Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer

Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer Ayesha Wadood, Ahmad Ali, Aroosa Faheem, Maria Ghaffar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6488171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Breast cancer (BRCA) is a highly heterogeneous disease, posing significant challenges in prognosis. Despite therapeutic advancements, recurrence and metastasis remain major obstacles, underscoring the need for novel prognostic markers. This study aims to identify key metabolic regulators that may serve as potential biomarkers to improve risk stratification and treatment strategies. Methods: RNA expression data from 208 tumor and 25 normal breast tissue samples were sourced from the GDC database and analyzed using R-based pipelines for differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), pathway enrichment, and survival analysis. Results: Differential expression analysis identified 1,663 dysregulated genes and WGCNA identified 59 gene modules, with the “tan” module (302 genes) significantly associated with tumor status. Cross-referencing WGCNA and differential expression results identified 254 commonly downregulated genes enriched in lipid metabolism pathways. Kaplan-Meier survival analysis highlighted five key genes ( ADIPOQ, LIPE, LEP, SLC2A4 , and LPL ) significantly associated with poor prognosis. The downregulation of these five genes suggests a metabolic shift in breast cancer, linking lipid and glucose metabolism dysregulation to tumor progression and poorer survival outcomes. Conclusion: This study identified a distinct metabolic signature in BRCA, characterized by altered lipid and glucose metabolism leading to disease progression and possibly implying poor prognosis. The identified genes may serve as novel prognostic biomarkers with potential therapeutic implications, warranting further clinical validation to enhance risk stratification and treatment strategies. Breast cancer prognostic markers differential expression analysis Weighted gene co-expression network analysis (WGCNA) lipid and glucose metabolism dysregulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Breast cancer (BRCA) continues to remain a major challenge in oncological practice and accounts for majority of the cancer-related morbidity and mortality among women, with nearly 2.3 million new cases and 685,000 mortalities reported annually [ 1 ]. Despite advancements in early detection and treatment, the overall mortality rate remains high, primarily because more than half of the patients have advanced disease at presentation when curative treatment options become less effective [ 2 ]. While the primary tumor itself is not inherently fatal, its ability to metastasize to vital organs such as the lungs, liver, brain, and bones is the major driver of breast cancer-related deaths [ 3 ]. Notably, between 25% and 50% of patients eventually develop metastases, even after undergoing initial treatment, underscoring the aggressive nature of the disease and the challenges in achieving long-term remission [ 4 ]. The heterogeneity of BRCA is accounted for by the underlying complex genetic, epigenetic, and metabolic alterations that influence tumor progression, therapy resistance, and patient outcomes [ 5 ]. Although traditional clinical and histopathological features—such as tumor size, morphological subtype, histological grade, and nodal involvement—offer some prognostic value, they are insufficient for guiding personalized treatment strategies [ 6 ]. This highlights an urgent need to identify novel biomarkers that can offer better risk stratification, predict treatment responses, and support the development of precision oncology approaches. With better understanding of molecular pathobiology of BRCA, more effective targeted treatments can be developed leading to favorable clinical outcomes. Cancer research has seen significant advancement with the advent of high-throughput sequencing allowing timely diagnosis and better prognostication. [ 7 ]. Recognizing differentially expressed genes (DEGs) that are at play in a specific disease, is the key step towards understanding disease-driving biological pathways. Transcriptomic profiling, especially via gene expression analysis, has led to the discovery of key mRNA signatures that ameliorate the classification, prognostication and therapeutic targeting of breast cancer [ 8 ]. Analytical techniques such as differential expression analysis (DEA) and Weighted Gene Co-expression Network Analysis (WGCNA) have significantly contributed towards identification of critical molecular networks driving cancer progression and biomarker discovery [ 9 – 12 ]. For instance, AMD1, EN1 , and VGLL1 have been implicated in disease progression and predicting poor prognosis in previous studies [ 13 ]. Nevertheless, due to uncertainty in the prognosis of breast cancer despite ongoing advancements, there remains a significant unmet need for the development of more efficient prognostic biomarkers. This study attempts to explore the prognostic potential of various genes by utilizing transcriptomic data and network-based analytical approaches. Using DEA and WGCNA, we aim to recognize genes involved in the metabolic regulation of breast tumor cells and identify their potential to predict prognosis and survival in breast cancer patients. By unravelling novel biomarkers associated with breast cancer prognosis, this study may contribute to the existing evidence of breast cancer prognostication and may also provide novel therapeutic targets. 2. Materials and Methods 2.1. Acquisition and Selection RNA sequencing data for breast cancer (BRCA) was retrieved from The Cancer Genome Atlas (TCGA) using the TCGAbiolinks package in R from GDC database. Transcriptomic data from the TCGA-BRCA project was queried and filtered to include 233 unique cases (25 normal and 208 tumor samples), ensuring a manageable dataset while maintaining representativeness. Moreover, the associated metadata or clinical data was also downloaded (Table S1 ). The queried data was downloaded and prepared using GDCdownload() and GDCprepare() functions. Gene expression data was extracted and formatted with gene identifiers. Corresponding sample metadata was also retrieved for further analysis. Flowchart of the overall study is shown in Fig. 1 2.2. Differential Expression Analysis Differential expression analysis was performed using the DESeq2 package [ 14 ] in R. Input data consisted of RNA sequencing read counts and corresponding metadata previously retrieved) from the TCGA-BRCA cohort. Gene counts were filtered to retain features with a minimum read count of 10 across all samples. Metadata included the sample groupings, specifically distinguishing between tumor and normal samples. The reference level for comparisons was set to normal. Results were extracted and genes with an adjusted p-value (FDR) < 1e-6 and log2FC ≥ ± 2 were considered statistically significant. A volcano plot and heatmap highlighting differentially expressed genes were generated using the EnhancedVolcano and pheatmap package in R. 2.3. Weighted Gene Co-expression Network Analysis (WGCNA) WGCNA was conducted to identify gene modules associated with clinical traits (Table S1 ) using WGCNA package [ 15 ] in R. The optimal soft-thresholding power (β = 12 in this study) was selected using the pickSoftThreshold function to ensure that the network adhered to the scale-free topology criterion with R 2 > 0.8. Subsequently, an adjacency matrix was calculated from pairwise Pearson correlations and transformed into a topological overlap matrix (TOM) to evaluate the strength of connections between genes. Distinct modules of highly interconnected genes were identified by applying hierarchical clustering to the TOM, combined with the DynamicTreeCut algorithm. To identify biologically relevant modules, module eigengenes (MEs), representing the first principal component of each module, were correlated with clinical traits. Metrics such as module membership (MM) and gene significance (GS) were calculated to assess the relevance of individual genes within each module. The module most significantly correlated with tissue type (e.g., ME12 in this study) underwent further analysis. Genes within this module were extracted and subjected to downstream analysis for functional enrichment analyses to uncover key biological processes and pathways. 