Integrative Single-Cell Transcriptomics and Co-Expression Network Analysis Identify SIMALR as a Prognostic Immune-Related LncRNA in Breast Cancer: In Silico Analysis and Validation

Integrative Single-Cell Transcriptomics and Co-Expression Network Analysis Identify SIMALR as a Prognostic Immune-Related LncRNA in Breast Cancer: In Silico Analysis and Validation | 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 Single-Cell Transcriptomics and Co-Expression Network Analysis Identify SIMALR as a Prognostic Immune-Related LncRNA in Breast Cancer: In Silico Analysis and Validation Fatemeh Balangi, Pouria Samadi, Fatemeh Maghool, Hamid Daneshvar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7739331/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose This study aimed to identify and characterize irLncRNAs associated with prognosis and immune modulation in breast cancer. Methods We integrated single-cell RNA sequencing hdWGCNA and bulk RNA-seq differential expression analysis results to identify candidate irLncRNAs. The top candidate, SIMALR, was further investigated using immune, survival, mutation analysis, and GSEA. RT-qPCR validation was performed on patient tissues. Results SIMALR was linked to favorable survival and enriched in immune pathways, including T-cell receptor signaling, Natural Killer (NK) cell cytotoxicity, and antigen processing. Pearson analysis showed co-expression of SIMALR-related genes (CD8A, CD4, TNF, LCP2, ITGB2) in key immune populations. SIMALR expression correlated with recruitment of M1 macrophages, CD8 + T cells, and memory CD4 + T cells. Mutation profiling associated SIMALR with alterations in TP53 and other cancer-related genes. RT-qPCR confirmed higher SIMALR expression in tumors. Discussion SIMALR may contribute to anti-tumor immunity, highlighting its potential as a promising biomarker and therapeutic target in breast cancer. Clinical significance Breast cancer remains a significant cause of mortality in women, and its heterogeneity complicates prognosis and treatment. Therefore, discovering novel biomarkers could improve therapeutic decisions. This study explored SIMALR, a LncRNA that contributes to cancer-associated fibroblast activity. By combining hdWGCNA network analysis, RNA-Seq data analysis, and RT-qPCR validation in tissues, we found that SIMALR expression correlates with immune cell recruitment and survival outcomes. These findings highlight its potential as a clinically relevant biomarker for determining prognosis and targeted therapeutic strategies in breast cancer. breast cancer long noncoding RNA SIMALR single-cell RNA sequencing weighted gene co-expression network analysis prognostic biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer exhibits considerable biological heterogeneity, encompassing genomic, epigenetic, and immunological diversity, and remains the most frequently diagnosed malignancy among women globally. In 2022, Breast cancer accounted for approximately 2.3 million new cases and 670,000 deaths globally. Projections estimate a 38% increase in incidence and a 68% rise in mortality by 2050, disproportionately impacting low-resource settings (Carlino et al., 2024 ; Kim et al., 2025 ). Although advances in early detection and systemic therapies have improved survival in many high-income regions, disease progression and therapeutic resistance continue to undermine outcomes (Ginsburg et al., 2020 ). Current clinical decision-making relies largely on anatomical staging systems, such as TNM classification. However, these frameworks fail to capture the molecular diversity of Breast cancer and often fail to predict therapeutic response accurately (Zubair et al., 2021 ). This limitation increases the risk of both overtreatment and resistance to therapy, highlighting the urgent need to integrate molecular biomarkers into clinical workflows (Khan et al., 2024 ; Zubair et al., 2021 ). In particular, the emergence of immune-based therapies has underscored the relevance of tumor immunobiology; however, existing biomarkers, such as Programmed Death-Ligand 1 (PD-L1) expression and tumor mutational burden, exhibit limited predictive accuracy across the heterogeneous Breast cancer landscape (Cui et al., 2023 )Consequently, the identification of robust, biologically relevant markers is critical to optimizing prognosis, refining therapeutic strategies, and advancing personalized medicine approaches in Breast cancer. Suppressor of Inflammatory Macrophage Apoptosis LncRNA (SIMALR) (also known as LINC02528), located on chromosome 6, encodes a long intergenic non-coding RNA (lincRNA) with distinct tissue-specific expression patterns. Under physiological conditions, SIMALR is selectively expressed in the testis, spleen, and peripheral blood, suggesting a potential role in immune regulation and reproductive biology (Zhang & Chen, 2023 ). Emerging evidence suggests key regulatory roles for SIMALR in diverse pathological contexts. In the context of atherosclerosis, analysis of carotid atherosclerotic plaques revealed that SIMALR modulates the apoptotic pathways of M1 macrophages by influencing the activity of the Netrin-1 molecule, thereby contributing to plaque stability (Cynn et al., 2023 ). Furthermore, through direct interaction, SIMALR enhances the GTPase activity of eukaryotic translation elongation factor 1 alpha 2 (eEF1A2) in nasopharyngeal carcinoma. This interaction promotes the translation of Integrin Subunit Beta 4 (ITGB4) and Integrin Subunit Alpha 6 (ITGA6), proteins associated with tumor progression and metastasis. Notably, elevated SIMALR expression serves as an independent prognostic factor in nasopharyngeal carcinoma, further highlighting its potential role in tumor genesis (Gong et al., 2024 ). Therefore, we performed high-dimensional weighted gene co-expression network (hdWGCNA) analysis of single-cell RNA sequencing (scRNA seq) data from breast cancer and normal tissues to obtain the immune-related gene module, followed by Differential expression analysis based on transcriptomic data from the TCGA-BRCA cohort to obtain highly expressed lncRNAs in breast cancer, and then performed Pearson statistical analysis between genes and lncRNAs to obtain a set of candidate lncRNAs that are highly associated with key immune-related genes involved in breast cancer pathogenesis. Among these, SIMALR exhibited a significant upregulation in tumor tissues and showed a strong correlation with five key immune-related genes: CD8A, CD4, Tumor Necrosis Factor (TNF), Lymphocyte Cytosolic Protein 2 (LCP2), and Integrin Subunit Beta 2 (ITGB2). We further investigated the role of SIMALR in modulating immune cell infiltration within the tumor microenvironment, its involvement in immune-related signaling pathways, and its association with patient survival outcomes and expression of other genes and mutations in various genes. Finally, we validated our in-silico findings through quantitative real-time PCR (qRT-PCR) analysis, confirming elevated expression of SIMALR in breast cancer tumor tissues relative to matched adjacent normal tissues. Materials and Methods Data Acquisition and Preprocessing scRNA-seq data utilized in this study were obtained from the Sunny et al(Wu et al., 2021 ). The dataset GSE176078, comprising 100,064 cells, was downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ). Following the selection criteria, only primary, non-metastatic, human breast cancer tissue samples were retained, resulting in a total of 26 samples for downstream analysis. The scRNA-seq dataset was processed using Seurat (Hao et al., 2024 ), with low-quality cells filtered based on gene count and mitochondrial content thresholds. After normalization and integration using the Harmony algorithm, differentially expressed genes (DEGs) were identified, and dimensionality reduction was performed. Clustering and marker identification were achieved through Seurat functions, with cell type annotation conducted via SingleR. To characterize intercellular communication, CellChat analysis was performed, revealing key ligand-receptor interactions (Jin et al., 2021 ). PCA (Principal component analysis), UMAP (Uniform Manifold Approximation and Projection) (Becht et al., 2018 ), and cell type annotation were subsequently performed to process and visualize the single-cell transcriptomic landscape. Moreover, raw transcriptomic data corresponding to normal breast tissues and breast cancer samples were retrieved from the Cancer Genome Atlas (TCGA) database. Comprehensive quality control procedures were applied, excluding samples with incomplete clinical information, low mapping quality, or abnormal expression distributions. After filtering, only those with an FDR (false discovery rate) 1 were considered statistically significant. The datasets were normalized using standard methods to minimize technical variability and to ensure comparability across all samples. hdWGCNA Implementation for CAFs hdWGCNA was performed using the hdWGCNA R package following the standard pipeline (Morabito et al., 2023 ). CAF populations were selected from the scRNA-seq data, and genes expressed in more than 5% of cells were retained. Data normalization was conducted using the SCTransform function. Pearson correlations were calculated between gene pairs, and a scale-free network was constructed using the soft-threshold power of nine as the optimal value. Adjacency and topological overlap matrices (TOM) were generated, and gene modules were identified using the dynamic Tree Cut algorithm. MEs were calculated to summarize module expression patterns, and module-trait relationships were