes. More specifically, our model displayed AUC values of 0.8898 and 0.8524 in global and local leave-one-out cross-validation (LOOCV), respectively. Furthermore, three human diseases (asthma, Crohn's disease, and type 2 diabetes) were studied to further illustrate prediction performance. Moreover, 8, 10, and 8 of the 10 highest ranked microbes were confirmed through recent experimental or clinical studies. In conclusion, RWHMDA is expected to display promising potential to predict disease-microbe associations for follow-up experimental studies and facilitate the prevention, diagnosis, treatment, and prognosis of complex human diseases.