2.4. Functional and Disease Enrichment Analysis Gene Ontology (GO) enrichment analysis was conducted to identify the biological processes (BP), molecular functions (MF), and cellular components (CC) significantly associated with the genes common to the ME12 module of WGCNA and differential expression analysis. Further, KEGG pathway enrichment analysis was also conducted to investigate biologically important pathways associated with this common gene set. Next, disease ontology (DO) was performed to find out the disease linked to the common genes. All of these analyses were performed using the clusterProfiler package [ 16 ] in R, which enables robust and comprehensive annotation and visualization of gene functions. The enrichment criteria included a p-value threshold of < 0.05 and a q-value threshold of < 0.05, with the Benjamini-Hochberg (BH) method applied to adjust for multiple testing and control the false discovery rate. The enrichment results were visualized using dot plots generated with ggplot2. 2.5. Protein Protein Interaction (PPI) Network Genes (Table S2) that were common in both ME12 module of WGCNA and differential expression analysis were subjected to further functional analysis. A PPI network was constructed using CytoHubba-a plugin in Cytoscape v3.10.3 to explore the relationships between these common genes and to identify hub genes critical to the biological networks. Hub genes were identified by calculating network centrality measures, which prioritize genes with the highest connectivity within the network. 2.6. Kaplan-Meier Survival Analysis Kaplan–Meier survival analysis was performed to assess the prognostic significance of selected biomarkers, including 10 hub genes, in breast cancer samples using the survival package [ 17 ] in R. Stratified overall survival analysis was conducted, and statistical significance was determined using the log-rank test, with a p-value < 0.05 considered statistically significant. 3. Results 3.1. Identification of Differentially Expressed Genes (DEGs) TCGA BRCA dataset contains 25 normal and 208 tumor samples. The raw data was normalized and only those genes were retained in the analysis that had a read count of more than ten. Differential expression analysis was carried out, using adj-p < 0.001in DESeq2 R package, between normal and tumor samples and identified 1663 differentially expressed genes out of total 54211 genes. Out of the 1663 differentially expressed genes, 980 and 683 genes were over expressed and under expressed, respectively in tumor samples (Table S3). Volcano plot (adj-p < 1e-6 and log2FC ≥ ± 2) and heatmap (Fig. 2 ) were plotted for better visualization of differentially expressed genes in BRCA samples as opposed to control samples. 3.2. Identification of significant Co-expression Module in BRCA samples The raw mRNA count data, comprising 233 breast cancer (BRCA) samples (25 normal and 208 tumor), were processed for normalization, retaining 54,211 genes with counts greater than 10. Following normalization, the data underwent pre-filtering and quality assessment to ensure robustness before applying Weighted Gene Co-expression Network Analysis (WGCNA). For quality assessment, hierarchical clustering and PCA were performed to check outliers. Among 233 samples, three outliers were detected (TCGA-AC-A2QH-01A-11R-A18M-07', 'TCGA-A7-A26I-01A-11R-A169-07', 'TCGA-AC-A3OD-01A-11R-A21T-07) which were removed from subsequent analysis. Finally, 230 samples were recruited for further analysis. The subsequent step was to perform variance stabilization using vst() function in deseq2. After this, a soft threshold was applied to identify genes in the same module. Based on a scale-free topology with R 2 > 0.8, Pearson’s correlation matrix of the genes was transformed into a strengthening adjacency matrix by power β = 12. All genes were clustered into 59 modules (ME0 to ME58) labeled with different colors. For clustering, topological overlap matrix (TOM)-based dissimilarity measure based on the Dynamic Tree Cut algorithm was used. All 59 modules with their corresponding genes number are shown in (Table S4) and are given different color. Following this, the interaction of these co-expression modules was analyzed by Pearson’s correlation coefficient. In the correlation, red represents positive correlation while blue shows negative correlation (Fig. 3 ). To identify which module is most differentially expressed across tissue type (tumor vs normal), a model was fitted using limma::lmFit() function. The analysis reveled that ME12 (tan colored module) was most differentially expressed between tumor and normal samples. This tan module (ME12) contains 302 genes (Table S5). Further, to investigate the association between module eigengenes (MEs) and clinical traits (tissue type and vital status), a correlation analysis was performed. This revealed that ME12 (the tan-colored module) was strongly negatively correlated with tissue type and strongly positively correlated with vital status (Fig. 4 ). A subsequent t-test confirmed the statistical significance of ME12's association with tissue type (p = 7.4e-11), indicating that genes in the ME12 module are downregulated in tumor samples compared to normal samples. However, ME12's association with vital status was not statistically significant. Common genes (254) between differentially expressed genes and ME12 module were extracted for further downstream analysis (Table S6). 3.3. Gene Ontology (GO) Enrichment Analysis GO enrichment analysis was performed on the common genes between differential expression analysis and ME12 module of WGCNA. In total, 320 GO terms were identified related to processes (BP), cellular components (CC) and Molecular functions (MF) (Table S7). Amongst BP, regulation of lipid metabolic processes, lipid storage and lipid catabolic processes were significantly enriched. For CC, Lipid droplets, caveola and extracellular matrix were the most prominent. Similarly, for MF, amide binding, NAD retinol dehydrogenase activity and peptide binding were the most significant molecular functions performed by those common genes. The bar plot of top significant enriched GO terms is shown in Fig. 5 . 3.4. KEGG Pathway Enrichment Analysis Additionally, to investigate pathways associated with the biological process of common genes, KEGG pathway enrichment analysis was performed. In total, 190 enriched pathways were identified (Table S8). Among these pathways only few were significantly enriched (adj. p-value < 0.05) (Fig. 6 ). The top two pathways, including PPAR signaling pathway and AMPK signaling pathway, are shown in (Figure S1 and Figure S2, respectively) with green colored genes indicating down regulated genes in tumor samples. 3.5. Disease Otology (DO) Enrichment Analysis DO enrichment analysis was also performed on the common genes between differentially expressed genes and ME12 of WGCNA. In total 521 disease terms were enriched (Table S9.). Top 20 DO terms (adj. p-value < 0.05) are shown in bar plot (Fig. 7 ). Among these DO terms, steatotic liver disease, arteriosclerosis and type 2 diabetes mellitus are at the top suggesting their pathological importance in breast cancer. 3.6. Identification of Hub Genes from PPI network The identified 254 common genes between differential expression analysis and ME12 module were further subjected to protein protein interaction (PPI) network analysis in STRING database. The resulting PPI network was exported to Cytoscape (version 3.10.3) for further processing and visualization. To find out hub genes, a plugin “Cytohubba” was utilized. Top 20 hub genes are shown in Fig. 8 with ADIPOQ ranked as the top hub gene, followed by PPARG, LIPE, LEP, PLIN1, CD36, FABP4, SLC2A4, LPL , and CIDEC in descending order. 3.7. Kaplan-Meier Survival Analysis of Hub Genes Kaplan–Meier survival analysis was performed to assess the prognostic impact of the 10 hub genes. Patients were stratified into high and low expression groups based on median gene expression levels. The survival curves revealed that low expression of five genes ( ADIPOQ, LIPE, LEP, SLC2A4 , and LPL ) was significantly associated with worse overall survival in breast cancer patients (p = 0.021), indicating their potential role as prognostic biomarkers. Figure 9 . 