Whole-exome sequencing reveals novel cancer genes and actionable targets in biliary tract cancers in primary sclerosing cholangitis

BACKGROUND

People with primary sclerosing cholangitis (PSC) have a 20% lifetime risk of biliary tract cancer (BTC). Using whole-exome sequencing, we characterized genomic alterations in tissue samples from BTC with underlying PSC.

METHODS

We extracted DNA from formalin-fixed, paraffin-embedded tumor and paired nontumor tissue from 52 resection or biopsy specimens from patients with PSC and BTC and performed whole-exome sequencing. Following copy number analysis, variant calling, and filtering, putative PSC-BTC-associated genes were assessed by pathway analyses and annotated to targeted cancer therapies.

RESULTS

We identified 53 candidate cancer genes with a total of 123 nonsynonymous alterations passing filtering thresholds in 2 or more samples. Of the identified genes, 19% had not previously been implicated in BTC, including CNGA3, KRT28, and EFCAB5. Another subset comprised genes previously implicated in hepato-pancreato-biliary cancer, such as ARID2, ELF3, and PTPRD. Finally, we identified a subset of genes implicated in a wide range of cancers such as the tumor suppressor genes TP53, CDKN2A, SMAD4, and RNF43 and the oncogenes KRAS, ERBB2, and BRAF. Focal copy number variations were found in 51.9% of the samples. Alterations in potential actionable genes, including ERBB2, MDM2, and FGFR3 were identified and alterations in the RTK/RAS (p = 0.036), TP53 (p = 0.04), and PI3K (p = 0.043) pathways were significantly associated with reduced overall survival.

CONCLUSIONS

In this exome-wide characterization of PSC-associated BTC, we delineated both PSC-specific and universal cancer genes. Our findings provide opportunities for a better understanding of the development of BTC in PSC and could be used as a platform to develop personalized treatment approaches.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Mucosal melanomas of different anatomic sites share a common global DNA methylation profile with cutaneous melanoma but show location-dependent patterns of genetic and epigenetic alterations

Cutaneous, ocular, and mucosal melanomas are histologically indistinguishable tumors that are driven by a different spectrum of genetic alterations. With current methods, identification of the site of origin of a melanoma metastasis is challenging. DNA methylation profiling has shown promise for the identification of the site of tumor origin in various settings. Here we explore the DNA methylation landscape of melanomas from different sites and analyze if different melanoma origins can be distinguished by their epigenetic profile. We performed DNA methylation analysis, next generation DNA panel sequencing, and copy number analysis of 82 non-cutaneous and 25 cutaneous melanoma samples. We further analyzed eight normal melanocyte cell culture preparations. DNA methylation analysis separated uveal melanomas from melanomas of other primary sites. Mucosal, conjunctival, and cutaneous melanomas shared a common global DNA methylation profile. Still, we observed location-dependent DNA methylation differences in cancer-related genes, such as low frequencies of RARB (7/63) and CDKN2A promoter methylation (6/63) in mucosal melanomas, or a high frequency of APC promoter methylation in conjunctival melanomas (6/9). Furthermore, all investigated melanomas of the paranasal sinus showed loss of PTEN expression (9/9), mainly caused by promoter methylation. This was less frequently seen in melanomas of other sites (24/98). Copy number analysis revealed recurrent amplifications in mucosal melanomas, including chromosomes 4q, 5p, 11q and 12q. Most melanomas of the oral cavity showed gains of chromosome 5p with TERT amplification (8/10), while 11q amplifications were enriched in melanomas of the nasal cavity (7/16). In summary, mucosal, conjunctival, and cutaneous melanomas show a surprisingly similar global DNA methylation profile and identification of the site of origin by DNA methylation testing is likely not feasible. Still, our study demonstrates tumor location-dependent differences of promoter methylation frequencies in specific cancer-related genes together with tumor site-specific enrichment for specific chromosomal changes and genetic mutations. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Sex Differences in Cancer Genomes: Much Learned, More Unknown

Cancer is a leading cause of death worldwide. Sex influences cancer in a bewildering variety of ways. In some cancer types, it affects prevalence; in others, genomic profiles, response to treatment, or mortality. In some, sex seems to have little or no influence. How and when sex influences cancer initiation and progression remain a critical gap in our understanding of cancer, with direct relevance to precision medicine. Here, we note several factors that complicate our understanding of sex differences: representativeness of large cohorts, confounding with features such as ancestry, age, obesity, and variability in clinical presentation. We summarize the key resources available to study molecular sex differences and suggest some likely directions for improving our understanding of how patient sex influences cancer behavior.