assessed. DEGs were extracted specifically from the CAFs-M15 module, which exhibited a distinct expression pattern associated with immune cell recruitment. This module prioritized genes based on their intra-module connectivity, as determined by their kME (module eigengene-based connectivity) values. Given its unique immune-related expression profile, CAFs-M15 was selected for subsequent functional characterization and clinical association analyses. Gene Network and Functional Enrichment Analysis of CAFs-M15 To investigate the functional interactions among DEGs in CAFs-M15, protein-protein interaction (PPI) analysis was performed using the STRING database ( https://string-db.org ) with a minimum confidence score of 0.4. Following the retrieval of interaction data, the network was imported into Cytoscape software for visualization. The network was organized using a degree-sorted circular layout, allowing for the identification of key hub genes based on their degree of connectivity. This approach facilitated a systematic assessment of the molecular relationships among CAFs-M15 genes and provided insights into potential functional hubs within the tumor microenvironment. Functional enrichment of CAFs-M15 was conducted using the Enrichr R package across curated gene sets, including GO and KEGG databases. Significant enrichments were determined based on adjusted p-values ( 1 for upregulation or < -1 for downregulation and FDR < 0.05, to conduct fast preranked GSEA on ranked gene lists, identifying key pathways and biological processes associated with CAFs-M15. Identification of Immune-Associated LncRNAs via DEG Correlation Analysis To identify LncRNAs with potential involvement in immune cell recruitment in breast cancer, we first extracted differentially expressed LncRNAs (DE-LncRNAs) based on transcriptomic data from the TCGA-BRCA cohort. Differential expression analysis was conducted to compare LncRNA expression profiles between breast cancer and normal breast tissues, using DESeq2 package, and significantly dysregulated LncRNAs were retained using adjusted p-value and log fold-change thresholds. Using network analysis metrics, the top six DEGs with the highest degree centrality were selected as key immune-related genes. To uncover LncRNAs most likely involved in modulating immune recruitment, Pearson correlation analysis (|cor| >0.6, p < 0.05) was conducted between the expression levels of these hub DEGs and the DE-LncRNAs across all breast cancer samples. LncRNAs with strong and significant correlations were considered as candidate regulators within the immune microenvironment and prioritized for further investigation. Immunology Analyses In order to investigate the effect of SIMALR expression on the recruitment of various immune cells and immune-related signaling pathways, Immune cell abundance and immune gene expression data were retrieved from the ImmReg database ( http://bio-bigdata.hrbmu.edu.cn/ ). Data were visualized using the ggplot2 R packages and graphs were drawn as Heatmap and Bubble plot. Survival Analyses To investigate the effect of SIMALR expression on the survival of breast cancer patients, overall survival (OS) analysis was performed using the UCSCXenaShiny platform ( https://shiny.hiplot.cn/ ). Patients were stratified by SIMALR expression (high vs. low, median split) and SIMALR expression levels were queried in the TCGA survival analysis module, focusing on breast cancer with the Kaplan-Meier test across all different disease stages. Gene Set Enrichment Analysis GSEA was conducted using the BEST platform ( https://rookieutopia.hiplot.com.cn/ ). Genes were ranked by correlation with SIMALR expression, and enrichment analysis was performed based on KEGG, GO, and Hallmark databases. Mutation Analyses The relationship between the level of SIMALR expression (high expression and low expression) and the occurrence of mutations in other genes were examined using the BEST ( https://rookieutopia.hiplot.com.cn/ ) database. Tissue Collection and Real-Time PCR The study protocol was reviewed and approved by the Ethics Committee of Kerman University of Medical Sciences, Kerman, Iran (Approval No: IR.KMU.AH.REC.1404.002). Paired tumor and adjacent normal tissues were anonymously collected from six treatment-naïve adult patients with breast cancer referred to Poursina laboratory, Isfahan, Iran. Written informed consent was obtained from all participants prior to enrollment in the study. Participants were fully informed about the study procedures, potential risks, and their right to withdraw at any time without repercussions. All procedures were carried out in accordance with the ethical principles outlined in the Declaration of Helsinki (2024 revision) ( World Medical Association. Declaration of Helsinki , 2024). Total RNA was extracted and cDNA was synthesized using a commercial kit (Sina Clone, Iran) following the manufacturer’s protocol. Quantitative real-time PCR (Roche, USA) was then performed to assess the expression of SIMALR in tumor versus normal tissues. Expression levels were normalized to β-actin as a housekeeping gene using the Livak method (2^–ΔΔCT) (Livak & Schmittgen, 2001 ). The study was performed over seven months, from January to July 2025, with four months dedicated to tissue collection and laboratory validation. Statistical Analysis Statistical analyses were performed using R v4.2.2. Pearson correlation was used for bivariate analysis, and the Wilcoxon signed-rank test was performed for paired comparisons between tumor and normal tissues. P-value < 0.05 was considered for statistical significance. Results Identification of Key Gene Modules and Functional Pathways Related to CAF Cells Using hdWGCNA As shown in Fig.1, we first performed hdWGCNA analysis to identify immune-related genes differentially expressed in patients with breast cancer compared to healthy individuals. As illustrated in Fig.1a, during the co-expression network construction, when the scale-free topology fitness index reached 0.90, the soft threshold power (β) was 9. Fig.1b illustrates a total of 19 gene modules were identified with distinct expression patterns in breast tumors compared to normal tissues. The relationships between the modules are visualized in Fig.1c. Next, we examined the association between these modules and nine different cell populations: myeloid, healthy epithelial, tumor epithelial, plasmablast, myeloid, T cell, B cell, Cancer Associated Fibroblasts (CAFs), and perivascular-like (PVL) cells (Fig.1d). We selected CAFs and examined the module-specific expression correlations within this cell type (Fig.1e). The results of the top 10 hub genes in each module, ranked by kME (eigengene-based connectivity) analysis, indicated that module 15 contains immune-related hub genes (Fig.1f). Also, this module showed the highest correlation with CAFs compared to all other modules (Fig.1g), and Differential Module Eigengene (DME) analysis further confirmed significantly higher expression of module 15. (Fig.1h). Based on these findings, module 15 was selected for additional studies. Selection of Key Immune-Related Genes Using Network Analysis Metrics A protein-protein interaction (PPI) network identified key hub genes: CD8A, CD4, TNF, LCP2, ITGB2, and PTPRC (Fig.2a). UMAP visualization showed that CD4, TNF, LCP2, ITGB2, and PTPRC genes were highly expressed in B cells, T cells, and myeloid cells, and the expression of the CD8A gene was predominant in B cells and T cells (Fig.2b-2h). Gene Set Enrichment Analysis (GSEA) of CAFs-M15 Genes To gain insight into the biological function of CAFs-M15 module genes, we performed GSEA using GO and KEGG databases. As shown in Fig.2i, GSEA based on the GO database across three domains - Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) - revealed that in the BP domain, CAFs-M15 genes were related to leukocyte cell-cell adhesion. In the CC domain, these genes were associated with the external side of the plasma membrane, and in the MF domain, these genes were related to antigen binding. KEGG-based GSEA further confirmed associations of these genes with cell adhesion molecules, hematopoietic cell lineage, leishmaniasis, and tuberculosis (Fig.2j). Overall, GO BP-based GSEA analyses highlighted the role of CAFs-M15 genes in response to external stimuli, immune system processes, immune response, and adaptive immune responses (Fig.2k-o). Identification of DELncRNAs with Prognostic Value in Breast Cancer RNA-seq data were extracted and analyzed from the TCGA database to identify a set of DELncRNAs. As shown in the Fig.2p, eight immune-related DELncRNAs were significantly associated with the expression of five hub immune genes (CD8A, CD4, TNF, LCP2, ITGB2 (. Among them, SIMALR was selected for further investigation as potential immune-related biomarker for breast cancer diagnosis. Relationship Between SIMAR Expression and Immune Cell Recruitment As shown in Fig.3a, SIMALR was most closely related to the recruitment of activated and resting memory CD4+ T cells, CD8+ T cells, regulatory T cells, gamma delta T cells, follicular helper T cells, and M1 macrophages. In addition to breast cancer, SIMALR expression was significantly correlated with M1 macrophage recruitment in 26 other cancer types. Relationship Between SIMALR Expression and Immune System Signaling in Breast Cancer As shown in Fig.3b, SIMALR expression significantly correlates with eight immune-related signaling pathways in breast cancer. The TCR signaling pathway, natural killer (NK) cell cytotoxicity, interleukin receptor, chemokine receptors, and antigen processing and presentation