4. Discussion Breast cancer (BRCA) remains the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality [ 18 ]. Despite advances in treatment, the disease's high molecular heterogeneity complicates prognosis and therapeutic decision-making [ 5 ]. While existing clinical, pathological, and molecular factors provide some prognostic value, they are often insufficient for precise risk stratification, highlighting the urgent need for novel biomarkers [ 18 , 19 ]. Our study identifies a distinct metabolic signature in BRCA, characterized by the collective downregulation of key lipid and glucose metabolism genes- ADIPOQ, LIPE, LEP, SLC2A4 , and LPL -suggesting a fundamental role of metabolic reprogramming in tumor progression and aggressiveness. To systematically identify prognostic metabolic markers, we integrated transcriptomic data from the GDC database, revealing 1,663 differentially expressed genes in breast cancer tissues compared to normal controls. Weighted Gene Co-expression Network Analysis (WGCNA) identified 302 genes significantly associated with tumor status. Following this, the differentially expressed genes (1,663 genes) were cross-referenced with those identified by WGCNA (302 genes) and pinpointed 254 common genes which were consistently downregulated in tumor samples. Functional enrichment analyses highlighted that these genes are predominantly involved in lipid metabolism, including lipid storage and catabolic processes, consistent with previous report linking lipid metabolic dysregulation to breast cancer pathophysiology [ 20 ]. Moreover, pathway analysis indicated significant associations with the PPAR and AMPK signaling pathways, which are critical regulators of lipid oxidation, glucose homeostasis, and tumor cell survival. Prior studies have shown that activation of these pathways can suppress tumor growth by modulating energy metabolism [ 21 – 24 ]. Additionally, Disease Ontology (DO) analysis revealed that these genes are associated with metabolic associated fatty liver disease, atherosclerosis, and type 2 diabetes mellitus, suggesting their involvement in metabolic dysfunctions commonly linked to breast cancer pathology. Prior studies also showed the link of various metabolic disorders with breast cancer [ 25 , 26 ]. Building on this, a set of 10 hub genes from the 254 common genes were identified including ADIPOQ, PPARG, LIPE, LEP, PLIN1, CD36, FABP4, SLC2A4, LPL , and CIDEC . The hub genes were further subjected to Kaplan-Meier survival analysis which demonstrated that five hub genes (including ADIPOQ, LIPE, LEP, SLC2A4, and LPL; all of which were downregulated in cancer tissues) correlated significantly to patient’s survival, highlighting their potential to serve as prognostic markers in breast cancer. This study found a distinctive metabolic pattern in BRCA, characterized by the under expression of five genes viz ADIPOQ, LIPE, LPL, SLC2A4 , and LEP. The aforementioned genes regulate lipid and glucose metabolism. The collective inhibition of all these genes in breast tumor cells lead to metabolic shift and their reliance on glycolysis that facilitate tumor progression, therapy resistance and thus poor prognosis. These findings present novel insights into how breast tumors achieve metabolic reprogramming to allow a sustained dependence on glucose instead of fatty acids which is necessary for them to sustain and maintain their virulence. The study also suggests that the identified metabolic targets can also be exploited for therapeutic interventions. ADIPOQ (Adiponectin) is famous for its role in the regulation of lipid metabolism and is also a recognized tumor suppressor gene. It works by downregulating SREBP-1 and related enzymes, thereby, upregulating lipolysis and fatty acid oxidation (FAO) while concomitantly reducing fatty acid synthesis (FAS) [ 27 , 28 ]. Under expression of ADIPOQ leads to increased FAS providing necessary lipids to allow tumor growth while simultaneously suppressing FAO leading to reduced apoptosis. This study found that ADIPOQ gene is significantly downregulated in breast tumor cells. This leads to augmented FAS and diminished FAO, possibly favoring tumor progression and conferring poor prognosis. Similar observation about ADIPOQ downregulation was also reported to be linked to poor survival in BRCA patients in a previous study [ 29 , 30 ]. This underscores ADIPOQ as a potential prognostic marker. Downregulation of LIPE (Hormone-Sensitive Lipase) and LPL (Lipoprotein Lipase) add to this tumor favoring metabolic transition by halting lipid breakdown and uptake respectively. Suppressing the expression of LIPE results in diminished lipids breakdown which in turn reduces the production of fatty acids that could otherwise serve as a substrate for beta oxidation. Moreover, suppression of LPL expression reduces fatty acid uptake by tumor cells compelling cellular metabolic needs to be met by glycolysis. This metabolic re-programming is in line with the findings reported by previous studies stating that increased reliance on glycolysis provides tumor cells with survival benefits in the setting of hypoxia and nutrient deprivation [ 29 , 31 ]. The aforementioned metabolic changes caused by the downregulation of both LIPE and LPL leading to tumor cells becoming more reliant on glycolysis is consistent with Warburg effect, a hallmark of neoplastic cells. We also found that the expression of SLC2A4 (GLUT4) was downregulated in breast tumor. SLC2A4 is an insulin depended glucose transporter needed for internalization of glucose molecules across the cell membrane [ 32 ]. While this appears counter-intuitive, however, under expression of GLUT4 (K m = 6.6 mM) deprives tumor cells of glucose thereby creating metabolic stress. This metabolic stress in turn leads to stabilization of hypoxia inducible factor 1α, which up regulates the expression of GLUT 1, a high affinity isoform of glucose transporter (K m = 3–7 mM) [ 33 , 34 ]. This adaptive GLUT1 upregulation further helps tumor cells sustain Warburg effect by maintaining their reliance on glucose for energy generation. This helps tumor cells with proliferation, escape from immune system and developing resistance to therapy [ 35 ]. GLUT4 downregulation, therefore, is yet another modality breast tumor cells employ to ensure glucose driven metabolic shift, nurturing tumor survival and aggressiveness. The last key gene found in the current study is LEP (Leptin). LEP downregulation causes perturbation in leptin signaling, imparting changes in PI3K/AKT/mTOR and JAK/STAT3 pathways, which are indispensable for tumor metabolism and sustenance. This results in increased glycolytic flux, inflammation, and aggressive tumor behavior adversely impacting the overall prognosis [ 29 , 30 , 36 ]. The concomitant downregulation of ADIPOQ, LIPE, LPL, SLC2A4 , and LEP may have future clinical implications. They may serve as biomarkers for prognostication or risk stratification. In addition, the findings of this study also highlight that metabolic targeted therapies might have a role to play in the management of breast cancer. Reversing the effects of ADIPOQ suppression by reactivating FAO and inhibiting FAS might help overwhelm the proto-onco effects of ADIPOQ suppression. Targeting GLUT1 or HIF-1α may ameliorate resistance to therapy in tumors harboring Warburg effect phenomenon. In addition, metabolic reprogramming is being increasingly reported as a potential contributor to the tumor’s ability to evade immune response. Thus, targeting metabolic shifts may help intensify the efficacy of current immune therapies. 5. Conclusion In summary, the results of this study highlight a unique metabolic pattern in breast tumor cells characterized by altered lipid and glucose metabolism. The down regulation of all the five genes ( ADIPOQ, LIPE, LPL , SLC2A4 , and LEP ) identified in this study nurtures a metabolic milieu that supports cancer survival, proliferation, and resistance to therapy possibly conferring poor prognosis. The study also provides newer insights into the mechanisms of this metabolic reprogramming, pinpointing potential metabolic targets that can be exploited for novel therapeutic developments for breast cancer. Declarations 7.1. Ethics approval and consent to participate Not Applicable 7.2. Consent for publication Not Applicable 7.3. