Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma

Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma | 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 Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma YouXin Tang, Peng Zhang, Yuan Yuan, JunXi Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444169/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 As the predominant subtype of breast cancer, invasive ductal carcinoma (IDC) is characterized by its aggressive invasive behavior and strong metastatic capacity. Exercise has been shown to confer multiple benefits in cancer prevention. This research sought elucidate the exercise-related mechanisms in IDC, emphasizing risk stratification therapeutic implications. Methods IDC-related datasets downloaded were from the gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases. Differential expression analysis, Cox univariable survival analysis, and machine learning methods were used to select exercise-related genes (ERGs) and construct a risk model. Subsequently, the prognostic evaluations were enhanced through independent survival analysis, nomogram development, enrichment profiling, tumor immune microenvironment assessment, and chemosensitivity testing. Besides, GSE195861 was analyzed to determine key cells and perform pseudo-time and cell communication analyses. Finally, Prognostic ERG gene expression was confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results A prognostic risk model with 8 prognostic ERGs (TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16) was constructed and demonstrated a strong prognostic effect. Subsequently, a nomogram was developed according to tumor stage and gender, showing strong predictive power for IDC prognosis. Subsequently, immune cells like immature B cells, pathways like hematopoietic cell lineage, and drug sensitivities to GW-441756 were detected to be linked to the risk stratification of IDC patients. Moreover, pseudo-time analysis revealed a notable correlation between prognostic ERGs' expression about differentiation status of key cells (NK cells and B cells), and cell signaling revealed key cell-macrophage interplay. Importantly, RT-qPCR confirmed that PGK1, SCG2, CALM2, and PHKA1 were abundantly expressed, while GYPC and IL16 were lowly expressed in IDC patients. Conclusion This study highlighted the pivotal role of exercise in IDC progression. A novel IDC-related risk model based on prognostic ERGs was developed and validated, and it exhibited robust predictive efficacy for IDC patient outcomes. Invasive ductal carcinoma Exercise Single-cell RNA sequencing Bulk RNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Background Per the GLOBOCAN 2022 report on global cancer statistics, breast cancer (BRCA) has emerged as the most prevalent malignancy among women globally, leading both in incidence and mortality rates [ 1 ]. As a highly heterogeneous tumor, the prognosis of BRCA patients varies significantly among its molecular subtypes [ 2 ]. Invasive ductal carcinoma (IDC), the most common histological variant, constitutes 70–75% of all cases [ 3 , 4 ]. As the predominant form of BRCA [ 5 ], IDC specifically breaches the basement membrane and spreads to remote locations, distinguishing it from ductal carcinoma in situ (DCIS). This aggressive, metastatic, and invasive phenotype of IDC is a primary driver of BRCA-related fatalities. Cancer invasion and metastasis, orchestrated by intricate molecular mechanisms—including oncogene activation, tumor suppressor gene silencing, and dysregulated signaling pathways—remain enigmatic [ 6 , 7 ]. Elucidating pivotal molecular biomarkers and therapeutic targets associated with BRCA invasion and metastasis may accelerate the development of novel diagnostic and treatment strategies for IDC. Exercise, defined as purposeful and planned physical activity designed to optimize health, has been shown to diminish the incidence of diverse cancers and augment the efficacy of cancer therapies [ 8 ]. In addition to reducing cancer incidence, exercise has been demonstrated to potentiate the efficacy of anticancer therapies and alleviate symptoms/adverse effects associated with cancer and its treatment. These benefits are mediated by modulating tumor angiogenesis, myokines, adipokines, related pathways, tumor metabolism, and anti-cancer immune responses [ 9 ]. Research indicates that exercise-related genes (ERGs) are pivotal in cancer biology, impacting tumor progression and clinical outcomes. For instance, Lu et al. demonstrated that the exercise-related gene TLR1 markedly decelerates the cell cycle and inhibits glioma cell growth and motility, significantly affecting prognosis [ 10 ]. Furthermore, Physical activity has also been demonstrated