showed the strongest correlations among these. Notably, SIMALR played an important role in TCR signaling, NK cell cytotoxicity, antimicrobial, and antigen processing and presentation across multiple cancers. SIMALR Expression Correlates with Breast Cancer Patient Survival Kaplan-Meier survival analysis showed that higher SIMALR expression was significantly associated with patient overall survival (P < 0.045) (Fig.3c). Functional Characterization of SIMALR by Gene Set Enrichment Analysis GO-based GSEA further highlighted the role of SIMALR in adaptive immune response, leukocyte cell adhesion, T-cell activation and proliferation, leukocyte proliferation, lymphocyte activation, Interferon gamma production, and cell killing (Fig.3d). Also, the hallmark gene set analysis in the BEST database showed a significant association of SIMALR expression with interferon-γ and α responses, allograft rejection, Interleukin-6/ Janus Kinases /Signal transducer and activator of transcription 3 (IL-6/JAK/STAT3) signaling, and inflammatory responses (Fig.3e). Based on GSEA using the BEST and KEGG databases, SIMALR expression is significantly associated with allograft rejection, autoimmune thyroid disease, graft-versus-host disease, antigen processing and presentation, intestinal immune network for immunoglobulin a production, type 1 diabetes, and Leishmania infection (Fig.3f). Pan-Cancer Analysis of SIMALR Expression According to the GEPIA database, SIMALR expression was significantly upregulated in cancer patients in 20 out of 31 cancer types, as shown in Fig.4a. However, in two cancers, Testicular Germ Cell Tumors (TCGT) and Acute Myeloid Leukemia (LAML), the expression of SIMALR was downregulated in cancer patients compared to normal individuals. In the remaining nine cancer types, no significant changes were observed. Association Between SIMALR Expression and Gene Mutations in Breast Cancer As shown in Fig.4b, increased SIMALR expression was significantly associated with increased mutation frequencies in Tumor Protein P53 (TP53), Titin (TTN), Hemicentin 1 (HMCN1), and Ryanodine Receptor 2 (RYR2), while decreased SIMALR expression was significantly associated with increased Mitogen-Activated Protein Kinase 1 (MAP3K1) mutations. SIMALR Expression in Different Types of Tumor Microenvironment Cells SIMALR was expressed in myeloid cells, B cells, T cells, plasmablasts, tumor epithelial cells, CAFs, and PVL cells. The highest expression was observed in CAFs and B cells, followed by T cells and myeloid cells (Fig.4c-4d). Laboratory Validation of SIMALR Expression In-silico findings were validated by measuring SIMALR expression in tumor and adjacent normal tissues from six breast cancer patients. As shown in Fig. 4e, SIMALR expression was significantly upregulated in tumor tissues, consistent with our in-silico findings. Discussion In this study, we integrated scRNA-seq and bulk RNA-seq to provide new evidence that SIMALR, as an irLncRNA, is significantly associated with patient survival and may serve as a novel prognostic biomarker in breast cancer. LncRNAs have emerged as essential regulators of gene expression and immune responses. Recent studies have highlighted their significance in breast cancer biology and patient prognosis (Arun & Spector, 2019; Cheetham et al., 2013; Hansji et al., 2014; Meng et al., 2014; Soudyab et al., 2016). Our study extends this foundation by identifying SIMALR as an LncRNA with the potential of modulating antitumor responses and immune cell recruitment. Furthermore, SIMALR expression correlates with immune-related genes and is associated with key immune cell populations, including M1 macrophages, CD8+ T cells, CD4+ memory T cells, and regulatory T cells. Unlike previous studies that have broadly cataloged LncRNAs in breast cancer (Gupta et al., 2010; Kim et al., 2018; Kosir et al., 2013), we used a more targeted and systematic strategy. We reduced a large set of DELncRNAs into a smaller subset relevant to the immune system by linking the results of differential expression analysis based on transcriptome data and the results of PPI analysis on gene modules obtained from hdWGCNA single-cell data. Among these, we ultimately focused on SIMALR due to its strong association with breast cancer and central immune genes. Through hdWGCNA and PPI analysis, we identified a module containing five immune genes (CD4, CD8A, TNF, LCP2, and ITGB2) that was strongly associated with CAFs, core cells in shaping the tumor microenvironment by supporting tumor cell survival, proliferation, angiogenesis, immunosuppression, and resistance to treatment (Chen et al., 2021). By examining the association of DE LncRNAs with five immune-related genes, we identified eight immune-related LncRNAs, with SIMALR showing the highest association with all five immune genes. Our ImmReg-based analyses revealed a link between SIMALR expression in breast cancer with the recruitment of CD8+ central memory T cells, CD4+ memory T cells, and M1 macrophages. Additionally, elevated expression of SIMALR was positively associated with TCR signaling pathways, NK cell cytotoxicity, interleukin and chemokine receptor signaling, antigen processing and presentation, all connected to an antitumor state in the TME. These findings suggest a role for SIMALR both in immune cell recruitment and activation in breast cancer. Notably, the effect of this lncRNA on regulatory T cells should not be overlooked. In regard to macrophages, the M1 phenotype is key to preventing cancer progression by tumor cell elimination (Shapouri-Moghaddam et al., 2018). However, in advanced tumors, cancer cells cause macrophages to switch to the M2 phenotype (Jayasingam et al., 2019; Mantovani et al., 2017), which helps tumor growth by inducing the epithelial-to-mesenchymal transition (EMT) process and maintaining stem cell characteristics (Chen et al., 2018). Also, M2 phenotype contributes to metastases by promoting angiogenesis and tissue remodeling, creating an immunosuppressive microenvironment (Lin et al., 2019; Pan et al., 2020). This is consistent with studies such as Cynn et al. [40], who found that SIMALR enhances the survival of M1-like macrophages by inhibiting apoptosis, and Li et al., who reported that expression of SIMALR in HMDMs (Human Monocyte-Derived Macrophages) enhances the inflammatory factors NTN1 and IRF4 (Li et al., 2018) and improves the function of M1 macrophages and consequently antitumor response. Our findings extend these results to breast cancer, suggesting that SIMALR may promote anti-tumor immunity by retaining M1 macrophages and T-cell responses within the tumor microenvironment (TME). Single-cell analyses showed high expression of ITGB2, LCP2, TNF, and CD4 genes in myeloid, T and B cells, as well as CD8 in B and T cells, suggesting the essential role of these genes in the communication of immune cells. Previous studies have indicated various functions for these markers in different types of breast cancer. For example, ITGB2 increases cell migration and invasion through activating FAK (Focal Adhesion Kinase) protein and stimulating Matrix Metallopeptidase 9 (MMP9) secretion (Liu et al., 2018). LCP2 indirectly affects TCR signaling and thus modulates the antitumor responses (Wang & Peng, 2021). Also, TNFα, as an essential pro-inflammatory cytokine within the TME, plays a critical role at all stages of breast cancer, including tumor cell proliferation and survival, EMT, metastasis, and recurrence.(Cruceriu et al., 2020) The expression of both CD4 and CD8 genes is linked to histological grade and the status of estrogen and progesterone receptors. CD4 expression also shows a connection with BRCA gene status. A high infiltration of CD4 and CD8 positive cells within the TME is significantly associated with reduced mortality rate in breast cancer patients with BRCA1 and BRCA2 mutations. Additionally, the presence of CD8+ cells is correlated with improved disease-free survival (Jørgensen et al., 2021). We validated that high SIMALR expression is significantly associated with improved patient survival using the Kaplan-Meier survival curve analysis. Consistently, Wei et al. identified SIMALR as a favorable prognostic marker in breast cancer by performing various survival analyses, such as univariate Cox proportional hazards regression analysis (Wei et al., 2021). Conversely, Yu and colleagues identified nineteen LncRNAs, including SIMALR, with the potential as biomarkers for breast cancer diagnosis; according to their results, high expression of SIMALR was observed in high-risk patients with poor prognosis (Yu et al., 2021). These discrepancies may reflect different roles of SIMALR across breast cancer subtypes or stages and warrant further stratified analysis. KEGG-based GSEA analysis showed an association between SIMALR expression and conditions like allograft rejection, autoimmune thyroid disease, graft-versus-host disease, antigen processing and presentation, intestinal immune network for immunoglobulin A production, type 1 diabetes, and Leishmania infection. In the context of leishmaniosis, macrophages are considered important reservoir of disease, and SIMALR might upregulate in macrophages to increase their survival during infection. Also, the study by Ye et al. confirmed that SIMALR is one of the genes highly expressed in patients with diabetes mellitus and tuberculosis, involving in various processes, including myeloid cell activation and defense response to pathogens, and is of utmost importance in host resistance to Mycobacterium tuberculosis infection (Ye et al., 2025)Totally, SIMALR can be investigated in each of the