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. 7.4. Competing interests The authors declare that they have no competing interests. 7.5. Funding The authors received no specific funding for this work. 7.6. Authors' contributions AW conceptualized the study, conducted the data analysis, and drafted the initial manuscript. AA, AF, and MG contributed to the writing, critical review, and revision of the manuscript for important intellectual content. All authors read and approved the final version of the manuscript. 7.7. Acknowledgements The authors acknowledge the Genomic Data Commons (GDC) and The Cancer Genome Atlas (TCGA) project for providing access to the publicly available datasets used in this study. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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Functional properties and genomics of glucose transporters. Curr Genomics. 2007;8(2):113–28. doi.org/10.2174/138920207780368187 . Shi Z, Liu J, Wang F, Li Y. Integrated analysis of Solute carrier family-2 members reveals SLC2A4 as an independent favorable prognostic biomarker for breast cancer. Channels. 2021;15(1):555–68. doi.org/10.1080/19336950.2021.1973788 . Jin TY, Saindane M, Park KS, Kim S, Nam S, Yoo Y, et al. LEP as a potential biomarker in prognosis of breast cancer: systemic review and meta analyses (PRISMA). Med. 2021;100(33):e26896. doi.org/10.1097/MD.0000000000026896 . Additional Declarations No competing interests reported. Supplementary Files GA.jpeg Graphical Abstract Supplementaryfiguresandtables.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6488171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445575285,"identity":"b6bf97b9-e494-4eed-9880-92795936771f","order_by":0,"name":"Ayesha Wadood","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYJACZjB5gIFBIqHCBkiSpOXBmTQStUg+bDtMWAv/jOTDnwtqDsvzHW8+eCPhzPnE/tnNBx8w1NhE49IicSMtTXrGscOGM88cS7ZIqLidOOPOsWQDhmNpuQ249NzIMWPmYbvNuAHIkEg4czuxAcRgbDiMU4v8jfzPn3n+3bYHa0lsO5c4n5AWgxs5DNK8bbcToVoOQBj4tBieeWYmzdv3PxnilzPJxhtvpCUbJODxi9zx5Mefeb6l2fYBQ+zmjwo72Xk3kg8++FBjg9v7AgmofEewygQMdUiA/wAq3x6f4lEwCkbBKBiZAADJMGpPPW2lAgAAAABJRU5ErkJggg==","orcid":"","institution":"Quaid-I-Azam University","correspondingAuthor":true,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Wadood","suffix":""},{"id":445575286,"identity":"a52361a5-d43f-4052-b447-5b5922a5476e","order_by":1,"name":"Ahmad Ali","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Ali","suffix":""},{"id":445575287,"identity":"35278bfb-d3bb-4713-942c-b238a57ceec2","order_by":2,"name":"Aroosa Faheem","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Aroosa","middleName":"","lastName":"Faheem","suffix":""},{"id":445575288,"identity":"f07dfda7-9240-49ea-9636-b260a267be79","order_by":3,"name":"Maria Ghaffar","email":"","orcid":"","institution":"Quaid-I-Azam University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Ghaffar","suffix":""}],"badges":[],"createdAt":"2025-04-20 08:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6488171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6488171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81088930,"identity":"6c81b39b-449e-4329-b0e3-ada65330128e","added_by":"auto","created_at":"2025-04-22 06:49:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139559,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of methodology of the current study\u003c/p\u003e","description":"","filename":"1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/f8baceffaf46ab993e2695f9.jpeg"},{"id":81088756,"identity":"bba6290d-a847-4386-9cf4-f412a0c33348","added_by":"auto","created_at":"2025-04-22 06:41:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":291191,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of differentially expressed genes in breast cancer compared to normal samples (a) The volcano plot illustrating differentially expressed genes in breast cancer. Dark purple dots on the eft side indicate downregulated genes. Whereas, dark purple dots on right side represent upregulated genes, and pink dots signify genes with no significant difference in expression. (B) The heatmap of differentially expressed genes, where rows correspond to genes and columns represent patient samples. The pink bar represents primary solid tumor, red bar represents metastatic samples, while the green bar indicates normal control samples.\u003c/p\u003e","description":"","filename":"2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/08b9b3ad2b9c145bc868229b.jpeg"},{"id":81088931,"identity":"485a653c-78e9-4515-9402-0b69f5e1cf0f","added_by":"auto","created_at":"2025-04-22 06:49:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":389298,"visible":true,"origin":"","legend":"\u003cp\u003eGene co-expression network analysis and modular structure identification. (a) The scale-free topology plot assesses the network's adherence to a scale-free topology. (b) The clustering dendrogram groups genes based on topological overlap, with assigned module colors representing distinct co-expression clusters. (c) The bar plot displays the identified modules, where bar colors indicate module identity and bar length represents the number of genes within each module. (d) The heatmap of eigengene correlations visualizes the Pearson correlation among co-expressed gene modules, highlighting the relationships between different modules.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/bc8b296150ca6703ab3646f0.jpeg"},{"id":81089993,"identity":"657e1b8e-5b9c-43c2-9e28-47183060dd59","added_by":"auto","created_at":"2025-04-22 06:57:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145132,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between module Table S6eigengenes (MEs) and clinical traits (tissue type: tumor vs. control, vital status: alive vs. deceased). (a) The heatmap highlights ME12 (tan-colored module) as significantly negatively correlated with tissue type, while no module shows a strong association with vital status. (b) The boxplot shows ME12 is significantly downregulated in tumors compared to normal samples (p = 7.4e-11, independent two-sample t-test). (c) The boxplot illustrates ME12’s relationship with vital status, showing no significant difference (p = 1.4e01).\u003c/p\u003e","description":"","filename":"4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/1739f2bd75b7ef073ba641f8.jpeg"},{"id":81088761,"identity":"82d73283-f1ed-457a-b013-4092584afcbb","added_by":"auto","created_at":"2025-04-22 06:41:58","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":247550,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of common genes from differential expression and the ME12 module. The bar plot illustrates the top 20 significantly enriched GO terms across biological processes (BP), cellular components (CC), and molecular functions (MF).\u003c/p\u003e","description":"","filename":"5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/bca662f02d81e5610cfa2237.jpeg"},{"id":81089994,"identity":"df5f6340-a1bf-4d4d-a3eb-9a7e454d8241","added_by":"auto","created_at":"2025-04-22 06:57:58","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60238,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of common genes from differential expression and the ME12 module. The bar plot displays the top 8 significantly enriched pathways, highlighting key biological processes.\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/8ebcbd29ff7645822a31e7ec.jpeg"},{"id":81088933,"identity":"0d92167f-fd55-4e7e-819a-1272224a7b54","added_by":"auto","created_at":"2025-04-22 06:49:58","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":227754,"visible":true,"origin":"","legend":"\u003cp\u003eDisease Ontology (DO) enrichment analysis of common genes from differential expression and the ME12 module. The bar plot displays the top 20 significantly enriched disease terms, highlighting key diseases associated with breast cancer.