to regulate the nuclear transport of SUMO1 and insulin-like growth factor 1 receptor (IGF1R) in the human hippocampal region, enhancing neurogenesis. KPT-330 suppresses IGF1R nuclear transport, inflammatory responses, and neuronal cell death by disrupting the IGF1R/RanBP2/SUMO1 complex [ 11 ]. Notwithstanding, the role of ERGs in IDC remains largely elusive. ScRNA-seq is a powerful technique enabling comprehensive analysis of genomic, transcriptomic, and epigenomic profiles at individual cellular resolution. This technology facilitates the identification of clinically relevant tumor subpopulations, the investigation of tissue heterogeneity, and high-resolution analysis of intercellular communication, thereby enabling a more comprehensive and precise transcriptomic characterization [ 12 ]. Moreover, scRNA-seq reveals tumor cellular heterogeneity and mechanisms of cancer progression and drug resistance [ 13 ]. As an illustration, research established a prognostic model linked to ferroptosis activation by analyzing differentially expressed genes in high versus low ferroptotic activity subgroups. This model exhibits robust predictive capabilities, discerning the divergent tumor microenvironments and drug resistance profiles of high- and low-risk patients, thereby revealing the ferroptosis-associated molecular pathways in BRCA[ 14 ]. A separate study conducted scRNA-seq on DCIS lesions and paired normal breast tissues, utilizing deduced copy number variation (CNV) profiling to identify tumor cells in DCIS and standard duct samples. Phylogenetic analysis revealed tumor clonal diversity linked to significant transcriptional variations [ 15 ]. Bulk transcriptomics enables the revelation of tissue-wide transcriptional expression profiles but obfuscates intercellular heterogeneity [ 16 ]. Conversely, single-cell transcriptomics delves into single-cell gene expression, uncovering the distribution and functional states of heterogeneous cell populations within tissues. This methodology significantly elevates data resolution and accuracy, enabling researchers to interrogate cellular heterogeneity and track temporal dynamics in cell states during disease progression [ 17 ]. Integrating both analytical modalities empowers researchers to examine gene function and regulatory networks from complementary vantage points, thereby elucidating complex molecular interactions and signaling cascades [ 18 ]. Previous research has not combined single-cell and bulk RNA-seq data to investigate exercise-associated gene mechanisms in IDC. Thus, this study capitalizes on public databases, integrating bulk transcriptomic and single-cell analyses, to discern exercise-related prognostic ERGs in IDC. We establish a prognostic risk model and perform comprehensive bioinformatic analyses, encompassing immune infiltration, functional enrichment, and drug sensitivity assessments.We identify crucial cell populations and interrogate the expression patterns of prognostic ERGs within these cells. Lastly, experimental validation is conducted to ascertain whether the expression of these ERGs aligns with bioinformatic predictions. Our findings could potentially offer novel therapeutic targets and directions for IDC management. 2. Methods 2.1 Data acquisition IDC patient data (RNA-seq, clinical, somatic mutations, survival) was acquired from the Cancer Genome Atlas (TCGA) database. The TCGA-BRCA comprised 772 tumor tissue samples as the IDC group, and 78 adjacent normal tissue samples as the normal group. Among these, 754 tumor tissue samples had available survival information. These samples were then divided into an IDC-training set (7 parts, 526 samples) and an IDC-internal validation set (3 parts, 228 samples) at a ratio of 7:3 for subsequent analysis. Gene expression data from the IDC-related external validation set (GSE26304) and scRNA-seq dataset (GSE195861) were retrieved from the Gene Expression Omnibus (GEO) database. GSE26304 (platform: GPL6848) retained 34 IDC tumor tissue samples with survival data, and GSE195861 (platform: GPL20795) included 6 IDC tumor tissue samples. Additionally, 206 ERGs were downloaded after being searched for the keyword “exercise” in the Molecular Signatures Database (MSigDB) ( Additional file 1 ). 