aforementioned fields and used in future studies to elucidate its function under different conditions. GSEA based on the GO database confirmed that SIMALR expression is associated with adaptive immune response, upregulation of leukocyte adhesion and proliferation, and T-cell activation and proliferation. This link revealed SIMALR as a key regulator of the immune system, especially T cells. Also, it can be concluded that SIMALR association with the aforementioned diseases is due to its effects on the immune cells. Similarly, in a study on cervical cancer, an association was reported between the expression of this LncRNA with Guanylate Binding Protein 1 (GBP1), a gene with high expression in the tumor tissues and immune cells, involved in the inflammatory processes and immune responses (Wang et al., 2024), which certainly requires more extensive studies in this field. In addition, according to mutation analyses, increased expression of SIMALR was significantly associated with increased mutations in the TP53, TTN, HMCN1, and RYR2 genes, but decreased expression of SIMALR was also significantly associated with increased MAP3K1 mutations. Among these, increased mutations in the p53 gene are of greater importance as a tumor suppressor with a central role in regulating the expression of a wide range of genes involved in apoptosis, growth, cell cycle arrest, and differentiation (Hernández Borrero & El-Deiry, 2021).In this context, additional research into the specific mutation types and the functions of the previously mentioned mutated genes may provide deeper understanding of the data. Interestingly, according to the results obtained from the GEPIA database, in addition to breast cancer, SIMALR was significantly overexpressed in 19 other cancers compared to normal. Analyzing microarray data by Gong and colleagues showed that SIMALR levels increased in nasopharyngeal carcinoma as an independent oncogene biomarker, and that increased SIMALR led to phosphorylation of the eEF1A2 factor, followed by translation of ITGB4 / ITGA6, which helps to create a malignant phenotype. They also found that in Nasopharyngeal cancer (NPC), N-acetyltransferase 10 increased SIMALR stability by changing N4-acetylcytidine (Gong et al., 2024). Notably, SIMALR has also been identified as an immune-related LncRNA with prognostic value in skin cutaneous melanoma (Ping et al., 2021). In addition, it has been suggested as a sphingolipid metabolism-related LncRNA that can be studied as a biomarker for pancreatic cancer prognosis (He et al., 2024). Therefore, research is required on the function and role of this LncRNA in other cancers. RT-qPCR validation confirmed higher SIMALR expression in breast tumors compared to the adjacent normal tissues. In this study, we examined the effect of SIMALR on patients with ductal carcinoma and mucinous carcinoma with grades 1, 2, and 3. However, further studies with a larger sample size covering different grades are needed to elucidate the potential of this irLncRNA as a diagnostic biomarker. In conclusion, SIMALR is a promising immune-related LncRNA associated with a favorable prognosis and immune cell infiltration in breast cancer. Also, its functional relevance in regulation of macrophages and T cells highlights its potential as a biomarker and therapeutic target. Given its central roles, further studies are needed to validate its subtype-specific role and mechanistic pathways. Declarations Acknowledgements This study was supported by Kerman University of Medical Sciences, Kerman, Iran (Grant number: 403000323). The authors acknowledge using OpenAI’s ChatGPT to assist with language refinement, grammar, and manuscript formatting. All scientific content, data interpretation, and conclusions are solely the authors' work. Data Availability Publicly available datasets used in this study are referenced accordingly. Additional data generated during this study are available from the corresponding authors upon reasonable request. Funding This study was supported by Kerman University of Medical Sciences, Kerman, Iran (Grant number: 403000323). Competing Interest The authors report there are no competing interests to declare. Author Contributions Conception and design: P.S. and F.S., Analysis and interpretation of the data: F.B., F.M., and E.A. H.D. and M.T., Drafting of the paper: F.B., F.M., and F.S., Revising it critically for intellectual content: P.S., E.A. H.D. and M.T. All authors approved the final manuscript and agreed to be accountable for all aspects of the work. Ethical approval The study was carried out according to the Declaration of Helsinki and was approved by the Ethics Committee of Kerman University of Medical Sciences, Kerman, Iran (Approval No: IR.KMU.AH.REC.1404.002). Consent to Participate Written informed consent was obtained from all participants prior to enrollment in the study. Permission to reproduce No materials from other sources are included in this manuscript. References Arun, G., & Spector, D. L. (2019). MALAT1 long non-coding RNA and breast cancer. RNA Biol , 16 (6), 860-863. https://doi.org/10.1080/15476286.2019.1592072 Becht, E., McInnes, L., Healy, J., Dutertre, C. A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2018). Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol . https://doi.org/10.1038/nbt.4314 Carlino, F., Solinas, C., Orditura, M., Bisceglia, M. D., Pellegrino, B., & Diana, A. (2024). Heterogeneity in breast cancer: clinical and therapeutic implications. 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Advanced approaches to breast cancer classification and diagnosis. Frontiers in Pharmacology , 11 , 632079. https://doi.org/10.3389/fphar.2020.632079 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 13 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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02:37:25","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129721,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/95a7318cbe1dec9c39c92ef1.html"},{"id":93542239,"identity":"efd1b763-77a4-4619-b0c7-7aa14d8b585f","added_by":"auto","created_at":"2025-10-15 02:37:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24419054,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key gene modules and functional pathways related to CAF cells using high-dimensional gene co-expression network analysis a) Optimal soft threshold selection b) The cluster dendrogram of co-expression genes c) Correlation between the 19 modules d) MEs (module eigengenes )in single cells e) Feature plots of 19 modules f) The top 10 hub genes in each module ranked by kME (eigengene-based connectivity) g) Expression levels of modules in different types of cell lines h) The fold-change for each of the modules (the size of each dot corresponds to the number of genes in that module. An “X” is placed over each point that does not reach statistical significance.)\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/92a54971302cec1738321c4a.png"},{"id":93543879,"identity":"af4a6baf-6751-43b8-b9fa-a0a440fcd871","added_by":"auto","created_at":"2025-10-15 02:45:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20769050,"visible":true,"origin":"","legend":"\u003cp\u003ea) Gene Network of CAFs-M15 b-h) UMAP plots of immune hub gene expression in different cells i-o) gene set enrichment analysis related to the CAFs-M15 p) Heatmap of Correlations between the immune hub genes and DELncRNAs (|cor| \u0026gt; 0.6)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/d0cb453d52bc6e59103a5cec.png"},{"id":93542242,"identity":"dd6be5f8-765b-40f9-9673-1fa48f7b84a5","added_by":"auto","created_at":"2025-10-15 02:37:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9210559,"visible":true,"origin":"","legend":"\u003cp\u003ea) SIMALR's association with immune system cells b)\u003cstrong\u003e \u003c/strong\u003eCorrelation of SIMALR with signaling pathways related to the immune system c) Relationship between SIMALR expression and patient survival d-f) Gene set enrichment analysis for SIMALR\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/ff582cef6728fa817f586798.png"},{"id":93543881,"identity":"80ac2308-1f1a-40ea-883d-e5866caa8ba8","added_by":"auto","created_at":"2025-10-15 02:45:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7807759,"visible":true,"origin":"","legend":"\u003cp\u003ea)\u003cstrong\u003e \u003c/strong\u003eExamination of SIMALR expression in different types of cancer b) Correlation of SIMALR gene expression with mutation in other genes in breast cancer c-d) SIMALR expression in different types of tumor microenvironment cells e) Comparison of SIMALR expression in tumor and normal tissue\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/c1f3913a3c6cc184bf02cadc.png"},{"id":93544619,"identity":"6ced1286-b54d-4ff5-8287-7417e75d3007","added_by":"auto","created_at":"2025-10-15 02:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":60577626,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7739331/v1/f8109fd4-96c2-43c5-8578-1099d7a48298.