\u003c/p\u003e","description":"","filename":"7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/ce128b2d535e77225cc0edb6.jpeg"},{"id":81088758,"identity":"bddebff3-7e3b-4db6-a617-4e6f5e658d34","added_by":"auto","created_at":"2025-04-22 06:41:58","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":63737,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene interaction network constructed using cytoHubba in Cytoscape and ranked. Nodes in red represent highly ranked hub genes, while yellow nodes indicate lower-ranked genes. Intermediate colors between red and yellow represent genes with moderate centrality in the network.\u003c/p\u003e","description":"","filename":"8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/528d16914ec2b8a49a407323.jpeg"},{"id":81088941,"identity":"07fe0276-147b-4dec-a873-3f6388ab822f","added_by":"auto","created_at":"2025-04-22 06:49:58","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":122081,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier analysis showing worse survival in breast cancer patients with low expression of \u003cem\u003eADIPOQ, LIPE, LEP, SLC2A4\u003c/em\u003e, and \u003cem\u003eLPL\u003c/em\u003e (p = 0.021, log-rank test). The risk table displays the number of patients at risk over time in each group.\u003c/p\u003e","description":"","filename":"9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/258bef218f0acc15eecf2268.jpeg"},{"id":81227361,"identity":"2a31a4da-bd62-4491-a43a-e128d8fee044","added_by":"auto","created_at":"2025-04-23 16:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2482069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/d89f86b1-86bd-4308-84cb-d08b82ff21f0.pdf"},{"id":81088753,"identity":"c4bb050a-d5f1-40a6-bc9d-0c097a863a2a","added_by":"auto","created_at":"2025-04-22 06:41:58","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":188320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GA.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/b2b2d83cde5e5e64b1c12d5f.jpeg"},{"id":81088766,"identity":"dafacbf7-a430-4916-9541-926ea9153d5b","added_by":"auto","created_at":"2025-04-22 06:41:58","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1026754,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiguresandtables.zip","url":"https://assets-eu.researchsquare.com/files/rs-6488171/v1/b22196e07f5049c9ec7e1529.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer (BRCA) continues to remain a major challenge in oncological practice and accounts for majority of the cancer-related morbidity and mortality among women, with nearly 2.3\u0026nbsp;million new cases and 685,000 mortalities reported annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in early detection and treatment, the overall mortality rate remains high, primarily because more than half of the patients have advanced disease at presentation when curative treatment options become less effective [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the primary tumor itself is not inherently fatal, its ability to metastasize to vital organs such as the lungs, liver, brain, and bones is the major driver of breast cancer-related deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, between 25% and 50% of patients eventually develop metastases, even after undergoing initial treatment, underscoring the aggressive nature of the disease and the challenges in achieving long-term remission [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe heterogeneity of BRCA is accounted for by the underlying complex genetic, epigenetic, and metabolic alterations that influence tumor progression, therapy resistance, and patient outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although traditional clinical and histopathological features\u0026mdash;such as tumor size, morphological subtype, histological grade, and nodal involvement\u0026mdash;offer some prognostic value, they are insufficient for guiding personalized treatment strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This highlights an urgent need to identify novel biomarkers that can offer better risk stratification, predict treatment responses, and support the development of precision oncology approaches. With better understanding of molecular pathobiology of BRCA, more effective targeted treatments can be developed leading to favorable clinical outcomes.\u003c/p\u003e \u003cp\u003eCancer research has seen significant advancement with the advent of high-throughput sequencing allowing timely diagnosis and better prognostication. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recognizing differentially expressed genes (DEGs) that are at play in a specific disease, is the key step towards understanding disease-driving biological pathways. Transcriptomic profiling, especially via gene expression analysis, has led to the discovery of key mRNA signatures that ameliorate the classification, prognostication and therapeutic targeting of breast cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Analytical techniques such as differential expression analysis (DEA) and Weighted Gene Co-expression Network Analysis (WGCNA) have significantly contributed towards identification of critical molecular networks driving cancer progression and biomarker discovery [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For instance, \u003cem\u003eAMD1, EN1\u003c/em\u003e, and \u003cem\u003eVGLL1\u003c/em\u003e have been implicated in disease progression and predicting poor prognosis in previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nevertheless, due to uncertainty in the prognosis of breast cancer despite ongoing advancements, there remains a significant unmet need for the development of more efficient prognostic biomarkers.\u003c/p\u003e \u003cp\u003eThis study attempts to explore the prognostic potential of various genes by utilizing transcriptomic data and network-based analytical approaches. Using DEA and WGCNA, we aim to recognize genes involved in the metabolic regulation of breast tumor cells and identify their potential to predict prognosis and survival in breast cancer patients. By unravelling novel biomarkers associated with breast cancer prognosis, this study may contribute to the existing evidence of breast cancer prognostication and may also provide novel therapeutic targets.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Acquisition and Selection\u003c/h2\u003e \u003cp\u003eRNA sequencing data for breast cancer (BRCA) was retrieved from The Cancer Genome Atlas (TCGA) using the TCGAbiolinks package in R from GDC database. Transcriptomic data from the TCGA-BRCA project was queried and filtered to include 233 unique cases (25 normal and 208 tumor samples), ensuring a manageable dataset while maintaining representativeness. Moreover, the associated metadata or clinical data was also downloaded (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The queried data was downloaded and prepared using GDCdownload() and GDCprepare() functions. Gene expression data was extracted and formatted with gene identifiers. Corresponding sample metadata was also retrieved for further analysis. Flowchart of the overall study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed using the DESeq2 package [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] in R. Input data consisted of RNA sequencing read counts and corresponding metadata previously retrieved) from the TCGA-BRCA cohort. Gene counts were filtered to retain features with a minimum read count of 10 across all samples. Metadata included the sample groupings, specifically distinguishing between tumor and normal samples. The reference level for comparisons was set to normal. Results were extracted and genes with an adjusted p-value (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;1e-6 and log2FC\u0026thinsp;\u0026ge;\u0026thinsp;\u0026plusmn;\u0026thinsp;2 were considered statistically significant. A volcano plot and heatmap highlighting differentially expressed genes were generated using the EnhancedVolcano and pheatmap package in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWGCNA was conducted to identify gene modules associated with clinical traits (Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) using WGCNA package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] in R. The optimal