Construction of a Pearson- and MIC-Based Co-expression Network to Identify Potential Cancer Genes

The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.

Identification of key genes in osteosarcoma - before and after CDK7 treatment

BACKGROUND

Osteosarcoma is one of the most common bone tumors, with a high degree of malignancy and a poor prognosis. Recent studies have shown that THZ2, a cyclin-dependent kinase 7 inhibitor, can exhibit strong antibone tumor effects in vivo and in vitro by inhibiting transcriptional activity. In this study, by screening the differentially expressed genes (DEGs) of osteosarcoma cells before and after THZ2 treatment, it provides new possible targets for the future targeted therapy of osteosarcoma.

METHODS

Download the gene expression profile of GSE134603 from the Gene Expression Omnibus database, and use the R software package "limma Geoquery" to screen DEGs. DAVID database was used for gene ontology analysis of DEGs. Use search tool for the retrieval of interacting genes online database and Cytoscape software to construct protein-protein interaction network. Use the "MCODE" plugin in Cytoscape to analyze key molecular complexes (module) of DEGs, and use the "Cluego" plugin to perform Kyoto Encyclopedia of Genes and Genomes enrichment analysis on module genes. The Hub gene is selected from the genes in DEGs that coexist in the top 30 Degree and the Kyoto Encyclopedia of Genes and Genomes pathway.

RESULTS

A total of 1033 DEGs were screened, including 800 up-regulated genes and 233 down-regulated genes. Gene ontology analysis showed that cell component is the main enrichment area of DEGs, mainly in the nucleus, cytoplasm, and nucleoplasm. In addition, in molecular function analysis, DEGs are mainly enriched in the process of protein binding. In biological process analysis, changes in DEGs can also be observed in transcription and regulation using DNA as a template. Twenty-nine module genes are enriched in the Ribosome biogenesis in eukaryotes pathway. Finally, 4 key genes are drawn: essential for mitotic growth 1, U3 SnoRNP protein 3 homolog, U3 small nucleolar RNA-associated protein 15 homolog, and WD repeat domain 3.

CONCLUSION

This study found that the 4 genes essential for mitotic growth 1, U3 SnoRNP protein 3 homolog, U3 small nucleolar RNA-associated protein 15 homolog, WD repeat domain 3, and the ribosome biogenesis in eukaryotes pathway play a very important role in the occurrence and development of osteosarcoma, and can become a new target for molecular targeted therapy of osteosarcoma in the future.

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the Cox model. This strategy is included a new learning technique, self-paced learning (SPL), and a new gene selection method, SCAD-Net penalty. The SPL method is adopted to aid to build a more accurate prediction with its built-in mechanism of learning from easy samples first and adaptively learning from hard samples. The SCAD-Net penalty has fixed the problem of the SCAD method without an inherent mechanism to fuse the prior graphical information. We combined the SPL with the SCAD-Net penalty to the Cox model (SSNC). The simulation shows that the SSNC outperforms the benchmark in terms of prediction and gene selection. The analysis of a large-scale experiment across several cancer datasets shows that the SSNC method not only results in higher prediction accuracies but also identifies markers that satisfactory stability across another validation dataset. The demo code for the proposed method is provided in supplemental file.

Targeted Deep Sequencing of Bladder Tumors Reveals Novel Associations between Cancer Gene Mutations and Mutational Signatures with Major Risk Factors

PURPOSE

Exome- and whole-genome sequencing of muscle-invasive bladder cancer has revealed important insights into the molecular landscape; however, there are few studies of non-muscle-invasive bladder cancer with detailed risk factor information.

