Cancer Informatics最新文献

{"title":"Different Tumor Types Share a Common Nuclear Map of Chromosome Territories.","authors":"Fritz F Parl","doi":"10.1177/11769351221148592","DOIUrl":"https://doi.org/10.1177/11769351221148592","url":null,"abstract":"<p><p>Different tumor types are characterized by unique histopathological patterns including distinctive nuclear architectures. I hypothesized that the difference in nuclear appearance is reflected in different nuclear maps of chromosome territories, the discrete regions occupied by individual chromosomes in the interphase nucleus. To test this hypothesis, I used interchromosomal translocations (ITLs) as an analytical tool to map chromosome territories in 11 different tumor types from the TCGA PanCancer database encompassing 6003 tumors with 5295 ITLs. For each chromosome I determined the number and percentage of all ITLs for any given tumor type. Chromosomes were ranked according to the frequency and percentage of ITLs per chromosome. The ranking showed similar patterns for all tumor types. Chromosomes 1, 8, 11, 17, and 19 were ranked in the top quarter, accounting for 35.2% of 5295 ITLs, whereas chromosomes 13, 15, 18, 21, and X were in the bottom quarter, accounting for only 10.5% ITLs. The correlation between the chromosome ranking in the total group of 6003 tumors and the ranking in individual tumor types was significant, ranging from <i>P</i> < .0001 to .0033. Thus, contrary to my hypothesis, different tumor types share a common nuclear map of chromosome territories. Based on the large number of ITLs in 11 different types of malignancy one can discern a shared pattern of chromosome territories in cancer and propose a probabilistic model of chromosomes 1, 8, 11, 17, 19 in the center of the nucleus and chromosomes 13, 15, 18, 21, X at the periphery.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351221148592"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/cd/06/10.1177_11769351221148592.PMC9903037.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Computational Approach to Predict the Role of Genetic Alterations in Methyltransferase Histones Genes With Implications in Liver Cancer.","authors":"Tania Isabella Aravena,&nbsp;Elizabeth Valdés,&nbsp;Nicolás Ayala,&nbsp;Vívian D'Afonseca","doi":"10.1177/11769351231161480","DOIUrl":"https://doi.org/10.1177/11769351231161480","url":null,"abstract":"<p><p>Histone methyltransferases (HMTs) comprise a subclass of epigenetic regulators. Dysregulation of these enzymes results in aberrant epigenetic regulation, commonly observed in various tumor types, including hepatocellular adenocarcinoma (HCC). Probably, these epigenetic changes could lead to tumorigenesis processes. To predict how histone methyltransferase genes and their genetic alterations (somatic mutations, somatic copy number alterations, and gene expression changes) are involved in hepatocellular adenocarcinoma processes, we performed an integrated computational analysis of genetic alterations in 50 HMT genes present in hepatocellular adenocarcinoma. Biological data were obtained through the public repository with 360 samples from patients with hepatocellular carcinoma. Through these biological data, we identified 10 HMT genes (<i>SETDB1, ASH1L, SMYD2, SMYD3, EHMT2, SETD3, PRDM14, PRDM16, KMT2C</i>, and <i>NSD3</i>) with a significant genetic alteration rate (14%) within 360 samples. Of these 10 HMT genes, <i>KMT2C</i> and <i>ASH1L</i> have the highest mutation rate in HCC samples, 5.6% and 2.8%, respectively. Regarding somatic copy number alteration, <i>ASH1L</i> and <i>SETDB1</i> are amplified in several samples, while <i>SETD3, PRDM14</i>, and <i>NSD3</i> showed a high rate of large deletion. Finally, <i>SETDB1, SETD3, PRDM14</i>, and <i>NSD3</i> could play an important role in the progression of hepatocellular adenocarcinoma since alterations in these genes lead to a decrease in patient survival, unlike patients who present these genes without genetic alterations. Our computational analysis provides new insights that help to understand how HMTs are associated with hepatocellular carcinoma, as well as provide a basis for future experimental investigations using HMTs as genetic targets against hepatocellular carcinoma.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231161480"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1e/b4/10.1177_11769351231161480.PMC10064455.