soft-thresholding power (β\u0026thinsp;=\u0026thinsp;12 in this study) was selected using the pickSoftThreshold function to ensure that the network adhered to the scale-free topology criterion with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8. Subsequently, an adjacency matrix was calculated from pairwise Pearson correlations and transformed into a topological overlap matrix (TOM) to evaluate the strength of connections between genes. Distinct modules of highly interconnected genes were identified by applying hierarchical clustering to the TOM, combined with the DynamicTreeCut algorithm. To identify biologically relevant modules, module eigengenes (MEs), representing the first principal component of each module, were correlated with clinical traits. Metrics such as module membership (MM) and gene significance (GS) were calculated to assess the relevance of individual genes within each module. The module most significantly correlated with tissue type (e.g., ME12 in this study) underwent further analysis. Genes within this module were extracted and subjected to downstream analysis for functional enrichment analyses to uncover key biological processes and pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Functional and Disease Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) enrichment analysis was conducted to identify the biological processes (BP), molecular functions (MF), and cellular components (CC) significantly associated with the genes common to the ME12 module of WGCNA and differential expression analysis. Further, KEGG pathway enrichment analysis was also conducted to investigate biologically important pathways associated with this common gene set. Next, disease ontology (DO) was performed to find out the disease linked to the common genes. All of these analyses were performed using the clusterProfiler package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] in R, which enables robust and comprehensive annotation and visualization of gene functions. The enrichment criteria included a p-value threshold of \u0026lt;\u0026thinsp;0.05 and a q-value threshold of \u0026lt;\u0026thinsp;0.05, with the Benjamini-Hochberg (BH) method applied to adjust for multiple testing and control the false discovery rate. The enrichment results were visualized using dot plots generated with ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Protein Protein Interaction (PPI) Network\u003c/h2\u003e \u003cp\u003eGenes (Table S2) that were common in both ME12 module of WGCNA and differential expression analysis were subjected to further functional analysis. A PPI network was constructed using CytoHubba-a plugin in Cytoscape v3.10.3 to explore the relationships between these common genes and to identify hub genes critical to the biological networks. Hub genes were identified by calculating network centrality measures, which prioritize genes with the highest connectivity within the network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Kaplan-Meier Survival Analysis\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis was performed to assess the prognostic significance of selected biomarkers, including 10 hub genes, in breast cancer samples using the survival package [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in R. Stratified overall survival analysis was conducted, and statistical significance was determined using the log-rank test, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Identification of Differentially Expressed Genes (DEGs)\u003c/h2\u003e \u003cp\u003eTCGA BRCA dataset contains 25 normal and 208 tumor samples. The raw data was normalized and only those genes were retained in the analysis that had a read count of more than ten. Differential expression analysis was carried out, using adj-p\u0026thinsp;\u0026lt;\u0026thinsp;0.001in DESeq2 R package, between normal and tumor samples and identified 1663 differentially expressed genes out of total 54211 genes. Out of the 1663 differentially expressed genes, 980 and 683 genes were over expressed and under expressed, respectively in tumor samples (Table S3). Volcano plot (adj-p\u0026thinsp;\u0026lt;\u0026thinsp;1e-6 and log2FC\u0026thinsp;\u0026ge;\u0026thinsp;\u0026plusmn;\u0026thinsp;2) and heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were plotted for better visualization of differentially expressed genes in BRCA samples as opposed to control samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Identification of significant Co-expression Module in BRCA samples\u003c/h2\u003e \u003cp\u003eThe raw mRNA count data, comprising 233 breast cancer (BRCA) samples (25 normal and 208 tumor), were processed for normalization, retaining 54,211 genes with counts greater than 10. Following normalization, the data underwent pre-filtering and quality assessment to ensure robustness before applying Weighted Gene Co-expression Network Analysis (WGCNA). For quality assessment, hierarchical clustering and PCA were performed to check outliers. Among 233 samples, three outliers were detected (TCGA-AC-A2QH-01A-11R-A18M-07', 'TCGA-A7-A26I-01A-11R-A169-07', 'TCGA-AC-A3OD-01A-11R-A21T-07) which were removed from subsequent analysis. Finally, 230 samples were recruited for further analysis. The subsequent step was to perform variance stabilization using vst() function in deseq2. After this, a soft threshold was applied to identify genes in the same module. Based on a scale-free topology with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8, Pearson\u0026rsquo;s correlation matrix of the genes was transformed into a strengthening adjacency matrix by power β\u0026thinsp;=\u0026thinsp;12. All genes were clustered into 59 modules (ME0 to ME58) labeled with different colors. For clustering, topological overlap matrix (TOM)-based dissimilarity measure based on the Dynamic Tree Cut algorithm was used. All 59 modules with their corresponding genes number are shown in (Table S4) and are given different color. Following this, the interaction of these co-expression modules was analyzed by Pearson\u0026rsquo;s correlation coefficient. In the correlation, red represents positive correlation while blue shows negative correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify which module is most differentially expressed across tissue type (tumor vs normal), a model was fitted using limma::lmFit() function. The analysis reveled that ME12 (tan colored module) was most differentially expressed between tumor and normal samples. This tan module (ME12) contains 302 genes (Table S5).\u003c/p\u003e \u003cp\u003eFurther, to investigate the association between module eigengenes (MEs) and clinical traits (tissue type and vital status), a correlation analysis was performed. This revealed that ME12 (the tan-colored module) was strongly negatively correlated with tissue type and strongly positively correlated with vital status (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A subsequent t-test confirmed the statistical significance of ME12's association with tissue type (p\u0026thinsp;=\u0026thinsp;7.4e-11), indicating that genes in the ME12 module are downregulated in tumor samples compared to normal samples. However, ME12's association with vital status was not statistically significant. Common genes (254) between differentially expressed genes and ME12 module were extracted for further downstream analysis (Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Gene Ontology (GO) Enrichment Analysis\u003c/h2\u003e \u003cp\u003eGO enrichment analysis was performed on the common genes between differential expression analysis and ME12 module of WGCNA. In total, 320 GO terms were identified related to processes (BP), cellular components (CC) and Molecular functions (MF) (Table S7). Amongst BP, regulation of lipid metabolic processes, lipid storage and lipid catabolic processes were significantly enriched. For CC, Lipid droplets, caveola and extracellular matrix were the most prominent. Similarly, for MF, amide binding, NAD retinol dehydrogenase activity and peptide binding were the most significant molecular functions performed by those common genes. The bar plot of top significant enriched GO terms is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. KEGG Pathway