2.2 Differential expression analysis The differentially expressed genes (DEGs) were determined between IDC and normal groups in the IDC-training set through the DESeq2 package [ 19 ]. Afterwards, ggVolcano [ 20 ] and ComplexHeatmap packages [ 21 ] were utilized to establish the volcano plot and heatmap, respectively, to display the markers and expression patterns of DEGs. 2.3 Determination and functional enrichment analysis of differentially expressed ERGs (DE-ERGs) The intersection of ERGs and DEGs was identified to obtain DE-ERGs using the VennDiagram package [ 22 ]. To make clear the biological functions and processes of the DE-ERGs in IDC, the clusterProfiler package [ 23 ] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a protein-protein interaction (PPI) network was established using the STRING database and visualized with Cytoscape software [ 24 ] to explore the interaction relationships among proteins associated with DE-ERGs. 2.4 Construction of IDC risk model In IDC patients from the IDC-training set, the univariate Cox regression analysis and proportional hazards (PH) assumption test were conducted on the DE-ERGs to select candidate prognostic ERGs via the survival package [ 25 ]. To further construct the risk model and prevent the model from over-fitting, the least absolute shrinkage and selection perator (LASSO) regression method (10-fold cross-validation) was conducted to select prognostic ERGs through the glmnet package [ 26 ]. These prognostic ERGs were then employed to establish the risk model with the below equation: The coef denotes the coefficient, and the expr denotes each gene’s expression value. Next, IDC patients from the IDC-training set, IDC-internal validation set, and GSE26304 were sorted into high-risk group (HRG) and low-risk group (LRG), based on the median value of risk scores, separately. Risk curves and survival status charts were plotted to visualize the distribution of IDC patients. The survminer package [ 27 ] and survivalROC package [ 28 ] were utilized to generate Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves to appraise the prognostic effectiveness of the risk model, respectively. If the area under the curve (AUC) values for 1-, 3-, and 5-year survival exceeded 0.6, the model had moderate discriminatory power. 2.5 Analyses of the clinical features and nomogram To appraise the significance of the risk model's impact on prognosis for forecasting overall survival (OS) in IDC patients in the IDC-training set, clinical features (gender, age, tumor stage) and risk scores were subjected to Cox regression modeling (both univariate and multivariate approaches, with PH assumption verification) utilizing the survival package for determination of independent predictive variables. Subsequently, a nomogram related to independent prognostic factors was developed via the rms package [ 29 ] for the prognosis of 1-, 3-, and 5-year survival in IDC patients. The nomogram assigned points to each factor separately. Each factor was associated with a specific point, and the sum of the points of all factors yielded the total points. Then, patients' 1-year, 3-year, and 5-year survival probabilities could be predicted based on the total points. To appraise the reliability of the nomogram, calibration curves and ROC curves were produced via the rms and survivalROC packages, respectively. The AUC values of the nomogram at 1-year, 3-year, and 5-year time points were all greater than 0.7, implying its favorable predictive ability. To deepen the understanding of how clinical traits correlate with risk assessment, disparities in risk scores among patient subgroups stratified by clinical features were analyzed ( p < 0.05). 2.6 Gene set enrichment analysis (GSEA) and immune infiltration analysis In the IDC-training set, the DESeq2 package was applied to investigate the distinctions between the HRG and LRG, and the correlations of DEGs with other genes were analyzed. Subsequently, the correlated genes were sorted by log 2 FC from most significant to least. Notably, “c2.cp.kegg.symbols.gm” acquired from MsigDB was utilized as a background set. Then, GSEA was performed via the clusterProfiler package to explore biological processes significantly enriched between the two groups of IDC patients, with |normalized enrichment score (NES)| > 1 and adj.p < 0.05 considered statistically significant. Moreover, GSEA was conducted to explore the biological functions and signaling pathways associated with the prognostic ERGs. The reference gene set used was “h.all.v2023.1.Hs.symbols.gmt”. The ssGSEA approach was applied to assess infiltration scores of 28 immune cell populations [ 30 ] across risk categories in the IDC-training dataset, followed by Wilcoxon analysis to detect differentially infiltrating immune cells (DICs). Subsequently, Spearman correlation analysis among DICs was utilized to probe their relationships via the psych package [ 31 ]. 2.7 Somatic mutation analysis Somatic mutation analysis can potentially uncover driver and therapy-related mutations within tumor cells. This analysis offers crucial information for cancer diagnosis, prognosis assessment, and the formulation of personalized treatment plans. To explore the variations in somatic mutations between the HRG and LRG in the IDC-training set, somatic mutation data were analyzed using the maftools package [ 32 ]. Meanwhile, waterfall plots were drawn to illustrate the distribution of the top 20 mutated sites in the two risk groups. Furthermore, tumor mutation burden (TMB) profiles were contrasted between the two risk categories. 