Copy Number Variation Data Research Articles

Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data.

Osteosarcoma is the most common primary malignancy in the musculoskeletal system. It is reported that copy number variation- (CNV-) derived lncRNAs contribute to the progression of osteosarcoma. However, whether CNV-derived lncRNAs affect the prognosis of osteosarcoma remains unclear. Here, we obtained osteosarcoma-related CNV data and gene expression profiles from The Cancer Genome Atlas (TCGA) database. CNV landscape analysis indicated that copy number amplification of lncRNAs was more frequent than deletion in osteosarcoma samples. Thirty-four CNV-lncRNAs with DNA-CNV frequencies greater than 30% and their corresponding 294 mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses revealed that these mRNAs were mainly enriched in olfaction, olfactory receptor activity, and olfactory transduction processes. Furthermore, we predicted that a total of 23 genes were cis-regulated by 16 CNV-lncRNAs, while 30 transcription factors (TFs) were trans-regulated by 5 CNV-lncRNAs. Through t-tests, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO), we constructed a CNV-related risk model including 3 lncRNAs (AC129492.1, PSMB1, and AC037459.4). The Kaplan-Meier (K-M) curves indicated that patients with high-risk scores showed poor prognoses. The areas under the receiver operating characteristic (ROC) curves (AUC) for predicting 3-, 5-, and 7-year overall survival (OS) were greater than 0.7, showing a satisfactory predictive efficiency. Gene set enrichment analysis (GSEA) revealed that the prognostic signature was intimately linked to skeletal system development, immune regulation, and inflammatory response. Collectively, our study developed a novel 3-CNV-lncRNA prognostic signature that would provide theoretical guidance for the clinical prognostic management of osteosarcoma.

Copy Number Variation Analysis of Euploid Pregnancy Loss.

Objectives: Copy number variant (CNV) is believed to be the potential genetic cause of pregnancy loss. However, CNVs less than 3 Mb in euploid products of conceptions (POCs) remain largely unexplored. The aim of this study was to investigate the features of CNVs less than 3 Mb in POCs and their potential clinical significance in pregnancy loss/fetal death. Methods: CNV data were extracted from a cohort in our institution and 19 peer-reviewed publications, and only those CNVs less than 3 Mb detected in euploid pregnancy loss/fetal death were included. We conducted a CNV map to analyze the distribution of CNVs in chromosomes using R packages karyoploteR_1.10.5. Gene names and annotated gene types covered by those CNVs were mined from the human Release 19 reference genome file and GENECODE database. We assessed the expression patterns and the consequences of murine knock-out of those genes using TiGER and Mouse Genome Informatics (MGI) databases. Functional enrichment and pathway analysis for genes in CNVs were performed using clusterProfiler V3.12.0. Result: Breakpoints of 564 CNVs less than 3 Mb were obtained from 442 euploid POCs, with 349 gains and 185 losses. The CNV map showed that CNVs were distributed in all chromosomes, with the highest frequency detected in chromosome 22 and the lowest frequency in chromosome Y, and CNVs showed a higher density in the pericentromeric and sub-telomeric regions. A total of 5,414 genes mined from the CNV regions (CNVRs), Gene Ontology (GO), and pathway analysis showed that the genes were significantly enriched in multiple terms, especially in sensory perception, membrane region, and tight junction. A total of 995 protein-coding genes have been reported to present mammalian phenotypes in MGI, and 276 of them lead to embryonic lethality or abnormal embryo/placenta in knock-out mouse models. CNV located at 19p13.3 was the most common CNV of all POCs. Conclusion: CNVs less than 3 Mb in euploid POCs distribute unevenly in all chromosomes, and a higher density was seen in the pericentromeric and sub-telomeric regions. The genes in those CNVRs are significantly enriched in biological processes and pathways that are important to embryonic/fetal development. CNV in 19p13.3 and the variations of ARID3A and FSTL3 might contribute to pregnancy loss.

Comprehensive Molecular Analyses of a TNF Family-Based Gene Signature as a Potentially Novel Prognostic Biomarker for Cervical Cancer.