Enrichment Analysis\u003c/h2\u003e \u003cp\u003eAdditionally, to investigate pathways associated with the biological process of common genes, KEGG pathway enrichment analysis was performed. In total, 190 enriched pathways were identified (Table S8). Among these pathways only few were significantly enriched (adj. p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The top two pathways, including PPAR signaling pathway and AMPK signaling pathway, are shown in (Figure\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure S2, respectively) with green colored genes indicating down regulated genes in tumor samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Disease Otology (DO) Enrichment Analysis\u003c/h2\u003e \u003cp\u003eDO enrichment analysis was also performed on the common genes between differentially expressed genes and ME12 of WGCNA. In total 521 disease terms were enriched (Table S9.). Top 20 DO terms (adj. p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bar plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Among these DO terms, steatotic liver disease, arteriosclerosis and type 2 diabetes mellitus are at the top suggesting their pathological importance in breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Identification of Hub Genes from PPI network\u003c/h2\u003e \u003cp\u003eThe identified 254 common genes between differential expression analysis and ME12 module were further subjected to protein protein interaction (PPI) network analysis in STRING database. The resulting PPI network was exported to Cytoscape (version 3.10.3) for further processing and visualization. To find out hub genes, a plugin \u0026ldquo;Cytohubba\u0026rdquo; was utilized. Top 20 hub genes are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e with \u003cem\u003eADIPOQ\u003c/em\u003e ranked as the top hub gene, followed by \u003cem\u003ePPARG, LIPE, LEP, PLIN1, CD36, FABP4, SLC2A4, LPL\u003c/em\u003e, and \u003cem\u003eCIDEC\u003c/em\u003e in descending order.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Kaplan-Meier Survival Analysis of Hub Genes\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis was performed to assess the prognostic impact of the 10 hub genes. Patients were stratified into high and low expression groups based on median gene expression levels. The survival curves revealed that low expression of five genes (\u003cem\u003eADIPOQ, LIPE, LEP, SLC2A4\u003c/em\u003e, and \u003cem\u003eLPL\u003c/em\u003e) was significantly associated with worse overall survival in breast cancer patients (p\u0026thinsp;=\u0026thinsp;0.021), indicating their potential role as prognostic biomarkers. Figure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBreast cancer (BRCA) remains the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Despite advances in treatment, the disease's high molecular heterogeneity complicates prognosis and therapeutic decision-making [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While existing clinical, pathological, and molecular factors provide some prognostic value, they are often insufficient for precise risk stratification, highlighting the urgent need for novel biomarkers [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study identifies a distinct metabolic signature in BRCA, characterized by the collective downregulation of key lipid and glucose metabolism genes-\u003cem\u003eADIPOQ, LIPE, LEP, SLC2A4\u003c/em\u003e, and \u003cem\u003eLPL\u003c/em\u003e-suggesting a fundamental role of metabolic reprogramming in tumor progression and aggressiveness.\u003c/p\u003e \u003cp\u003eTo systematically identify prognostic metabolic markers, we integrated transcriptomic data from the GDC database, revealing 1,663 differentially expressed genes in breast cancer tissues compared to normal controls. Weighted Gene Co-expression Network Analysis (WGCNA) identified 302 genes significantly associated with tumor status. Following this, the differentially expressed genes (1,663 genes) were cross-referenced with those identified by WGCNA (302 genes) and pinpointed 254 common genes which were consistently downregulated in tumor samples. Functional enrichment analyses highlighted that these genes are predominantly involved in lipid metabolism, including lipid storage and catabolic processes, consistent with previous report linking lipid metabolic dysregulation to breast cancer pathophysiology [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Moreover, pathway analysis indicated significant associations with the PPAR and AMPK signaling pathways, which are critical regulators of lipid oxidation, glucose homeostasis, and tumor cell survival. Prior studies have shown that activation of these pathways can suppress tumor growth by modulating energy metabolism [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, Disease Ontology (DO) analysis revealed that these genes are associated with metabolic associated fatty liver disease, atherosclerosis, and type 2 diabetes mellitus, suggesting their involvement in metabolic dysfunctions commonly linked to breast cancer pathology. Prior studies also showed the link of various metabolic disorders with breast cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Building on this, a set of 10 hub genes from the 254 common genes were identified including \u003cem\u003eADIPOQ, PPARG, LIPE, LEP, PLIN1, CD36, FABP4, SLC2A4, LPL\u003c/em\u003e, and \u003cem\u003eCIDEC\u003c/em\u003e. The hub genes were further subjected to Kaplan-Meier survival analysis which demonstrated that five hub genes (including \u003cem\u003eADIPOQ, LIPE, LEP, SLC2A4, and LPL;\u003c/em\u003e all of which were downregulated in cancer tissues) correlated significantly to patient\u0026rsquo;s survival, highlighting their potential to serve as prognostic markers in breast cancer.\u003c/p\u003e \u003cp\u003eThis study found a distinctive metabolic pattern in BRCA, characterized by the under expression of five genes viz \u003cem\u003eADIPOQ, LIPE, LPL, SLC2A4\u003c/em\u003e, and \u003cem\u003eLEP.\u003c/em\u003e The aforementioned genes regulate lipid and glucose metabolism. The collective inhibition of all these genes in breast tumor cells lead to metabolic shift and their reliance on glycolysis that facilitate tumor progression, therapy resistance and thus poor prognosis. These findings present novel insights into how breast tumors achieve metabolic reprogramming to allow a sustained dependence on glucose instead of fatty acids which is necessary for them to sustain and maintain their virulence. The study also suggests that the identified metabolic targets can also be exploited for therapeutic interventions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eADIPOQ\u003c/em\u003e (Adiponectin) is famous for its role in the regulation of lipid metabolism and is also a recognized tumor suppressor gene. It works by downregulating \u003cem\u003eSREBP-1\u003c/em\u003e and related enzymes, thereby, upregulating lipolysis and fatty acid oxidation (FAO) while concomitantly reducing fatty acid synthesis (FAS) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Under expression of \u003cem\u003eADIPOQ\u003c/em\u003e leads to increased FAS providing necessary lipids to allow tumor growth while simultaneously suppressing FAO leading to reduced apoptosis. This study found that \u003cem\u003eADIPOQ\u003c/em\u003e gene is significantly downregulated in breast tumor cells. This leads to augmented FAS and diminished FAO, possibly favoring tumor progression and conferring poor prognosis. Similar observation about \u003cem\u003eADIPOQ\u003c/em\u003e downregulation was also reported to be linked to poor survival in BRCA patients in a previous study [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This underscores \u003cem\u003eADIPOQ\u003c/em\u003e as a potential prognostic marker.