EXPERIMENTAL DESIGN

We examined the relationship between smoking and other bladder cancer risk factors and somatic mutations and mutational signatures in bladder tumors. Targeted sequencing of frequently mutated genes in bladder cancer was conducted in 322 formalin-fixed paraffin-embedded bladder tumors from a population-based case-control study. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI), evaluating mutations and risk factors. We used SignatureEstimation to extract four known single base substitution mutational signatures and Poisson regression to calculate risk ratios (RR) and 95% CIs, evaluating signatures and risk factors.

RESULTS

Non-silent KDM6A mutations were more common in females than males (OR = 1.83; 95% CI, 1.05-3.19). There was striking heterogeneity in the relationship between smoking status and established single base substitution signatures: current smoking status was associated with greater ERCC2-Signature mutations compared with former (P = 0.024) and never smoking (RR = 1.40; 95% CI, 1.09-1.80; P = 0.008), former smoking was associated with greater APOBEC-Signature13 mutations (P = 0.05), and never smoking was associated with greater APOBEC-Signature2 mutations (RR = 1.54; 95% CI, 1.17-2.01; P = 0.002). There was evidence that smoking duration (the component most strongly associated with bladder cancer risk) was associated with ERCC2-Signature mutations and APOBEC-Signature13 mutations among current (P trend = 0.005) and former smokers (P = 0.0004), respectively.

CONCLUSIONS

These data quantify the contribution of bladder cancer risk factors to mutational burden and suggest different signature enrichments among never, former, and current smokers.

In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma

BACKGROUND

Changes in lipid metabolism pathways play a major role in colon carcinogenesis and development. Hence, we conducted a systematic analysis of lipid metabolism-related genes to explore new markers that predict the prognosis of colon adenocarcinoma (COAD).

METHODS

The non-negative Matrix Factorization (NMF) algorithm was applied to identify the molecular subtypes based on lipid metabolism-related genes. A weighted correlation network analysis (WCGNA) was used to identify co-expressed genes, and Lasso multivariate Cox analysis was performed to build a risk prognosis model. A timer database was used to analyze the immune infiltration of the gene signature and the GSCALite database was used for genome-wide analysis of the gene signature.

RESULTS

TCGA-COAD samples were divided into 3 subtypes based on lipid metabolism-related genes. 2739 genes were identified by WGCNA analysis. Finally, an 8-gene signature (RTN2, FYN, HEYL, FAM69A, FBXL5, HMGN2, LGALS4, STOX1) was constructed that demonstrated good robustness in different datasets, as well as an independent risk factor for colon cancer patients' prognosis. In addition, our model's predictive efficacy overall was higher than that of the other published models, and the 8 genes' expression analysis indicated that RTN2, HEYL, and STOX1 were all expressed highly significantly in COAD, while FAM69A, FBXL5, LGALS4, FYN and HMGN2 were expressed significantly poorly in cancer tissues, which was confirmed in immunohistochemistry. The 8 genes were expressed significantly differently in COAD immune subtypes and correlated with clinical variables. Genome-wide analysis revealed that the STOX1 mutation frequency was the highest, and genome methylation influenced HEYL, FAM69A, and STOX1 gene expression significantly; further, the expression of HEYL and FBXL5 was correlated positively with Copy number variation (CNV) and was regulated significantly by CNV in most cancers. FBXL5 was correlated significantly with austocystin d and bafilomycin and played an important role in anti-tumor and immunotherapy. The HEYL, FYN, FAM69A, and RTN2 genes' expression was associated with the EMT pathway's activation, while LGALS4 and STOX1 were associated significantly with the EMT pathway's inhibition.

CONCLUSION

This study constructed an 8-gene signature as a novel marker to predict colon cancer patients' survival.