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Single-Cell Transcriptomics and Co-Expression Network Analysis Identify SIMALR as a Prognostic Immune-Related LncRNA in Breast Cancer: In Silico Analysis and Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer exhibits considerable biological heterogeneity, encompassing genomic, epigenetic, and immunological diversity, and remains the most frequently diagnosed malignancy among women globally. In 2022, Breast cancer accounted for approximately 2.3\u0026nbsp;million new cases and 670,000 deaths globally. Projections estimate a 38% increase in incidence and a 68% rise in mortality by 2050, disproportionately impacting low-resource settings (Carlino et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although advances in early detection and systemic therapies have improved survival in many high-income regions, disease progression and therapeutic resistance continue to undermine outcomes (Ginsburg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCurrent clinical decision-making relies largely on anatomical staging systems, such as TNM classification. However, these frameworks fail to capture the molecular diversity of Breast cancer and often fail to predict therapeutic response accurately (Zubair et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This limitation increases the risk of both overtreatment and resistance to therapy, highlighting the urgent need to integrate molecular biomarkers into clinical workflows (Khan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zubair et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, the emergence of immune-based therapies has underscored the relevance of tumor immunobiology; however, existing biomarkers, such as Programmed Death-Ligand 1 (PD-L1) expression and tumor mutational burden, exhibit limited predictive accuracy across the heterogeneous Breast cancer landscape (Cui et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)Consequently, the identification of robust, biologically relevant markers is critical to optimizing prognosis, refining therapeutic strategies, and advancing personalized medicine approaches in Breast cancer.\u003c/p\u003e\u003cp\u003eSuppressor of Inflammatory Macrophage Apoptosis LncRNA (SIMALR) (also known as LINC02528), located on chromosome 6, encodes a long intergenic non-coding RNA (lincRNA) with distinct tissue-specific expression patterns. Under physiological conditions, SIMALR is selectively expressed in the testis, spleen, and peripheral blood, suggesting a potential role in immune regulation and reproductive biology (Zhang \u0026amp; Chen, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Emerging evidence suggests key regulatory roles for SIMALR in diverse pathological contexts. In the context of atherosclerosis, analysis of carotid atherosclerotic plaques revealed that SIMALR modulates the apoptotic pathways of M1 macrophages by influencing the activity of the Netrin-1 molecule, thereby contributing to plaque stability (Cynn et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, through direct interaction, SIMALR enhances the GTPase activity of eukaryotic translation elongation factor 1 alpha 2 (eEF1A2) in nasopharyngeal carcinoma. This interaction promotes the translation of Integrin Subunit Beta 4 (ITGB4) and Integrin Subunit Alpha 6 (ITGA6), proteins associated with tumor progression and metastasis. Notably, elevated SIMALR expression serves as an independent prognostic factor in nasopharyngeal carcinoma, further highlighting its potential role in tumor genesis (Gong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, we performed high-dimensional weighted gene co-expression network (hdWGCNA) analysis of single-cell RNA sequencing (scRNA seq) data from breast cancer and normal tissues to obtain the immune-related gene module, followed by Differential expression analysis based on transcriptomic data from the TCGA-BRCA cohort to obtain highly expressed lncRNAs in breast cancer, and then performed Pearson statistical analysis between genes and lncRNAs to obtain a set of candidate lncRNAs that are highly associated with key immune-related genes involved in breast cancer pathogenesis. Among these, SIMALR exhibited a significant upregulation in tumor tissues and showed a strong correlation with five key immune-related genes: CD8A, CD4, Tumor Necrosis Factor (TNF), Lymphocyte Cytosolic Protein 2 (LCP2), and Integrin Subunit Beta 2 (ITGB2). We further investigated the role of SIMALR in modulating immune cell infiltration within the tumor microenvironment, its involvement in immune-related signaling pathways, and its association with patient survival outcomes and expression of other genes and mutations in various genes. Finally, we validated our in-silico findings through quantitative real-time PCR (qRT-PCR) analysis, confirming elevated expression of SIMALR in breast cancer tumor tissues relative to matched adjacent normal tissues.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Acquisition and Preprocessing\u003c/h2\u003e\u003cp\u003escRNA-seq data utilized in this study were obtained from the Sunny et al(Wu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The dataset GSE176078, comprising 100,064 cells, was downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Following the selection criteria, only primary, non-metastatic, human breast cancer tissue samples were retained, resulting in a total of 26 samples for downstream analysis. The scRNA-seq dataset was processed using Seurat (Hao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with low-quality cells filtered based on gene count and mitochondrial content thresholds. After normalization and integration using the Harmony algorithm, differentially expressed genes (DEGs) were identified, and dimensionality reduction was performed. Clustering and marker identification were achieved through Seurat functions, with cell type annotation conducted via SingleR. To characterize intercellular communication, CellChat analysis was performed, revealing key ligand-receptor interactions (Jin et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). PCA (Principal component analysis), UMAP (Uniform Manifold Approximation and Projection) (Becht et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and cell type annotation were subsequently performed to process and visualize the single-cell transcriptomic landscape.\u003c/p\u003e\u003cp\u003eMoreover, raw transcriptomic data corresponding to normal breast tissues and breast cancer samples were retrieved from the Cancer Genome Atlas (TCGA) database. Comprehensive quality control procedures were applied, excluding samples with incomplete clinical information, low mapping quality, or abnormal expression distributions. After filtering, only those with an FDR (false discovery rate)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an absolute log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered statistically significant. The datasets were normalized using standard methods to minimize technical variability and to ensure comparability across all samples.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ehdWGCNA Implementation for CAFs\u003c/h3\u003e\n\u003cp\u003ehdWGCNA was performed using the hdWGCNA R package following the standard pipeline (Morabito et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CAF populations were selected from the scRNA-seq data, and genes expressed in more than 5% of cells were retained. Data normalization was conducted using the SCTransform function. Pearson correlations were calculated between gene pairs, and a scale-free network was constructed using the soft-threshold power of nine as the optimal value. Adjacency and topological overlap matrices (TOM) were generated, and gene modules were identified using the dynamic Tree Cut algorithm.\u003c/p\u003e\u003cp\u003eMEs were calculated to summarize module expression patterns, and module-trait relationships were assessed. DEGs were extracted specifically from the CAFs-M15 module, which exhibited a distinct expression pattern associated with immune cell recruitment. This module prioritized genes based on their intra-module connectivity, as determined by their kME (module eigengene-based connectivity) values. Given its unique immune-related expression profile, CAFs-M15 was selected for subsequent functional characterization and clinical association analyses.\u003c/p\u003e\n\u003ch3\u003eGene Network and Functional Enrichment Analysis of CAFs-M15\u003c/h3\u003e\n\u003cp\u003eTo investigate the functional interactions among DEGs in CAFs-M15, protein-protein interaction (PPI) analysis was performed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a minimum confidence score of 0.4. Following the retrieval of interaction data, the network was imported into Cytoscape software for visualization. The network was organized using a degree-sorted circular layout, allowing for the identification of key hub genes based on their degree of connectivity. This approach facilitated a systematic assessment of the molecular relationships among CAFs-M15 genes and provided insights into potential functional hubs within the tumor microenvironment.\u003c/p\u003e\u003cp\u003eFunctional enrichment of CAFs-M15 was conducted using the Enrichr R package across curated gene sets, including GO and KEGG databases. Significant enrichments were determined based on adjusted p-values (\u0026lt;\u0026thinsp;0.05). Additionally, Fast gene set enrichment analysis (fgsea) was performed using criteria of log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 for upregulation or \u0026lt; -1 for downregulation and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, to conduct fast preranked GSEA on ranked gene lists, identifying key pathways and biological processes associated with CAFs-M15.\u003c/p\u003e\n\u003ch3\u003eIdentification of Immune-Associated LncRNAs via DEG Correlation Analysis\u003c/h3\u003e\n\u003cp\u003eTo identify LncRNAs with potential involvement in immune cell recruitment in breast cancer, we first extracted differentially expressed LncRNAs (DE-LncRNAs) based on transcriptomic data from the TCGA-BRCA cohort. Differential expression analysis was conducted to compare LncRNA expression profiles between breast cancer and normal breast tissues, using DESeq2 package, and significantly dysregulated LncRNAs were retained using adjusted p-value and log fold-change thresholds.