\u003c/p\u003e \u003cp\u003eDownregulation of \u003cem\u003eLIPE\u003c/em\u003e (Hormone-Sensitive Lipase) and \u003cem\u003eLPL\u003c/em\u003e (Lipoprotein Lipase) add to this tumor favoring metabolic transition by halting lipid breakdown and uptake respectively. Suppressing the expression of \u003cem\u003eLIPE\u003c/em\u003e results in diminished lipids breakdown which in turn reduces the production of fatty acids that could otherwise serve as a substrate for beta oxidation. Moreover, suppression of \u003cem\u003eLPL\u003c/em\u003e expression reduces fatty acid uptake by tumor cells compelling cellular metabolic needs to be met by glycolysis. This metabolic re-programming is in line with the findings reported by previous studies stating that increased reliance on glycolysis provides tumor cells with survival benefits in the setting of hypoxia and nutrient deprivation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The aforementioned metabolic changes caused by the downregulation of both \u003cem\u003eLIPE\u003c/em\u003e and \u003cem\u003eLPL\u003c/em\u003e leading to tumor cells becoming more reliant on glycolysis is consistent with Warburg effect, a hallmark of neoplastic cells.\u003c/p\u003e \u003cp\u003eWe also found that the expression of \u003cem\u003eSLC2A4\u003c/em\u003e (GLUT4) was downregulated in breast tumor. \u003cem\u003eSLC2A4\u003c/em\u003e is an insulin depended glucose transporter needed for internalization of glucose molecules across the cell membrane [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While this appears counter-intuitive, however, under expression of GLUT4 (K\u003csub\u003em\u003c/sub\u003e= 6.6 mM) deprives tumor cells of glucose thereby creating metabolic stress. This metabolic stress in turn leads to stabilization of hypoxia inducible factor 1α, which up regulates the expression of GLUT 1, a high affinity isoform of glucose transporter (K\u003csub\u003em\u003c/sub\u003e= 3\u0026ndash;7 mM) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This adaptive GLUT1 upregulation further helps tumor cells sustain Warburg effect by maintaining their reliance on glucose for energy generation. This helps tumor cells with proliferation, escape from immune system and developing resistance to therapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. GLUT4 downregulation, therefore, is yet another modality breast tumor cells employ to ensure glucose driven metabolic shift, nurturing tumor survival and aggressiveness.\u003c/p\u003e \u003cp\u003eThe last key gene found in the current study is \u003cem\u003eLEP\u003c/em\u003e (Leptin). \u003cem\u003eLEP\u003c/em\u003e downregulation causes perturbation in leptin signaling, imparting changes in PI3K/AKT/mTOR and JAK/STAT3 pathways, which are indispensable for tumor metabolism and sustenance. This results in increased glycolytic flux, inflammation, and aggressive tumor behavior adversely impacting the overall prognosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concomitant downregulation of \u003cem\u003eADIPOQ, LIPE, LPL, SLC2A4\u003c/em\u003e, and \u003cem\u003eLEP\u003c/em\u003e may have future clinical implications. They may serve as biomarkers for prognostication or risk stratification. In addition, the findings of this study also highlight that metabolic targeted therapies might have a role to play in the management of breast cancer. Reversing the effects of \u003cem\u003eADIPOQ\u003c/em\u003e suppression by reactivating FAO and inhibiting FAS might help overwhelm the proto-onco effects of \u003cem\u003eADIPOQ\u003c/em\u003e suppression. Targeting GLUT1 or HIF-1α may ameliorate resistance to therapy in tumors harboring Warburg effect phenomenon. In addition, metabolic reprogramming is being increasingly reported as a potential contributor to the tumor\u0026rsquo;s ability to evade immune response. Thus, targeting metabolic shifts may help intensify the efficacy of current immune therapies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, the results of this study highlight a unique metabolic pattern in breast tumor cells characterized by altered lipid and glucose metabolism. The down regulation of all the five genes (\u003cem\u003eADIPOQ, LIPE, LPL\u003c/em\u003e, \u003cem\u003eSLC2A4\u003c/em\u003e, and \u003cem\u003eLEP\u003c/em\u003e) identified in this study nurtures a metabolic milieu that supports cancer survival, proliferation, and resistance to therapy possibly conferring poor prognosis. The study also provides newer insights into the mechanisms of this metabolic reprogramming, pinpointing potential metabolic targets that can be exploited for novel therapeutic developments for breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.1.\u0026nbsp;Ethics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.2.\u0026nbsp;Consent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7.3.\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.4.\u0026nbsp;Competing interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.5.\u0026nbsp;Funding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.6.\u0026nbsp;Authors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAW conceptualized the study, conducted the data analysis, and drafted the initial manuscript. AA, AF, and MG contributed to the writing, critical review, and revision of the manuscript for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.7.\u0026nbsp;Acknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the Genomic Data Commons (GDC) and The Cancer Genome Atlas (TCGA) project for providing access to the publicly available datasets used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Med. 2021;100(33):e26896. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1097/MD.0000000000026896\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000026896\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, prognostic markers, differential expression analysis, Weighted gene co-expression network analysis (WGCNA), lipid and glucose metabolism dysregulation","lastPublishedDoi":"10.21203/rs.3.rs-6488171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6488171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBreast cancer (BRCA) is a highly heterogeneous disease, posing significant challenges in prognosis. Despite therapeutic advancements, recurrence and metastasis remain major obstacles, underscoring the need for novel prognostic markers. This study aims to identify key metabolic regulators that may serve as potential biomarkers to improve risk stratification and treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eRNA expression data from 208 tumor and 25 normal breast tissue samples were sourced from the GDC database and analyzed using R-based pipelines for differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), pathway enrichment, and survival analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDifferential expression analysis identified 1,663 dysregulated genes and WGCNA identified 59 gene modules, with the “tan” module (302 genes) significantly associated with tumor status. Cross-referencing WGCNA and differential expression results identified 254 commonly downregulated genes enriched in lipid metabolism pathways. Kaplan-Meier survival analysis highlighted five key genes (\u003cem\u003eADIPOQ, LIPE, LEP, SLC2A4\u003c/em\u003e, and \u003cem\u003eLPL\u003c/em\u003e) significantly associated with poor prognosis. The downregulation of these five genes suggests a metabolic shift in breast cancer, linking lipid and glucose metabolism dysregulation to tumor progression and poorer survival outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study identified a distinct metabolic signature in BRCA, characterized by altered lipid and glucose metabolism leading to disease progression and possibly implying poor prognosis. The identified genes may serve as novel prognostic biomarkers with potential therapeutic implications, warranting further clinical validation to enhance risk stratification and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Integrative Transcriptomic Analysis Identifies ADIPOQ, LIPE, LEP, SLC2A4, and LPL as Prognostic Metabolic Markers in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 06:41:53","doi":"10.21203/rs.3.rs-6488171/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30bb37b6-702e-4b7c-8b91-6255643b9453","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T16:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 06:41:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6488171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6488171","identity":"rs-6488171","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}