2.8 Drug sensitivity analyses The drug sensitivity analysis was conducted using the GDSC database to provide management recommendations for IDC. The pRRophetic package [ 33 ] was utilized to compute the half-maximal inhibitory concentration (IC 50 ) of 138 common chemotherapeutic and molecular-targeted agents in the IDC-training set to infer drug sensitivity. The Wilcoxon test was conducted to contrast the variations in sensitivity of medications for IDC clinical treatment between the two groups (p < 0.05). The most significantly different 20 drugs were identified and visualized through box plot methodology. Furthermore, the correlations between prognostic ERGs and drugs, as well as between risk scores and drugs, were evaluated using Spearman analysis, emphasizing the top 10 compounds showing the lowest p-values. 2.9 The scRNA-seq data processing In GSE195861, scRNA-seq data were filtered via the Seurat package [ 34 ] to select high-quality cells (Cells with < 200 genes and genes in < 3 cells were excluded; retained cells met criteria: nFeature RNA 200-4,000, nCount RNA < 20,000, mitochondrial content < 15%). After normalizing the data, the VST method was employed to extract and display the top 2,000 highly variable genes (HVGs). The principal component analysis (PCA) results were analyzed using the ScaleData, JackStrawPlot, and JackStraw functions to determine the top significant principal components (PCs) (p < 0.05). Cell clustering was achieved by applying uniform manifold approximation and projection (UMAP) for dimension reduction based on leading PCs, with cluster determination conducted using FindNeighbors and FindClusters functions (resolution = 0.6). Then, cells were annotated as different types according to marker genes via the FindAllMarkers function (min.pct = 0.6, only.pos = TRUE, logfc.threshold = 0.5) and the CellMarker database. Bubble plots were employed to depict marker gene expression patterns across various cell types. To understand the biological pathways in which the annotated cells were involved and the biological functions they performed, the ReactomeGSA package was used to explore the functional enrichment of the annotated cells. Furthermore, the annotated cells with higher expression levels of prognostic ERGs were regarded as key cells (considering the existing literature). 2.10 Pseudo-time analysis and cell communication Key cells were subjected to dimensionality reduction (resolution = 0.6) employing the DDRTree package [ 35 ]. Subsequently, the orderCell function was employed to determine the cells' differentiation states. Moreover, the Branched Expression Analysis Modeling (BEAM) method from the monocle package [ 36 ] was utilized for pseudo-time analysis to explore variations in the expression levels of prognostic ERGs during key cell differentiation. The CellChat package [ 37 ] analyzed the communication between the key and other cell clusters separately. The critical ligand-receptor interactions linking key cell clusters with other cellular groups were then visualized through bubble plot methodology. 2.11 Experimental validation by reverse transcription quantitative PCR (RT-qPCR) A total of 6 IDC patients and 6 healthy controls were enrolled from the First Affiliated Hospital of Xinjiang Medical University, where tissue specimens were obtained after acquiring written informed consent. The study received ethical clearance from the institutional Ethics Committee (K202504- 66)(Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University,April, 2025),Samples were collected from 2025.4-2025.5. Total RNA was separated using Trizol reagent (Ambion, Texas, USA). Then, cDNA was synthesized from the RNA via the SweScript First Strand cDNA synthesis kit (Servicebio, Wuhan, China). The amplification conditions were determined according to the instructions. PCR amplification occurred in 10 µL reaction volumes. The 2 −ΔΔCt method was utilized to determine prognostic ERG mRNA expression levels. RT-qPCR primer pairs were designed by Sangon, Shanghai, with sequences shown in Table 1 . GAPDH served as the internal control for normalization. Table 1 The primer sequences Primer Sequence GYPC-F AGCCTCGAGCCTGATCCA GYPC-R GCATGACGAAGAGGAGGGAG TRDN-F TCACAGAAGACATAGTGACGACG TRDN-R TGGCAATAGAGCTTGCTGAAA PGK1-F TTGACCGAATCACCGACCTC PGK1-R CATAACGACCCGCTTCCCTT SCG2-F AAGGCTCCCTTATGGTGCTG SCG2-R GCATCCTGGCCAAGTACTCA CALM2-F GGCGAATTAGTCCGAGTGGA CALM2-R TGCTTCTGTGGGATTCTGCC PHKA1-F AAGCGTTCGTCCCACTGATT PHKA1-R GGGATGACCACCATTGGACT MLIP-F GTGACACGTCTGGTCCTTGG MLIP-R GAGCCCATGTTCTTGCCATTG IL16-F CTACAGCAGAGGCCACAGTC IL16-R GTGCCACCCAGCTGTAAGAT GAPDH-F ATGGGCAGCCGTTAGGAAAG GAPDH-R AGGAAAAGCATCACCCGGAG 2.12 Statistical analysis Data was analyzed using R software and GraphPad Prism (version 10). Relative mRNA expression levels of prognostic ERGs were compared using t-test analysis. Statistical comparisons were performed via the Wilcoxon test, with p < 0.05 indicating statistical significance. 