Translational adaptation of human viruses to the tissues they infect

Viruses need to hijack the translational machinery of the host cell for a productive infection to happen. However, given the dynamic landscape of tRNA pools among tissues, it is unclear whether different viruses infecting different tissues have adapted their codon usage toward their tropism. Here, we collect the coding sequences of 502 human-infecting viruses and determine that tropism explains changes in codon usage. Using the tRNA abundances across 23 human tissues from The Cancer Genome Atlas (TCGA), we build an in silico model of translational efficiency that validates the correspondence of the viral codon usage with the translational machinery of their tropism. For instance, we detect that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is specifically adapted to the upper respiratory tract and alveoli. Furthermore, this correspondence is specifically defined in early viral proteins. The observed tissue-specific translational efficiency could be useful for the development of antiviral therapies and vaccines.

Molecular characterization of long-term survivors of hepatocellular carcinoma

Hepatocellular carcinoma is one of the most fatal cancers, and the majority of patients die within three years. However, a small proportion of patients overcome this fatal disease and survive for more than five years. To determine the molecular characteristics of long-term survivors (survival ≥ 5 years), we analyzed the genomic and clinical data of hepatocellular carcinoma patients from The Cancer Genome Atlas and the International Cancer Genome Consortium databases, and identified molecular features that were strongly associated with the patients' prognosis. Genes involved in the cell cycle were expressed at lower levels in tumor tissues from long-term survivors than those from short-term survivors (survival ≤ 1 years). High levels of positive regulators of the G₁/S cell cycle transition (cyclin-dependent kinase 2 [CDK2], CDK4, Cyclin E2 [CCNE2], E2F1, E2F2) were potential markers of poor prognosis. Hepatocellular carcinoma patients with TP53 mutations were mainly belonged to the short-term survivor group. Abemaciclib, an FDA-approved selective inhibitor of CDK4/6, inhibited the cell proliferation and tumor growth of hepatocellular carcinoma cells in vitro and in vivo. Thus, high G₁/S transition-related gene levels and TP53 mutations are promising diagnostic biomarkers for short-term survivals, and abemaciclib may be a potential targeted drug for hepatocellular carcinoma.

An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data

Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model) can avoid it, they need strict assumptions. This undermines the very nature of things, which is very detrimental to the study of practical problems. The method proposed in this paper can apply well to the censored data, but does not need to make any additional assumptions about the original problem. It can be said that it breaks through the above two kinds of major limitations. The algorithm is a kind of inductive transfer learning method, which can fully obtain the information in the censored data, using domain-specific information implicit in each feature to enhance the generalization capability of the model. We also used two common performance metrics as criteria to judge the predictive performance differences between the models in this article and those of other mainstream models. The results show that the model proposed in this paper is 10 ∼ 15 percent higher than other mainstream models, which proves that our multi-task learning model has a great advantage in the survival analysis of cancer genes.

Comprehensive analysis of cancer breakpoints reveals signatures of genetic and epigenetic contribution to cancer genome rearrangements

Understanding mechanisms of cancer breakpoint mutagenesis is a difficult task and predictive models of cancer breakpoint formation have to this time failed to achieve even moderate predictive power. Here we take advantage of a machine learning approach that can gather important features from big data and quantify contribution of different factors. We performed comprehensive analysis of almost 630,000 cancer breakpoints and quantified the contribution of genomic and epigenomic features-non-B DNA structures, chromatin organization, transcription factor binding sites and epigenetic markers. The results showed that transcription and formation of non-B DNA structures are two major processes responsible for cancer genome fragility. Epigenetic factors, such as chromatin organization in TADs, open/closed regions, DNA methylation, histone marks are less informative but do make their contribution. As a general trend, individual features inside the groups show a relatively high contribution of G-quadruplexes and repeats and CTCF, GABPA, RXRA, SP1, MAX and NR2F2 transcription factors. Overall, the cancer breakpoint landscape can be represented by well-predicted hotspots and poorly predicted individual breakpoints scattered across genomes. We demonstrated that hotspot mutagenesis has genomic and epigenomic factors, and not all individual cancer breakpoints are just random noise but have a definite mutation signature. Besides we found a long-range action of some features on breakpoint mutagenesis. Combining omics data, cancer-specific individual feature importance and adding the distant to local features, predictive models for cancer breakpoint formation achieved 70-90% ROC AUC for different cancer types; however precision remained low at 2% and the recall did not exceed 50%. On the one hand, the power of models strongly correlates with the size of available cancer breakpoint and epigenomic data, and on the other hand finding strong determinants of cancer breakpoint formation still remains a challenge. The strength of predictive signals of each group and of each feature inside a group can be converted into cancer-specific breakpoint mutation signatures. Overall our results add to the understanding of cancer genome rearrangement processes.