BackgroundIncreasing evidence suggests that tumour necrosis factor (TNF) family genes play important roles in cervical cancer (CC). However, whether TNF family genes can be used as prognostic biomarkers of CC and the molecular mechanisms of TNF family genes remain unclear.MethodsA total of 306 CC and 13 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. We identified differentially expressed TNF family genes between CC and normal samples and subjected them to univariate Cox regression analysis for selecting prognostic TNF family genes. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to screen genes to establish a TNF family gene signature. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the TNF family gene signature. Finally, methylation and copy number variation data of CC were used to analyse the potential molecular mechanisms of TNF family genes.ResultsA total of 26 differentially expressed TNF family genes were identified between the CC and normal samples. Next, a TNF family gene signature, including CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 was constructed based on univariate Cox, LASSO, and multivariate Cox regression analyses. The TNF family gene signature was related to age, pathological stages M and N, and could predict patient survival independently of clinical factors. Moreover, KEGG enrichment analysis suggested that the TNF family gene signature was mainly involved in the TGF-β signaling pathway, and the TNF family gene signature could affect the immunotherapy response. Finally, we confirmed that the mRNA expressions of CD27, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 were upregulated in CC, while that of EDA was downregulated. The mRNA expressions of CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be influenced by gene methylation and copy number variation.ConclusionOur study is the first to demonstrate that CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be used as prognostic biomarkers of CC and are associated with the immunotherapy response of CC.

Identification of non-small-cell lung cancer subtypes by unsupervised clustering of CT image features with distinct prognoses and gene pathway activities

ObjectiveIntra-tumour heterogeneity exists in non-small-cell lung cancer (NSCLC). Medical imaging can provide a comprehensive and detailed description of the tumour. Moreover, quantitative features can reflect the potentially valuable information of tumours that is neglected by visual diagnosis. To this end, we identified imaging subtypes of NSCLC by computed tomography (CT) images. MethodsWe analysed CT image data from a single-centre discovery cohort (n = 239), an internal validation cohort (n = 80) and an independent multi-centre external validation cohort (n = 63). Quantitative image features were extracted to identify intra-tumour heterogeneity. An unsupervised consensus clustering algorithm was used to identify and validate robust imaging subtypes. Then, we assessed the clinical relevance and prognostic value of these subtypes. Furthermore, differential gene expression profiles with copy number variation (CNV) data were used to identify the underlying biological patterns associated with subtypes and to map molecular activation patterns in dysregulated pathways. ResultsThree distinct imaging subtypes, spherical tumour clusters, irregular large tumour clusters and hollow tumour clusters, were identified and validated in the discovery and validation cohorts. Imaging subtypes showed significant survival differences in all cohorts. Moreover, each imaging subtype had a specific activated pattern in dysregulated pathways that can be targeted. ConclusionThe imaging subtypes provide complimentary prognostic value to establish non-invasive histopathological subtypes with molecular signatures and may help stratify NSCLC patients for targeted diagnosis and treatment. SignificanceThis study identified three imaging NSCLC subtypes that implied different prognosis and dysregulated pathways. These results may contribute to personalized diagnosis and treatment for NSCLC patients.

Classifying the multi-omics data of gastric cancer using a deep feature selection method

Gastric cancer has the highest incidence among all types of malignant tumors. The rapid development of high-throughput gene technology has greatly promoted people’s understanding of gastric cancer at the molecular level. However, there is a lack of information in single omics data, so dimensionality reduction is an effective method to overcome the dimensionality disaster of omics data. omics data has the characteristics of being multivariate and high-dimensional, which affects the efficiency of classification. Therefore, dimensionality reduction is an effective method to overcome the dimensionality disaster of omics data. However, neural network learning algorithm is seldom used to improve classification accuracy when feature selection of multi-omics data is carried out, therefore, in this study, a random forest deep feature selection (RDFS) algorithm was proposed. By integrating gene expression (Exp) data and copy number variation (CNV) data, the dimensions of multi-omics data were reduced and improve the classification accuracy by using a random forest and deep neural network. The results showed that the accuracy and area under the curve (AUC) of multi-omics data were better than that of single-omics data under the RDFS algorithm. With other feature selection algorithms, RDFS also had a higher prediction accuracy and AUC. We also validated the effect of feature selection on RDFS. Finally, survival analysis was used to evaluate the important genes identified during feature selection and to obtain enrichment gene ontology (GO) terms and biological pathways for these genes.

CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing

BackgroundAlthough both copy number variations (CNVs) and single nucleotide variations (SNVs) detected by single-cell RNA sequencing (scRNA-seq) are used to study intratumor heterogeneity and detect clonal groups, a software that integrates these two types of data in the same cells is unavailable.ResultsWe developed Clonal Architecture with Integration of SNV and CNV (CAISC), an R package for scRNA-seq data analysis that clusters single cells into distinct subclones by integrating CNV and SNV genotype matrices using an entropy weighted approach. The performance of CAISC was tested on simulation data and four real datasets, which confirmed its high accuracy in sub-clonal identification and assignment, including subclones which cannot be identified using one type of data alone. Furthermore, integration of SNV and CNV allowed for accurate examination of expression changes between subclones, as demonstrated by the results from trisomy 8 clones of the myelodysplastic syndromes (MDS) dataset.ConclusionsCAISC is a powerful tool for integration of CNV and SNV data from scRNA-seq to identify clonal clusters with better accuracy than obtained from a single type of data. CAISC allows users to interactively examine clonal assignments.

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI)

BackgroundAlthough copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of their clinical significance requires a thorough literature review. This process can be extremely time-consuming and may delay disease diagnosis. Therefore, we have developed CNV Extraction, Transformation, and Loading Artificial Intelligence (CNV-ETLAI), an innovative tool that allows experts to classify and interpret CNVs accurately and efficiently. MethodsWe combined text, table, and image processing algorithms to develop an artificial intelligence platform that automatically extracts, transforms, and organizes CNV information into a database. To validate CNV-ETLAI, we compared its performance to ground truth datasets labeled by a human expert. In addition, we analyzed the CNV data, which was collected using CNV-ETLAI via a crowdsourcing approach. ResultsIn comparison to a human expert, CNV-ETLAI improved CNV detection accuracy by 4% and performed the analysis 60 times faster. This performance can improve even further with upscaling of the CNV-ETLAI database as usage increases. 5,800 CNVs from 2,313 journal articles were collected. Total CNV frequency for the whole chromosome was highest for chromosome X, whereas CNV frequency per 1 Mb of genomic length was highest for chromosome 22. ConclusionsWe have developed, tested, and shared CNV-ETLAI for research and clinical purposes (https://lmic.mgh.harvard.edu/CNV-ETLAI). Use of CNV-ETLAI is expected to ease and accelerate diagnostic classification and interpretation of CNVs.

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes.

Based on alterations in gene expression associated with the production of glycolysis and cholesterol, this research classified glioma into prognostic metabolic subgroups. In this study, data from the CGGA325 and The Cancer Genome Atlas (TCGA) datasets were utilized to extract single nucleotide variants (SNVs), RNA-seq expression data, copy number variation data, short insertions and deletions (InDel) mutation data, and clinical follow-up information from glioma patients. Glioma metabolic subtypes were classified using the ConsensusClusterPlus algorithm. This study determined four metabolic subgroups (glycolytic, cholesterogenic, quiescent, and mixed). Cholesterogenic patients had a higher survival chance. Genome-wide investigation revealed that inappropriate amplification of MYC and TERT was associated with improper cholesterol anabolic metabolism. In glioma metabolic subtypes, the mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) presented deletion and amplification, respectively. Differentially upregulated genes in the glycolysis group were related to pathways, including IL-17, HIF-1, and TNF signaling pathways and carbon metabolism. Downregulated genes in the glycolysis group were enriched in terpenoid backbone biosynthesis, nitrogen metabolism, butanoate metabolism, and fatty acid metabolism pathway. Cox analysis of univariate and multivariate survival showed that risks of glycolysis subtypes were significantly higher than other subtypes. Those results were validated in the CGGA325 dataset. The current findings greatly contribute to a comprehensive understanding of glioma and personalized treatment.

Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations

BackgroundCervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis.MethodsDifferentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups.ResultsThe study developed a 5-gene prognostic model that could accurately classify cervical cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer.ConclusionsThe 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.

Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma.