\u003c/p\u003e\u003cp\u003eUsing network analysis metrics, the top six DEGs with the highest degree centrality were selected as key immune-related genes. To uncover LncRNAs most likely involved in modulating immune recruitment, Pearson correlation analysis (|cor| \u0026gt;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was conducted between the expression levels of these hub DEGs and the DE-LncRNAs across all breast cancer samples. LncRNAs with strong and significant correlations were considered as candidate regulators within the immune microenvironment and prioritized for further investigation.\u003c/p\u003e\n\u003ch3\u003eImmunology Analyses\u003c/h3\u003e\n\u003cp\u003eIn order to investigate the effect of SIMALR expression on the recruitment of various immune cells and immune-related signaling pathways, Immune cell abundance and immune gene expression data were retrieved from the ImmReg database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.hrbmu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.hrbmu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data were visualized using the ggplot2 R packages and graphs were drawn as Heatmap and Bubble plot.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSurvival Analyses\u003c/h2\u003e\u003cp\u003eTo investigate the effect of SIMALR expression on the survival of breast cancer patients, overall survival (OS) analysis was performed using the UCSCXenaShiny platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shiny.hiplot.cn/\u003c/span\u003e\u003cspan address=\"https://shiny.hiplot.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patients were stratified by SIMALR expression (high vs. low, median split) and SIMALR expression levels were queried in the TCGA survival analysis module, focusing on breast cancer with the Kaplan-Meier test across all different disease stages.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGene Set Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eGSEA was conducted using the BEST platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rookieutopia.hiplot.com.cn/\u003c/span\u003e\u003cspan address=\"https://rookieutopia.hiplot.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Genes were ranked by correlation with SIMALR expression, and enrichment analysis was performed based on KEGG, GO, and Hallmark databases.\u003c/p\u003e\n\u003ch3\u003eMutation Analyses\u003c/h3\u003e\n\u003cp\u003eThe relationship between the level of SIMALR expression (high expression and low expression) and the occurrence of mutations in other genes were examined using the BEST (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rookieutopia.hiplot.com.cn/\u003c/span\u003e\u003cspan address=\"https://rookieutopia.hiplot.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTissue Collection and Real-Time PCR\u003c/h2\u003e\u003cp\u003e The study protocol was reviewed and approved by the Ethics Committee of Kerman University of Medical Sciences, Kerman, Iran (Approval No: IR.KMU.AH.REC.1404.002). Paired tumor and adjacent normal tissues were anonymously collected from six treatment-na\u0026iuml;ve adult patients with breast cancer referred to Poursina laboratory, Isfahan, Iran. Written informed consent was obtained from all participants prior to enrollment in the study. Participants were fully informed about the study procedures, potential risks, and their right to withdraw at any time without repercussions. All procedures were carried out in accordance with the ethical principles outlined in the Declaration of Helsinki (2024 revision) (\u003cem\u003eWorld Medical Association. Declaration of Helsinki\u003c/em\u003e, 2024).\u003c/p\u003e\u003cp\u003eTotal RNA was extracted and cDNA was synthesized using a commercial kit (Sina Clone, Iran) following the manufacturer\u0026rsquo;s protocol. Quantitative real-time PCR (Roche, USA) was then performed to assess the expression of SIMALR in tumor versus normal tissues. Expression levels were normalized to β-actin as a housekeeping gene using the Livak method (2^\u0026ndash;ΔΔCT) (Livak \u0026amp; Schmittgen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study was performed over seven months, from January to July 2025, with four months dedicated to tissue collection and laboratory validation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R v4.2.2. Pearson correlation was used for bivariate analysis, and the Wilcoxon signed-rank test was performed for paired comparisons between tumor and normal tissues. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered for statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of Key Gene Modules and Functional Pathways Related to CAF Cells Using hdWGCNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.1, we first performed hdWGCNA analysis to identify immune-related genes differentially expressed in patients with breast cancer compared to healthy individuals. As illustrated in Fig.1a, during the co-expression network construction, when the scale-free topology fitness index reached 0.90, the soft threshold power (\u0026beta;) was 9.\u003c/p\u003e\n\u003cp\u003eFig.1b illustrates a total of 19 gene modules were identified with distinct expression patterns in breast tumors compared to normal tissues. The relationships between the modules are visualized in Fig.1c.\u003c/p\u003e\n\u003cp\u003eNext, we examined the association between these modules and nine different cell populations: \u0026nbsp;myeloid, healthy epithelial, tumor epithelial, plasmablast, myeloid, T cell, B cell, Cancer Associated Fibroblasts (CAFs), and perivascular-like (PVL) cells (Fig.1d). We selected CAFs and examined the module-specific expression correlations within this cell type (Fig.1e). The results of the top 10 hub genes in each module, ranked by kME (eigengene-based connectivity) analysis, indicated that module 15 contains immune-related hub genes (Fig.1f). Also, this module showed the highest correlation with CAFs compared to all other modules (Fig.1g), and Differential Module Eigengene (DME) analysis further confirmed significantly higher expression of module 15. (Fig.1h). Based on these findings, module 15 was selected for additional studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of Key Immune-Related Genes Using Network Analysis Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA protein-protein interaction (PPI) network identified key hub genes: CD8A, CD4, TNF, LCP2, ITGB2, and PTPRC (Fig.2a). UMAP visualization showed that CD4, TNF, LCP2, ITGB2, and PTPRC genes were highly expressed in B cells, T cells, and myeloid cells, and the expression of the CD8A gene was predominant in B cells and T cells (Fig.2b-2h).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis (GSEA) of CAFs-M15 Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gain insight into the biological function of CAFs-M15 module genes, we performed GSEA using GO and KEGG databases. As shown in Fig.2i, GSEA based on the GO database across three domains - Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) - revealed that in the BP domain, CAFs-M15 genes were related to leukocyte cell-cell adhesion. In the CC domain, these genes were associated with the external side of the plasma membrane, and in the MF domain, these genes were related to antigen binding.\u003c/p\u003e\n\u003cp\u003eKEGG-based GSEA further confirmed associations of these genes with cell adhesion molecules, hematopoietic cell lineage, leishmaniasis, and tuberculosis (Fig.2j). Overall, GO BP-based GSEA analyses highlighted the role of CAFs-M15 genes in response to external stimuli, immune system processes, immune response, and adaptive immune responses (Fig.2k-o).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DELncRNAs with Prognostic Value in Breast Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq data were extracted and analyzed from the TCGA database to identify a set of DELncRNAs. As shown in the Fig.2p, eight immune-related DELncRNAs were significantly associated with the expression of five hub immune genes (CD8A, CD4, TNF, LCP2, ITGB2 (. Among them, SIMALR was selected for further investigation as potential immune-related biomarker for breast cancer diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between SIMAR Expression and Immune Cell Recruitment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.3a, SIMALR was most closely related to the recruitment of activated and resting memory CD4+ T cells, CD8+ T cells, regulatory T cells, gamma delta T cells, follicular helper T cells, and M1 macrophages. In addition to breast cancer, SIMALR expression was significantly correlated with M1 macrophage recruitment in 26 other cancer types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship Between SIMALR Expression and Immune System Signaling in Breast Cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.3b, SIMALR expression significantly correlates with eight immune-related signaling pathways in breast cancer. The TCR signaling pathway, natural killer (NK) cell cytotoxicity, interleukin receptor, chemokine receptors, and antigen processing and presentation showed the strongest correlations among these. Notably, SIMALR played an important role in TCR signaling, NK cell cytotoxicity, antimicrobial, and antigen processing and presentation across multiple cancers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSIMALR Expression Correlates with Breast Cancer Patient Survival \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis showed that higher SIMALR expression was significantly associated with patient overall survival (P \u0026lt; 0.045) (Fig.3c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Characterization of SIMALR by Gene Set Enrichment Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO-based GSEA further highlighted the role of SIMALR in adaptive immune response, leukocyte cell adhesion, T-cell activation and proliferation, leukocyte proliferation, lymphocyte activation, Interferon gamma production, and cell killing (Fig.3d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, the hallmark gene set analysis in the BEST database showed a significant association of SIMALR expression with interferon-\u0026gamma; and \u0026alpha; responses, allograft rejection, Interleukin-6/ Janus Kinases /Signal transducer and activator of transcription 3 (IL-6/JAK/STAT3) signaling, and inflammatory responses (Fig.3e).