3. Results 3.1 Identification and functional analysis of 120 differentially expressed ERGs (DE-ERGs) he IDC-training set revealed 16,340 DEGs, including 9,530 genes with up-regulation and 6,810 with down-regulation in the IDC group. The volcano plot depicted the top 5 (up/down) regulated DEGs (Fig. 1 a). These DEGs had diverse expression patterns between the normal and IDC groups in the heat map (Fig. 1 b). The intersection of 16,340 DEGs and 206 ERGs revealed 120 DE-ERGs (Fig. 1 c, Additional file 2 ). Following this, GO and KEGG analyses were conducted on the 120 DE-ERGs to elucidate the molecular biological processes. A total of 663 GO terms indicated that DE-ERGs were mainly enriched in muscle contraction, striated muscle contraction and muscle system process (p < 0.05, Fig. 1 d, Additional file 3 ). Furthermore, KEGG analysis demonstrated that DE-ERGs were enriched in 37 KEGG pathways, such as cytoskeleton in muscle cells, insulin signaling pathway, and adrenergic signaling in cardiomyocytes (p < 0.05, Fig. 1 e, Additional file 4 ). The protein interactions among the 120 DE-ERGs were investigated, yielding a PPI network comprising 119 proteins and 469 interaction pairs. Within this network, TTN, MYH6, ACTC1, ANK2, PGK1, and ACTA1 exhibited extensive interactions with other proteins (Fig. 1 f). These analyses provided insights on the molecular mechanisms underlying IDC related to exercise. 3.2 Establishment and validation of the risk models in IDC The univariate Cox regression analysis and PH assumption tests identified 8 candidate prognostic ERGs notably linked to OS in IDC patients in the IDC-training set. Specifically, TRDN, PGK1, SCG2, CALM2, PHKA1, and MLIP were considered as risk factors (HR > 1). GYPC and IL16 were considered as protective factors (HR < 1) (Fig. 2 a, Additional file 5 ). To optimize gene selection and derive a more reliable model, 8 prognostic ERGs (TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16) were identified using LASSO regression analysis (optimal lambda = 0.001227376) (Fig. 2 b). Subsequently, the specific formula for IDC patients’ risk model was: risk score = (0.656) * TRDN expression + (0.536) * PGK1 expression + (0.327) * SCG2 expression + (0.424) * CALM2 expression + (0.120) * PHKA1 expression + (1.740) * MLIP expression + (-0.208) * GYPC expression + (-0.187) * IL16 expression. Moreover, to evaluate the risk model of the 8 prognostic ERGs, the IDC samples were classified into HRG and LRG using a median value for the risk score of 6.079661 (high/low risk patients = 263/263) in the IDC-training set. The risk profile and survival status plot illustrated that in the HRG, as the risk scores rose, the survival time decreased, and the number of death cases increased accordingly (Fig. 2 c-d). KM curves disclosed that the survival probability was lower in the HRG, suggesting that IDC patients with high risk scores experienced a poorer prognosis (p < 0.0001, Fig. 2 e). The AUC values verified that the risk model had a high efficacy for forecasting the OS for IDC patients (AUC = 0.644, 0.721 and 0.711 at 1, 3 and 5 years, 95% CI = 0.544–0.744, 0.681–0.761, 0.667–0.755 at 1, 3 and 5 years, respectively, Fig. 2 f). Moreover, the stability of the prognosis model was appraised in the IDC-internal validation set and GSE26304, which yielded comparable results to the IDC-training set (Fig. 2 g-h). These findings further proved the universality of the risk model. 3.3 Clinical features between the HRG and LRG Independent prognostic analyses were essential for establishing robust clinical decision support systems. Hence, in the IDC-training set, tumor stage and risk score were regarded as independent prognostic factors through univariate, multivariate Cox regression analyses, and PH assumption tests (Fig. 3 a-b and Additional file 6–7 ). Subsequently, according to the risk model from the IDC-training set, a nomogram was established by employing risk score and tumor stage to demonstrate the predictive accuracy and clinical utility for IDC patients at 1-year, 3-year, and 5-year (Fig. 3 c). The calibration curves suggested a great fit between the ideal curve and actual curve of survival in the nomogram at 1-year, 3-year, and 5-year. That was to say, the smaller the