Identification of key biomarkers and functional pathways in osteosarcomas with lung metastasis: Evidence from bioinformatics analysis

BACKGROUND

In osteosarcoma, the lung is the most common metastatic organ. Intensive work has been made to illuminate the pathogeny, but the specific metastatic mechanism remains unclear. Thus, we conducted the study to seek to find the key genes and critical functional pathways associated with progression and treatment in lung metastasis originating from osteosarcoma.

METHODS

Two independent datasets (GSE14359 and GSE85537) were screened out from the Gene Expression Omnibus (GEO) database and the overlapping differentially expressed genes (DEGs) were identified using GEO2R online platform. Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted using DAVID. Meanwhile, the protein-protein interaction (PPI) network constructed by STRING was visualized using Cytoscape. Afterwards, the key module and hub genes were extracted from the PPI network using the MCODE and cytoHubba plugin. Moreover, the raw data obtained from GSE73166 and GSE21257 were applied to verify the expression differences and conduct the survival analyses of hub genes, respectively. Finally, the interaction network of miRNAs and hub genes constructed by ENCORI was visualized using Cytoscape.

RESULTS

A total of 364 DEGs were identified, comprising 96 downregulated genes and 268 upregulated genes, which were mainly involved in cancer-associated pathways, adherens junction, ECM-receptor interaction, focal adhesion, MAPK signaling pathway. Subsequently, 10 hub genes were obtained and survival analysis demonstrated SKP2 and ASPM were closely related to poor prognosis of patients with osteosarcoma. Finally, hsa-miR-340-5p, has-miR-495-3p, and hsa-miR-96-5p were found to be most closely associated with these hub genes according to the interaction network of miRNAs and hub genes.

CONCLUSION

The key genes and functional pathways identified in the study may contribute to understanding the molecular mechanisms involved in the carcinogenesis and progression of lung metastasis originating from osteosarcoma, and provide potential diagnostic and therapeutic targets.

Screening and identification of key genes between liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) by bioinformatic analysis

BACKGROUND

Liver hepatocellular carcinoma (LIHC) and cholangiocarcinoma (CHOL) are common primary liver cancers worldwide. Liver stem cells have biopotential to differentiate into either hepatocytes and cholangiocytes, the phenotypic overlap between LIHC and CHOL has been acceptable as a continuous liver cancer spectrum. However, few studies directly investigated the underlying molecular mechanisms between LIHC and CHOL.

METHOD

To identify the candidate genes between LIHC and CHOL, three data series including GSE31370, GSE15765 and GSE40367 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape.

RESULTS

A total of 171 DEGs were identified, consisting of 49 downregulated genes and 122 upregulated genes. Compared with CHOL, the enriched functions of the DEGs mainly included steroid metabolic process, acute inflammatory response, coagulation. Meanwhile, the pathway of KEGG enrichment analyses showed that the upregulated gene(s) were mainly enriched complement and coagulation cascades, cholesterol metabolism and PPAR signaling pathway, while the downregulated gene(s) were mainly enriched in ECM-receptor interaction, focal adhesion, bile secretion. Similarly, the most significant module was identified and biological process analysis revealed that these genes were mainly enriched in regulation of blood coagulation, acute inflammatory response, complement and coagulation cascades. Finally, two (ITIH2 and APOA2) of 10 hub genes had been screened out to help differential diagnosis.

CONCLUSION

171 DEGs and two (ITIH2 and APOA2) of 10 hub genes identified in the present study help us understand the different molecular mechanisms between LIHC and CHOL, and provide candidate targets for differential diagnosis.