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9610566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer.","authors":"Mehdi Hamaneh,&nbsp;Yi-Kuo Yu","doi":"10.1177/11769351231159893","DOIUrl":"https://doi.org/10.1177/11769351231159893","url":null,"abstract":"<p><strong>Motivation: </strong>The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.</p><p><strong>Results: </strong>A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.</p><p><strong>Conclusions: </strong>MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231159893"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/38/68/10.1177_11769351231159893.PMC10052604.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9234981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.","authors":"Nazneen Fatima Shaikh,&nbsp;Chan Shen,&nbsp;Traci LeMasters,&nbsp;Nilanjana Dwibedi,&nbsp;Amit Ladani,&nbsp;Usha Sambamoorthi","doi":"10.1177/11769351231165161","DOIUrl":"https://doi.org/10.1177/11769351231165161","url":null,"abstract":"<p><strong>Objectives: </strong>This study examined prescription NSAIDs as one of the leading predictors of incident depression and assessed the direction of the association among older cancer survivors with osteoarthritis.</p><p><strong>Methods: </strong>This study used a retrospective cohort (N = 14, 992) of older adults with incident cancer (breast, prostate, colorectal cancers, or non-Hodgkin's lymphoma) and osteoarthritis. We used the longitudinal data from the linked Surveillance, Epidemiology, and End Results -Medicare data for the study period from 2006 through 2016, with a 12-month baseline and 12-month follow-up period. Cumulative NSAIDs days was assessed during the baseline period and incident depression was assessed during the follow-up period. An eXtreme Gradient Boosting (XGBoost) model was built with 10-fold repeated stratified cross-validation and hyperparameter tuning using the training dataset. The final model selected from the training data demonstrated high performance (Accuracy: 0.82, Recall: 0.75, Precision: 0.75) when applied to the test data. SHapley Additive exPlanations (SHAP) was used to interpret the output from the XGBoost model.</p><p><strong>Results: </strong>Over 50% of the study cohort had at least one prescption of NSAIDs. Nearly 13% of the cohort were diagnosed with incident depression, with the rates ranging between 7.4% for prostate cancer and 17.0% for colorectal cancer. The highest incident depression rate of 25% was observed at 90 and 120 cumulative NSAIDs days thresholds. Cumulative NSAIDs days was the sixth leading predictor of incident depression among older adults with OA and cancer. Age, education, care fragmentation, polypharmacy, and zip code level poverty were the top 5 predictors of incident depression.</p><p><strong>Conclusion: </strong>Overall, 1 in 8 older adults with cancer and OA were diagnosed with incident depression. Cumulative NSAIDs days was the sixth leading predictor with an overall positive association with incident depression. However, the association was complex and varied by the cumulative NSAIDs days.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231165161"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/25/bc/10.1177_11769351231165161.PMC10123903.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9356662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma.","authors":"Yongjun Liu,&nbsp;Heping Zhang,&nbsp;Yuqing Xu,&nbsp;Yao-Zhong Liu,&nbsp;David P Al-Adra,&nbsp;Matthew M Yeh,&nbsp;Zhengjun Zhang","doi":"10.1177/11769351231190477","DOIUrl":"https://doi.org/10.1177/11769351231190477","url":null,"abstract":"Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231190477"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/11/97/10.1177_11769351231190477.PMC10413891.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10305114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program.","authors":"Jianglin Feng,&nbsp;Esteban Astiazaran Symonds,&nbsp;Jason H Karnes","doi":"10.1177/11769351221144132","DOIUrl":"https://doi.org/10.1177/11769351221144132","url":null,"abstract":"<p><p>Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association follows a simple linear function. Here, we aimed to address these factors by combining visualization and quantification a large-scale contemporary electronic health record database (the All of Us Research Program). We find clear visual and quantitative evidence that breast cancer is strongly, positively, and near-linearly associated with total cholesterol and low-density lipoprotein cholesterol, but not associated with triglycerides. The association of breast cancer with high-density