\u003c/p\u003e\n\u003cp\u003eBased on GSEA using the BEST and KEGG databases, SIMALR expression is significantly associated with allograft rejection, autoimmune thyroid disease, graft-versus-host disease, antigen processing and presentation, intestinal immune network for immunoglobulin a production, type 1 diabetes, and Leishmania infection (Fig.3f).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePan-Cancer Analysis of SIMALR Expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the GEPIA database, SIMALR expression was significantly upregulated in cancer patients in 20 out of 31 cancer types, as shown in Fig.4a. However, in two cancers, Testicular Germ Cell Tumors (TCGT) and Acute Myeloid Leukemia (LAML), the expression of SIMALR was downregulated in cancer patients compared to normal individuals. In the remaining nine cancer types, no significant changes were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between SIMALR Expression and Gene Mutations in Breast Cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.4b, increased SIMALR expression was significantly associated with increased mutation frequencies in Tumor Protein P53 (TP53), Titin (TTN), Hemicentin 1 (HMCN1), and Ryanodine Receptor 2 (RYR2), while decreased SIMALR expression was significantly associated with increased Mitogen-Activated Protein Kinase 1 (MAP3K1) mutations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSIMALR Expression in Different Types of Tumor Microenvironment Cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSIMALR was expressed in myeloid cells, B cells, T cells, plasmablasts, tumor epithelial cells, CAFs, and PVL cells. The highest expression was observed in CAFs and B cells, followed by T cells and myeloid cells (Fig.4c-4d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Validation of SIMALR Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn-silico findings were validated by measuring SIMALR expression in tumor and adjacent normal tissues from six breast cancer patients. As shown in Fig. 4e, SIMALR expression was significantly upregulated in tumor tissues, consistent with our in-silico findings.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we integrated scRNA-seq and bulk RNA-seq to provide new evidence that SIMALR, as an irLncRNA, is significantly associated with patient survival and may serve as a novel prognostic biomarker in breast cancer. LncRNAs have emerged as essential regulators of gene expression and immune responses. Recent studies have highlighted their significance in breast cancer biology and patient prognosis (Arun \u0026amp; Spector, 2019; Cheetham et al., 2013; Hansji et al., 2014; Meng et al., 2014; Soudyab et al., 2016). Our study extends this foundation by identifying SIMALR as an LncRNA with the potential of modulating antitumor responses and immune cell recruitment. Furthermore, SIMALR expression correlates with immune-related genes and is associated with key immune cell populations, including M1 macrophages, CD8+ T cells, CD4+ memory T cells, and regulatory T cells. Unlike previous studies that have broadly cataloged LncRNAs in breast cancer (Gupta et al., 2010; Kim et al., 2018; Kosir et al., 2013), we used a more targeted and systematic strategy. We reduced a large set of DELncRNAs into a smaller subset relevant to the immune system by linking the results of differential expression analysis based on transcriptome data and the results of PPI analysis on gene modules obtained from hdWGCNA single-cell data. Among these, we ultimately focused on SIMALR due to its strong association with breast cancer and central immune genes.\u003c/p\u003e\n\u003cp\u003eThrough hdWGCNA and PPI analysis, we identified a module containing five immune genes (CD4, CD8A, TNF, LCP2, and ITGB2) that was strongly associated with CAFs, core cells in shaping the tumor microenvironment by supporting tumor cell survival, proliferation, angiogenesis, immunosuppression, and resistance to treatment (Chen et al., 2021). By examining the association of DE LncRNAs with five immune-related genes, we identified eight immune-related LncRNAs, with SIMALR showing the highest association with all five immune genes.\u003c/p\u003e\n\u003cp\u003eOur ImmReg-based analyses revealed a link between SIMALR expression in breast cancer with the recruitment of CD8+ central memory T cells, CD4+ memory T cells, and M1 macrophages. Additionally, elevated expression of SIMALR was positively associated with TCR signaling pathways, NK cell cytotoxicity, interleukin and chemokine receptor signaling, antigen processing and presentation, all connected to an antitumor state in the TME. These findings suggest a role for SIMALR both in immune cell recruitment and activation in breast cancer.\u0026nbsp;Notably, the effect of this lncRNA on regulatory T cells should not be overlooked.\u003c/p\u003e\n\u003cp\u003eIn regard to macrophages, the M1 phenotype is key to preventing cancer progression by tumor cell elimination\u0026nbsp;(Shapouri-Moghaddam et al., 2018). However, in advanced tumors, cancer cells cause macrophages to switch to the M2 phenotype (Jayasingam et al., 2019; Mantovani et al., 2017), which helps tumor growth by inducing the epithelial-to-mesenchymal transition (EMT) process and maintaining stem cell characteristics (Chen et al., 2018). Also, M2 phenotype contributes to metastases by promoting angiogenesis and tissue remodeling, creating an immunosuppressive microenvironment (Lin et al., 2019; Pan et al., 2020). This is consistent with studies such as Cynn et al. [40], who found that SIMALR enhances the survival of \u0026nbsp;M1-like macrophages by inhibiting apoptosis, and Li et al., who reported that expression of SIMALR in HMDMs (Human Monocyte-Derived Macrophages) enhances the inflammatory factors NTN1 and IRF4 (Li et al., 2018) and improves the function of M1 macrophages and consequently antitumor response. Our findings extend these results to breast cancer, suggesting that SIMALR may promote anti-tumor immunity by retaining M1 macrophages and T-cell responses within the tumor microenvironment (TME).\u003c/p\u003e\n\u003cp\u003eSingle-cell analyses showed high expression of ITGB2, LCP2, TNF, and CD4 genes in myeloid, T and B cells, as well as CD8 in B and T cells, suggesting the essential role of these genes in the communication of immune cells. Previous studies have indicated various functions for these markers in different types of breast cancer. For example, ITGB2 increases cell migration and invasion through activating FAK (Focal Adhesion Kinase) protein and stimulating Matrix Metallopeptidase 9 (MMP9) secretion (Liu et al., 2018). LCP2 indirectly affects TCR signaling and thus modulates the antitumor responses (Wang \u0026amp; Peng, 2021). Also, TNFα, as an essential pro-inflammatory cytokine within the TME, plays a critical role at all stages of breast cancer, including tumor cell proliferation and survival, EMT, metastasis, and recurrence.(Cruceriu et al., 2020) The expression of both CD4 and CD8 genes is linked to histological grade and the status of estrogen and progesterone receptors. CD4 expression also shows a connection with BRCA gene status. A high infiltration of CD4 and CD8 positive cells within the TME is significantly associated with reduced mortality rate in breast cancer patients with BRCA1 and BRCA2 mutations. Additionally, the presence of CD8+ cells is correlated with improved disease-free survival (Jørgensen et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe validated that high SIMALR expression is significantly associated with improved patient survival using the Kaplan-Meier survival curve analysis. Consistently, Wei et al. identified SIMALR as a favorable prognostic marker in breast cancer by performing various survival analyses, such as univariate Cox proportional hazards regression analysis (Wei et al., 2021). Conversely, Yu and colleagues identified nineteen LncRNAs, including SIMALR, with the potential as biomarkers for breast cancer diagnosis; according to their results, high expression of SIMALR was observed in high-risk patients with poor prognosis (Yu et al., 2021). These discrepancies may reflect different roles of SIMALR across breast cancer subtypes or stages and warrant further stratified analysis.