Background: DNA methylation (MET)–mediated transcriptomic disturbance and copy number variations (CNVs) exert a significant influence in stimulating the heterogeneous progression of stomach adenocarcinoma (STAD). Nevertheless, the relation of DNA MET with CNVs, together with its impact on tumor occurrence, is still unclear. Methods: The messenger RNA (mRNA) expression (EXP) profiles, DNA MET, and DNA copy numbers, together with STAD mutation data, were collected from the TCGA official data portal. We employed circular binary segmentation algorithm in “DNAcopy.” library of R package for mapping DNA CNV data at genetic level for all samples based on the segmented CNV data. Stable clusters of samples were recognized using negative matrix factorization cluster analysis based on 50 iterations and the “brunet” method using the MET-correlated (METcor) and CNV-correlated (CNVcor) genes. The R package “iCluster” method was utilized to comprehensively analyze the EXP, MET, and DNA CNV profiles. Results: A total of 313 STAD samples were isolated for checking DNA copy numbers and MET and for measuring EXP. In accordance with our results, we discovered obvious co-regulation of CNVcor genes and METcor counterparts. Apart from that, these genes were subject to multi-omics integration. Meanwhile, three subtypes of STAD were detected and confirmed based on independent data. Among them, the subtype with increased aggressiveness was related to decreased mutation frequencies of ARID1A, PIK3CA, ZFHX3, SPECC1, OBSCN, KMT2D, FSIP2, ZBTB20, TTN, and RANBP2, together with the abnormal levels of JPH3, KCNB1, and PLCXD3. Conclusion: According to the results, these aforementioned genes exerted crucial roles in the development of invasive STAD. Our findings on transcriptomic regulation genomically and epigenetically facilitate the understanding of the STAD pathology from different aspects, which help to develop efficient anti-STAD therapy.

Visual Exploratory Data Analysis for Copy Number Variation Studies in Biomedical Research

The study of Copy Number Variations (CNVs) is recently emerging as a hot topic for biomedical cancer research. While different data sources, websites, and tools concerning genomic CNVs have been made publicly available, CNV data is still a largely unexplored source of biological information, due to the limitations of currently available analysis tools. To this respect, we propose a novel platform, named VarNuCopy, that overcomes such limitations by pursuing the core principles of Exploratory Data Analysis (EDA) in the context of Copy Number Variation (CNV) data. The platform has been made publicly available as a web application, and is, to our best knowledge, the first tool enabling visual, interactive exploration and analysis of the CNV landscape of multiple species. Through novel client and server-side optimizations inspired by scalable data science, VarNuCopy implements a comprehensive and efficient data exploration solution that empowers researchers to easily recognize complex trends and patterns within a huge amount of data concerning CNVs, and to identify new target genes that might function as tumor suppressor and oncogenes.

Circulating Exosomal microRNAs as Predictive Biomarkers of Neoadjuvant Chemotherapy Response in Breast Cancer.

Background: Neoadjuvant chemotherapy (NACT) is an increasingly used approach for treatment of breast cancer. The pathological complete response (pCR) is considered a good predictor of disease-specific survival. This study investigated whether circulating exosomal microRNAs could predict pCR in breast cancer patients treated with NACT. Method: Plasma samples of 20 breast cancer patients treated with NACT were collected prior to and after the first cycle. RNA sequencing was used to determine microRNA profiling. The Cancer Genome Atlas (TCGA) was used to explore the expression patterns and survivability of the candidate miRNAs, and their potential targets based on the expression levels and copy number variation (CNV) data. Results: Three miRNAs before that NACT (miR-30b, miR-328 and miR-423) predicted pCR in all of the analyzed samples. Upregulation of miR-127 correlated with pCR in triple-negative breast cancer (TNBC). After the first NACT dose, pCR was predicted by exo-miR-141, while miR-34a, exo-miR182, and exo-miR-183 predicted non-pCR. A significant correlation between the candidate miRNAs and the overall survival, subtype, and metastasis in breast cancer, suggesting their potential role as predictive biomarkers of pCR. Conclusions: If the miRNAs identified in this study are validated in a large cohort of patients, they might serve as predictive non-invasive liquid biopsy biomarkers for monitoring pCR to NACT in breast cancer.