lipoprotein cholesterol was non-linear and age dependent. Standardized odds ratios were 2.12 (95% confidence interval 1.9-2.48), <i>P</i> = 5.6 × 10^-31 for total cholesterol; 1.99 (1.75-2.26), <i>P</i> = 2.6 × 10^-26 for low-density lipoprotein cholesterol; 1.69 (1.3-2.2), <i>P</i> = 9.0 × 10^-5 for high-density lipoprotein cholesterol at age < 56; and 0.65 (0.55-0.78), <i>P</i> = 1.2 × 10^-6 for high-density lipoprotein cholesterol at age ⩾ 56. The inclusion of the lipid levels measured after antihyperlipidemic treatment in the analysis results in erroneous associations. We demonstrate that the use of the logistic regression without inspecting risk variable linearity and accounting for confounding effects may lead to inconsistent results.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351221144132"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8b/89/10.1177_11769351221144132.PMC9841847.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10550794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.","authors":"K Chandrashekar,&nbsp;Anagha S Setlur,&nbsp;Adithya Sabhapathi C,&nbsp;Satyam Suresh Raiker,&nbsp;Satyam Singh,&nbsp;Vidya Niranjan","doi":"10.1177/11769351221147244","DOIUrl":"https://doi.org/10.1177/11769351221147244","url":null,"abstract":"<p><p>Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351221147244"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c2/da/10.1177_11769351221147244.PMC9880585.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10591008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to \"Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression\".","authors":"","doi":"10.1177/11769351231189051","DOIUrl":"https://doi.org/10.1177/11769351231189051","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1177/11769351231177269.].</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231189051"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/24/b1/10.1177_11769351231189051.PMC10350781.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9827252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma.","authors":"Safir Ullah Khan,&nbsp;Zahid Ullah,&nbsp;Hadia Shaukat,&nbsp;Sheeza Unab,&nbsp;Saba Jannat,&nbsp;Waqar Ali,&nbsp;Amir Ali,&nbsp;Muhammad Irfan,&nbsp;Muhammad Fiaz Khan,&nbsp;Rodolfo Daniel Cervantes-Villagrana","doi":"10.1177/11769351231177267","DOIUrl":"https://doi.org/10.1177/11769351231177267","url":null,"abstract":"<p><p>The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with <i>TP53</i> followed by <i>CDKN2A</i> being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed <i>that TP53</i>, <i>MDM2</i>, <i>CDKN2A</i>, and <i>TP53BP1</i> were overexpressed, while <i>MDM4</i> and <i>CDKN2B</i> were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, <i>CDKN2A</i> was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the <i>MDM4</i> was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. <i>MDM2</i> expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. <i>TP53</i> showed a positive correlation with B cells, <i>MDM2</i> with CD8+<i>T</i> cells, macrophages and neutrophils, and <i>MDM4</i> with neutrophils. <i>CDKN2A/B</i> had a non-significant correlation with all six types of immune cells. However, <i>TP53BP1</i> was positively correlated with all five types of immune cells except B cells. Only <i>TP53, MDM2</i>, and <i>CDKN2A</i> had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231177267"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/e4/10.1177_11769351231177267.PMC10475268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10283819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.","authors":"Trinh Nguyen,&nbsp;Xiaopeng Bian,&nbsp;David Roberson,&nbsp;Rakesh Khanna,&nbsp;Qingrong Chen,&nbsp;Chunhua Yan,&nbsp;Rowan Beck,&nbsp;Zelia Worman,&nbsp;Daoud Meerzaman","doi":"10.1177/11769351231180992","DOIUrl":"https://doi.org/10.1177/11769351231180992","url":null,"abstract":"<p><strong>Introduction: </strong>In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently.</p><p><strong>Methods: </strong>We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow.</p><p><strong>Results: </strong>The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing.</p><p><strong>Conclusion: </strong>Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"22 ","pages":"11769351231180992"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/28/1c/10.1177_11769351231180992.PMC10278438.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9707715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}