Screening of Methylation Gene Sites as Prognostic Signature in Lung Adenocarcinoma

PURPOSE

Most lung adenocarcinoma (LUAD) patients are diagnosed at the advanced stage and have poor prognosis. DNA methylation plays an important role in the prognosis prediction of cancers. The objective of this study was to identify new DNA methylation sites as biomarkers for LUAD prognosis.

MATERIALS AND METHODS

We downloaded DNA methylation data from The Cancer Genome Atlas data portal. Cox proportional hazard regression model and random survival forest algorithm were applied to identify the DNA-methylation sites. Methylation of sites were validated in the Gene Expression Omnibus cohorts. Function annotation were done to explore the biological function of DNA methylated sites signature.

RESULTS

Six DNA methylation sites were identified as prognosis signature. The signature yielded acceptable discrimination between the high-risk group and low-risk group. The discrimination effect of this DNA methylation signature for the OS was obvious, with a median OS of 21.89 months vs. 17.74 months for high-risk vs. low-risk groups. This prognostic prediction model was validated by the test group and GEO dataset. The predictive survival value was higher for the prognostic prediction model than that for the tumor node metastasis stage. Adjuvant hemotherapy could not affect the prediction of the signature. Functional analysis indicated that these signature genes were involved in protein binding and cytoplasm.

CONCLUSION

We identified the prognostic signature for LUAD by combining six DNA methylation sites. This could service as potential robust and specificity signature in the prognosis prediction of LUAD.

Assessing the prognostic value of stemness-related genes in breast cancer patients

Breast cancer (BC) is currently one of the deadliest tumors worldwide. Cancer stem cells (CSCs) are a small group of tumor cells with self-renewal and differentiation abilities and high treatment resistance. One of the reasons for treatment failures is the inability to completely eliminate tumor stem cells. By using the edgeR package, we identified stemness-related differentially expressed genes in GSE69280. Via Lasso-penalized Cox regression analysis and univariate Cox regression analysis, survival genes were screened out to construct a prognostic model. Via nomograms and ROC curves, we verified the accuracy of the prognostic model. We selected 4 genes (PSMB9, CXCL13, NPR3, and CDKN2C) to establish a prognostic model from TCGA data and a validation model from GSE24450 data. We found that the low-risk score group had better OS than the high-risk score group, whether using TCGA or GSE24450 data. A prognostic model including four stemness-related genes was constructed in our study to determine targets of breast cancer stem cells (BCSCs) and improve the treatment effect.

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD). In CapsNetMMD, we make use of known breast cancer-related genes to transform the issue of gene identification into the issue of supervised classification. The features of genes are generated through comprehensive integration of multi-omics data, e.g., mRNA expression, z scores for mRNA expression, DNA methylation, and two forms of DNA copy-number alterations (CNAs). By modeling features based on the capsule network, we identify breast cancer-related genes with a significantly better performance than other existing machine learning methods. The predicted genes with prognostic values play potential important roles in breast cancer and may serve as candidates for biologists and medical scientists in the future studies of biomarkers.

CONDEL: Detecting Copy Number Variation and Genotyping Deletion Zygosity from Single Tumor Samples Using Sequence Data

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage.

The mutREAD method detects mutational signatures from low quantities of cancer DNA

Mutational processes acting on cancer genomes can be traced by investigating mutational signatures. Because high sequencing costs limit current studies to small numbers of good-quality samples, we propose a robust, cost- and time-effective method, called mutREAD, to detect mutational signatures from small quantities of DNA, including degraded samples. We show that mutREAD recapitulates mutational signatures identified by whole genome sequencing, and will ultimately allow the study of mutational signatures in larger cohorts and, by compatibility with formalin-fixed paraffin-embedded samples, in clinical settings.