Identification of key genes associated with survival of glioblastoma multiforme using integrated analysis of TCGA datasets

Background and ObjectiveGlioblastoma (GBM) is the most aggressive type of brain tumor. In spite of having various treatment options, GBM patients usually have a poor prognosis. Genetic markers play a vital role in the progression of the disease. Identification of these novel molecular biomarkers is essential to explain the mechanisms or improve the prognosis of GBM. Advances in high throughput genomic technologies enable the analysis of the varied types of omics data to find biomarkers in GBM. Although data repositories like The Cancer Genome Atlas (TCGA) are rich sources of such multi-omics data, integrating these different genomic datasets of varying quality and patient heterogeneity is challenging. MethodsMulti-omics gene expression datasets from TCGA consisting of DNA methylation, RNA sequencing, and copy number variation (CNV) of GBM patient is obtained to carry out the analysis. The Cox proportional hazards regression model is developed in R to identify significant genes from diverse datasets associated with the patient's survival. (Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) is used as an estimator for the model. Validation is performed to determine the accuracy and corresponding prediction error. ResultsFive key genes are identified from DNA Methylation and RNA sequencing datasets those are ANK1, HOXA9, TOX2, CXCR6, PIGZ, and L3MBTL, KDM5B, CCDC138, NUS1P1, and ARHGAP42, respectively. Higher expression values of these genes determine better survival of the GBM patients. Kaplan-Meier estimate curves show the exact correlation. Lower values of AIC and BIC determine the suitability of the model. The prediction model is validated on the test set and signifies a low error rate. Copy number variation data is also analysed to find the significant chromosomal location of GBM patients associated with chromosome 2,5,6,7,12,13, respectively. Among all nine CNV locations are found to be influencing the progression of GBM. ConclusionIntegrated analysis of multiple omics dataset is carried out to identify significant genes from DNA Methylation and RNA sequencing profiles of 76 common individuals. Copy number variation dataset for the same patients is analyzed to recognize notable locations associated with 22 chromosomes. The survival analysis determines the correlation of these biomarkers with the progression of the disease.

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.

BackgroundThe mechanism of cancer occurrence and development could be understood with multi‐omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).MethodsClinical follow‐up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA‐Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic‐related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene‐based prediction model for LUAD was validated in validation datasets.ResultsThe study filtered 2071 prognostic‐related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis‐related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression‐related. A 7‐gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5‐year AUC of survival in the validation set and training set was all ˃ 0.67. Similar results were obtained from multi‐omics validation datasets.ConclusionsThe study builds a novel 7‐gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.

A novel machine learning derived RNA-binding protein gene-based score system predicts prognosis of hepatocellular carcinoma patients.

BackgroundAlthough the expression of RNA-binding protein (RBP) genes in hepatocellular carcinoma (HCC) varies and is associated with tumor progression, there has been no overview study with multiple cohorts and large samples. The HCC-associated RBP genes need to be more accurately identified, and their clinical application value needs to be further explored.MethodsFirst, we used the robust rank aggregation (RRA) algorithm to extract HCC-associated RBP genes from nine HCC microarray datasets and verified them in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and International Cancer Genome Consortium (ICGC) Japanese liver cancer (ICGC-LIRI-JP) cohort. In addition, the copy number variation (CNV), single-nucleotide variant (SNV), and promoter-region methylation data of HCC-associated RBP genes were analyzed. Using the random forest algorithm, we constructed an RBP gene–based prognostic score system (RBP-score). We then evaluated the ability of RBP-score to predict the prognosis of patients. The relationships between RBP-score and other clinical characteristics of patients were analyzed.ResultsThe RRA algorithm identified 30 RBP mRNAs with consistent expression patterns across the nine HCC microarray datasets. These 30 RBP genes were defined as HCC-associated RBP genes. Their mRNA expression patterns were further verified in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Among these 30 RBP genes, some showed significant copy number gain or loss, while others showed differences in the methylation levels of their promoter regions. Some RBP genes were risk factors or protective factors for the prognosis of patients. We extracted 10 key HCC-associated RBP genes using the random forest algorithm and constructed an RBP-score system. RBP-score effectively predicted the overall survival (OS) and disease-free survival (DFS) of HCC patients and was associated with the tumor, node, metastasis (TNM) stage, α-fetoprotein (AFP), and metastasis risk. The clinical value of RBP-score was validated in datasets from different platforms. Cox analysis suggested that a high RBP-score was an independent risk factor for poor prognosis in HCC patients. We also successfully established a combined RBP-score+TNM LASSO-Cox model that more accurately predicted the prognosis.ConclusionThe RBP-score system constructed based on HCC-associated RBP genes is a simple and highly effective prognostic evaluation tool. It is suitable for different subgroups of HCC patients and has cross-platform characteristics. Combining RBP-score with the TNM staging system or other clinical parameters can lead to an even greater clinical benefit. In addition, the identified HCC-associated RBP genes may serve as novel targets for HCC treatment.

