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Generalized linear models with quasi-likelihood (GLMQL) are used to handle discrete, overdispersed RNA-Seq counts, improving upon conventional t-test assumptions. Trimmed mean of M-values (TMM) normalization accounts for compositional library differences. Using an untreated TCGA BRCA cohort of 104 patients, the analysis targets protein-coding genes enriched with the Magnitude Altitude Scoring (MAS) system, highlighting genes linked to apoptosis, EMT, angiogenesis, hypoxia response, and KRAS signaling, and emphasizing SPRR family transcripts as therapeutic candidates.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identifying-key-genes-involved-in-axillary-lymph-node-metastasis-in-breast-cancer-using-advanced-rna-seq-analysis-a-methodological-approach-with-glmql-and-mas/332233/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identifying-key-genes-involved-in-axillary-lymph-node-metastasis-in-breast-cancer-using-advanced-rna-seq-analysis-a-methodological-approach-with-glmql-and-mas/332233.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What core methodological problem does this study target in RNA-Seq cancer analysis?","Question",{"text":112,"@type":113},"It targets challenges in analyzing RNA-Seq data that arise from discreteness and overdispersion, which can make conventional approaches like the t-test unsuitable for count-based transcriptome data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the study model RNA-Seq counts and why?",{"text":117,"@type":113},"It uses generalized linear models with quasi-likelihood (GLMQLs) to accommodate discrete and overdispersed RNA-Seq counts more appropriately than methods assuming normality and equal variances.",{"name":119,"@type":110,"acceptedAnswer":120},"How are key ALNM-related genes identified and validated in this workflow?",{"text":121,"@type":113},"The approach analyzes an untreated TCGA BRCA cohort, focuses on protein-coding genes, applies TMM normalization for compositional differences, and uses the Magnitude Altitude Scoring (MAS) system to pinpoint genes linked to ALNM and relevant biological pathways.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332233,1790847569,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","Article  \nIdentifying Key Genes Involved in Axillary Lymph Node  \nMetastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS  \nMostafa Rezapour 1, *, Robert Wesolowski 2 and Metin Nafi Gurcan 1  \nCitation: Rezapour, M.; Wesolowski, R.; Gurcan, M.N. Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis:  \nA Methodological Approach with GLMQL and MAS. Int. J. Mol. Sci. 2024, 25, 7306. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/ijms25137306](10.3390/ijms25137306)  \nAcademic Editors: Christos Papadimitriou, Michalis Liontos and Flora Zagouri  \nReceived: 29 May 2024  \nRevised: 23 June 2024  \nAccepted: 1 July 2024  \nPublished: 3 July 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA; [mgurcan@wakehealth.edu](mgurcan@wakehealth.edu)  \n2 Division of Medical Oncology, James Cancer Hospital and the Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA; [robert.wesolowski@osumc.edu](robert.wesolowski@osumc.edu)  \n* [Correspondence: mrezapou@wakehealth.edu](Correspondence: mrezapou@wakehealth.edu)  \nAbstract: Our study aims to address the methodological challenges frequently encountered in RNASeq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such asthe t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial–mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.  \nKeywords: breast cancer; axillary lymph node metastasis (ALNM); RNA sequencing (RNA-Seq); gene expression analysis; generalized linear models; quasi-likelihood F test  \n1. In","cbCaivafJUqeQmDu","https://ap.wps.com/l/cbCaivafJUqeQmDu","pdf",10303443,26,"English","# Introduction\n# Methodological Approach\n## Data and Cohort Selection\n## GLMQL Modeling and Quasi-Likelihood Testing\n## TMM Normalization\n## MAS-Based Gene Identification\n# Results and Biological Interpretation\n## Key ALNM-Associated Protein-Coding Genes\n## Pathways and Functional Processes\n# Discussion and Implications\n# Conclusion","[{\"question\":\"What core methodological problem does this study target in RNA-Seq cancer analysis?\",\"answer\":\"It targets challenges in analyzing RNA-Seq data that arise from discreteness and overdispersion, which can make conventional approaches like the t-test unsuitable for count-based transcriptome data.\"},{\"question\":\"How does the study model RNA-Seq counts and why?\",\"answer\":\"It uses generalized linear models with quasi-likelihood (GLMQLs) to accommodate discrete and overdispersed RNA-Seq counts more appropriately than methods assuming normality and equal variances.\"},{\"question\":\"How are key ALNM-related genes identified and validated in this workflow?\",\"answer\":\"The approach analyzes an untreated TCGA BRCA cohort, focuses on protein-coding genes, applies TMM normalization for compositional differences, and uses the Magnitude Altitude Scoring (MAS) system to pinpoint genes linked to ALNM and relevant biological pathways.\"}]","Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis - A Methodological Approach with GLMQL and MAS | PDF",1789997465,66]