An Entropy-Based Method for Identifying Mutual Exclusive Driver Genes in Cancer

Cancer in essence is a complex genomic alteration disease which is caused by the somatic mutations during the lifetime. According to previous researches, the first step to overcome cancer is to identify driver genes which can promote carcinogenesis. However, it is still a big challenge to precisely and efficiently extract the cancer related driver genes because the nature of cancer is heterogeneous and there exists tremendously irrelevant passenger mutations which have no function impact on the cancer's development. In this work, we proposed a novel entropy-based method namely EntroRank to identify driver genes by integrating the subcellular localization information and mutual exclusive of variation frequency into the network. EntroRank can take into full consideration different properties of driver genes. Considering the modularity of driver genes, the mutated genes in the network were first clustered into different subgroups according to their located compartments. After that, the structural entropy of the gene in the subgroup was employed to measure its indispensability. Considering mutual exclusive property between driver genes in the modules, relative entropy was utilized to measure the degree of mutual exclusive between two mutated genes in terms of their variation frequency. We applied our method to three different cancers including lung, prostate, and breast cancer. The results show our method not only detect the well-known important drivers but also prioritiz the rare unknown driver genes. Besides, EntroRank can identify driver genes having mutual exclusive property. Compared with other existing methods, our method achieves a better performance for most of cancer types in terms of Precision, Recall, and Fscore.

LncRNA-PCAT1 maintains characteristics of dermal papilla cells and promotes hair follicle regeneration by regulating miR-329/Wnt10b axis

BACKGROUND

The failure of hair follicle regeneration is the major cause of alopecia, which is a highly prevalent disease worldwide. Dermal papilla (DP) cells play important role in the regulation of hair follicle regeneration. However, the molecular mechanism of how dermal papilla cells direct follicle regeneration is still to be elucidated.

METHODS

In vitro DP 3D culturing and in vivo nude mice DP sphere implanted models were used to examine the molecular regulation of DP cells and follicle regeneration. qRT-PCR and Western blotting were used to detect gene and protein expression, respectively. Immunofluorescence was used to detect the expression level of Wnt10b, Ki-67 and β-catenin. Luciferase assay was used to examine the relationship among PCAT1, miR-329 and Wnt10b. ALP activity was measured by ELISA. H&E staining was used to measure follicle growth in skin tissues.

RESULTS

Up-regulation of PCAT1 and Wnt10b, however, down-regulation of miR-329 were found in the in vitro 3D dermal papilla. Bioinformatics analysis and luciferase assays demonstrated that PCAT1 promoted Wnt10b expression by sponging miR-329. Knockdown of PCAT1 suppressed the proliferation and activity, as well as ALP and other DP markers of DP cells by targeting miR-329. Knockdown of PCAT1 regulated miR-329/Wnt10b axis to attenuate β-catenin expression and nucleus translocation to inhibit Wnt/β-catenin signaling. Furthermore, knockdown of PCAT1 suppressed DP sphere induced follicle regeneration and hair growth in nude mice.

CONCLUSION

PCAT1 maintains characteristics of DP cells by targeting miR-329 to activating Wnt/β-catenin signaling pathway, thereby promoting hair follicle regeneration.

Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Extreme intratumour heterogeneity and driver evolution in mismatch repair deficient gastro-oesophageal cancer

Mismatch repair deficient (dMMR) gastro-oesophageal adenocarcinomas (GOAs) show better outcomes than their MMR-proficient counterparts and high immunotherapy sensitivity. The hypermutator-phenotype of dMMR tumours theoretically enables high evolvability but their evolution has not been investigated. Here we apply multi-region exome sequencing (MSeq) to four treatment-naive dMMR GOAs. This reveals extreme intratumour heterogeneity (ITH), exceeding ITH in other cancer types >20-fold, but also long phylogenetic trunks which may explain the exquisite immunotherapy sensitivity of dMMR tumours. Subclonal driver mutations are common and parallel evolution occurs in RAS, PIK3CA, SWI/SNF-complex genes and in immune evasion regulators. MSeq data and evolution analysis of single region-data from 64 MSI GOAs show that chromosome 8 gains are early genetic events and that the hypermutator-phenotype remains active during progression. MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types reveals mutation rates and their timing to determine phylogenetic tree morphologies.