Biological Measures in the WLS: Genetic and Microbiome Data

Ever since releasing genotype data in 2017, the WLS continually expands resources available to users interested in genetic research. Key advantages to the WLS data for genetics research include its sibling sample and nearly full life course longitudinal study design. In 2021, we now have state-of-the-art polygenic scores available in multiple domains, such as health, cognition, fertility, personality, risk behaviors and attitudes, and life satisfaction. The scores cover phenotypes spanning from adventurousness, through educational attainment, to age at which voice deepened. Additionally, the genotype data was re-imputed in 2021 to the superior Haplotype Reference Consortium reference panel and the WLS expects to obtain copy number variants data next year. In addition to genetic data, we have a set of novel microbiome data on a subset of participants that allows researchers to study relationships between environments and gut microbial composition.

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high dimensional gene expression, copy number variation and mutation data into cancer subtype informed low dimensional latent representations. The accuracy (test set F1 score >90%) of the MFmap subtype prediction is validated in ten different cancer datasets. We use breast cancer and glioblastoma cohorts as examples to show how subtype specific drug sensitivity can be translated to individual tumour samples. The low dimensional latent representations extracted by MFmap explain known and novel subtype specific features and enable the analysis of cell-state transformations between different subtypes. From a methodological perspective, we report that MFmap is a semi-supervised method which simultaneously achieves good generative and predictive performance and thus opens opportunities in other areas of computational biology.

Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC.

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

ABSTRACT Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.

Comprehensive Analysis of m6A Regulators Characterized by the Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma to Aid Immunotherapy and Chemotherapy.

BackgroundN6-Methyladenosine (m6A), which is a prevalent regulator of mRNA expression, has gathered increasing study interests. Though the role of m6A as being important in many biological processes (such as growth and proliferation of cancers) has been well documented, its potential role in tumor immune microenvironment (TIME) has rarely been analyzed.MethodsWe downloaded RNA expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data from The Cancer Genome Atlas (TCGA). We then curated 21 m6A regulators and clustered patients into three m6A subtypes and m6A-related gene subtypes and compared them based on overall survival (OS). The combination of CIBERSORT as well as ssGSEA quantified the infiltration levels of immune cells and immune-related functions. The m6A scores were determined by using principal component analysis (PCA) algorithm. Furthermore, we evaluate the correlation of m6A regulators with immune and response to therapy.ResultsThree m6A clusters were identified based on the TCGA-HNSCC cohort, and there were significant associations among them in overall outcomes and caner-related pathways. We found that three m6A clusters were consistent with three phenotypes: immune-inflamed, immune-dessert, and immune-excluded. HNSCC patients were divided into high– and low–m6A score groups based on the cutoff of m6A score. Patients with lower m6A score had better overall survival outcome. Further analysis indicated that patients with higher m6A score presented higher tumor mutation burden (TMB). In addition, patients in low–m6A score subgroup had high chemotherapeutics sensitivity. GEO cohort confirmed patients with low m6A score demonstrated significant overall survival advantages and clinical benefits. Low m6A score carry an increased neoantigen load, eliciting a response to immunotherapy, and its value in predicting survival outcomes of immunotherapy was also confirmed in three anti-PD-1 cohorts.ConclusionsOur study demonstrated that m6A regulators are closely related to TIME and the m6A score was an effective prognostic biomarker and predictive indicator for immunotherapy and chemotherapeutics. Comprehensive evaluation of m6A regulators in tumors will extend our understanding of TIME and effectively guide increasing study investigations on immunotherapy and chemotherapy strategies for HNSCC.