\u003c/p\u003e\n\u003cp\u003eKEGG-based GSEA analysis showed an association between SIMALR expression and conditions like allograft rejection, autoimmune thyroid disease, graft-versus-host disease, antigen processing and presentation, intestinal immune network for immunoglobulin A production, type 1 diabetes, and Leishmania infection. In the context of leishmaniosis, macrophages are considered\u0026nbsp;important reservoir of disease, and SIMALR might upregulate in macrophages to increase their survival during infection. Also, the study by Ye et al. confirmed that SIMALR is one of the genes highly expressed in patients with diabetes mellitus and tuberculosis, involving in various processes, including myeloid cell activation and defense response to pathogens, and is of utmost importance in host resistance to Mycobacterium tuberculosis infection (Ye et al., 2025)Totally, SIMALR can be investigated in each of the aforementioned fields and used in future studies to elucidate its function under different conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSEA based on the GO database confirmed that SIMALR expression is associated with adaptive immune response, upregulation of leukocyte adhesion and proliferation, and T-cell activation and proliferation. This link revealed SIMALR as a key regulator of the immune system, especially T cells. Also, it can be concluded that SIMALR association with the aforementioned diseases is due to its effects on the immune cells. Similarly, in a study on cervical cancer, an association was reported between the expression of this LncRNA with Guanylate Binding Protein 1 (GBP1), a gene with high expression in the tumor tissues and immune cells, involved in the inflammatory processes and immune responses (Wang et al., 2024),\u0026nbsp;which certainly requires more extensive studies in this field.\u003c/p\u003e\n\u003cp\u003eIn addition, according to mutation analyses, increased expression of SIMALR was significantly associated with increased mutations in the TP53, TTN, HMCN1, and RYR2 genes, but decreased expression of SIMALR was also significantly associated with increased MAP3K1 mutations. Among these, increased mutations in the p53 gene are of greater importance as a tumor suppressor with a central role in regulating the expression of a wide range of genes involved in apoptosis, growth, cell cycle arrest, and differentiation (Hernández Borrero \u0026amp; El-Deiry, 2021).In this context, additional research into the specific mutation types and the functions of the previously mentioned mutated genes may provide deeper understanding of the data.\u003c/p\u003e\n\u003cp\u003eInterestingly, according to the results obtained from the GEPIA database, in addition to breast cancer, SIMALR was significantly overexpressed in 19 other cancers compared to normal. Analyzing microarray data by Gong and colleagues showed that SIMALR levels increased in nasopharyngeal carcinoma as an independent oncogene biomarker, and that increased SIMALR led to phosphorylation of the eEF1A2 factor, followed by translation of ITGB4 / ITGA6, which helps to create a malignant phenotype. They also found that in Nasopharyngeal cancer (NPC), N-acetyltransferase 10 increased SIMALR stability by changing N4-acetylcytidine (Gong et al., 2024). Notably, SIMALR has also been identified as an immune-related LncRNA with prognostic value in skin cutaneous melanoma (Ping et al., 2021). In addition, it has been suggested as a sphingolipid metabolism-related LncRNA that can be studied as a biomarker for pancreatic cancer prognosis (He et al., 2024).\u0026nbsp;Therefore, research is required on the function and role of this LncRNA in other cancers.\u003c/p\u003e\n\u003cp\u003eRT-qPCR validation confirmed higher SIMALR expression in breast tumors compared to the adjacent normal tissues. In this study, we examined the effect of SIMALR on patients with ductal carcinoma and mucinous carcinoma with grades 1, 2, and 3. However, further studies with a larger sample size covering different grades are needed to elucidate the potential of this irLncRNA as a diagnostic biomarker.\u003c/p\u003e\n\u003cp\u003eIn conclusion, SIMALR is a promising immune-related LncRNA associated with a favorable prognosis and immune cell infiltration in breast cancer. Also, its functional relevance in regulation of macrophages and T cells highlights its potential as a biomarker and therapeutic target. Given its central roles, further studies are needed to validate its subtype-specific role and mechanistic pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Kerman University of Medical Sciences, Kerman, Iran (Grant number: 403000323).\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge using OpenAI\u0026rsquo;s ChatGPT to assist with language refinement, grammar, and manuscript formatting. All scientific content, data interpretation, and conclusions are solely the authors\u0026apos; work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets used in this study are referenced accordingly. Additional data generated during this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Kerman University of Medical Sciences, Kerman, Iran (Grant number: 403000323).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: P.S. and F.S., Analysis and interpretation of the data: F.B., F.M., and E.A. H.D. and M.T., Drafting of the paper: F.B., F.M., and F.S., Revising it critically for intellectual content: P.S., E.A. H.D. and M.T. All authors approved the final manuscript and agreed to be accountable for all aspects of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The study was carried out according to the Declaration of Helsinki and was approved by the Ethics Committee of Kerman University of Medical Sciences, Kerman, Iran (Approval No: IR.KMU.AH.REC.1404.002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to enrollment in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo materials from other sources are included in this manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eArun, G., \u0026amp; Spector, D. 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Advanced approaches to breast cancer classification and diagnosis. \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e, 632079. https://doi.org/10.3389/fphar.2020.632079\u003cem\u003e\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"breast cancer, long noncoding RNA, SIMALR, single-cell RNA sequencing, weighted gene co-expression network analysis, prognostic biomarker","lastPublishedDoi":"10.21203/rs.3.rs-7739331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7739331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to identify and characterize irLncRNAs associated with prognosis and immune modulation in breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe integrated single-cell RNA sequencing hdWGCNA and bulk RNA-seq differential expression analysis results to identify candidate irLncRNAs. The top candidate, SIMALR, was further investigated using immune, survival, mutation analysis, and GSEA. RT-qPCR validation was performed on patient tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSIMALR was linked to favorable survival and enriched in immune pathways, including T-cell receptor signaling, Natural Killer (NK) cell cytotoxicity, and antigen processing. Pearson analysis showed co-expression of SIMALR-related genes (CD8A, CD4, TNF, LCP2, ITGB2) in key immune populations. SIMALR expression correlated with recruitment of M1 macrophages, CD8 + T cells, and memory CD4 + T cells. Mutation profiling associated SIMALR with alterations in TP53 and other cancer-related genes. RT-qPCR confirmed higher SIMALR expression in tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSIMALR may contribute to anti-tumor immunity, highlighting its potential as a promising biomarker and therapeutic target in breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical significance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreast cancer remains a significant cause of mortality in women, and its heterogeneity complicates prognosis and treatment. Therefore, discovering novel biomarkers could improve therapeutic decisions. This study explored SIMALR, a LncRNA that contributes to cancer-associated fibroblast activity. By combining hdWGCNA network analysis, RNA-Seq data analysis, and RT-qPCR validation in tissues, we found that SIMALR expression correlates with immune cell recruitment and survival outcomes. These findings highlight its potential as a clinically relevant biomarker for determining prognosis and targeted therapeutic strategies in breast cancer.\u003c/p\u003e","manuscriptTitle":"Integrative Single-Cell Transcriptomics and Co-Expression Network Analysis Identify SIMALR as a Prognostic Immune-Related LncRNA in Breast Cancer: In Silico Analysis and Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:37:17","doi":"10.21203/rs.3.rs-7739331/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T10:43:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-13T09:04:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T02:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84096805603741359181684994794461601818","date":"2025-11-02T03:12:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125698614648521725188921158728246957460","date":"2025-10-29T15:05:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T18:42:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T10:00:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T09:57:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2025-09-29T07:43:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fb823b08-44b1-45f6-a07c-544c66572a4c","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T14:09:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:37:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7739331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7739331","identity":"rs-7739331","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}