divergence between the model’s predicted outcomes and the actual outcomes, the superior the efficiency of the nomogram (Fig. 3 d). Then ROC curves demonstrated the nomogram’s accurate predictive capabilities, and AUC values at 1, 3, and 5 years were 0.825, 0.794, and 0.750, respectively (95% CI = 0.072–0.907, 0.753–0.834, 0.706–0.795 at 1-year, 3-year, and 5-year, respectively, Fig. 3 e). A nomogram based on independent prognostic factors could forecast both short-term and long-term OS in IDC patients and facilitate their medical management. Visualization of the correlations between various clinical features and risk scores demonstrated notable differences across tumor stages (p < 0.05). Specifically, there were notable differences in the risk score distributions between stage I and II, stage I and III, as well as stage I and IV (p < 0.05). In contrast, there was no notably difference in the magnitude of risk scores across genders and age subgroups (Fig. 3 f). 3.4 Biological mechanisms and immune cell infiltration landscape associated with risk scores in IDC To explore the molecular mechanisms underlying the correlation between risk scores and IDC prognosis, GSEA was performed. It was observed that there was significant enrichment between the HRG and LRG in 45 biological pathways, such as hematopoietic cell lineage, primary immunodeficiency, allograft rejection, and cell cycle (adj.p < 0.05, Fig. 4 a, Additional file 8 ). Furthermore, GSEA revealed that the key pathways enriched in prognostic ERGs include epithelial mesenchymal transition, TNFA signaling via NFKB, hallmark inflammatory response, oxidative phosphorylation, G2M checkpoint, and IL6 JAK STAT3 signaling (p < 0.05, Fig. 4 b, Additional file 9–16 ). Tumor-infiltrating immune cells had a remarkable impact on cancer progression and were closely associated with the clinical outcomes of patients. The immune cell infiltration abundance socres were compared in the IDC-training set (Fig. 4 c). Among them, 13 DICs showed significant dissimilarities, such as immature B cells, activated B cells, and natural killer (NK) cells (p 0.3, p < 0.001). Among them, the strongest relationship was noticed between activated B cells and immature B cells (cor = 0.91, p < 0.001) (Fig. 4 e). The aforementioned findings indicated that biological functions and abnormal immune infiltration could offer valuable insights for IDC associated with exercise, possessing crucial clinical significance. 3.5 Analysis of TMB under different risk score levels After detecting the transcriptional changes in the aforementioned section, the interaction between TMB and risk score was explored. The frequency of TMB in the top 20 sites for both HRG and LRG was analyzed. Analysis of the TMB data revealed that missense mutation was the most common classification of genetic variation in the IDC-training set. The waterfall plot demonstrated that typical gene mutations were present in both the HRG and the LRG. In the HRG, the top 5 mutant sites were TP53, PI3KCA, TTN, CATA3, and KMT2C, with TP53 boasting a dominant mutation rate of 50% and PI3KCA registering 28% (Fig. 5 a). Conversely, the top 5 mutant sites comprised PI3KCA, TP53,CATA3, TTN, and MAP3K1 in the LRG, where PI3KCA and TP53 displayed mutation rates of 36% and 28%, respectively (Fig. 5 b). Interestingly, PI3KCA, TP53, CATA3, and TTN consistently occupied the top 4 positions in both groups, playing pivotal roles in modulating IDC. Notably, patients in the HRG exhibited higher TMB values compared to those in the LRG (p < 0.0001, Fig. 5 c). In conclusion, the integration of TMB and risk score demonstrated improved predictive ability regarding the prognosis of IDC patients. 3.6 Correlations between drugs and prognostic ERGs Drug sensitivity analyses of anticancer drugs were carried out, and statistically remarkable disparities in the sensitivity of 118 drugs were identified ( Additional file 17 ), with the top 20 drugs showing the smallest p-values selected for further analysis among different risk groups. Notably, low-risk patients showed significantly greater sensitivities to drugs like A.769662, AP.25354, Gemcitabine, Sunitinib, and VX.680 compared to higher risk patients (p < 0.0001). Nevertheless, higher risk patients exhibited lower IC 50 for GW-441756 (p < 0.0001), implying that IDC patients at higher risk were inclined to display reduced resistance to chemotherapy (Fig. 6 a). Subsequently, the correlation heatmap revealed that there was the strongest positive relationship between vinorelbine and PGK1 (cor = 0.72, p < 0.0001), and the strongest negative association was found between AZ682 and GYPC (cor = -0.82, p 0.60, p < 0.0001, Fig. 6 c).The research findings suggested that these drugs had the potential to improve the treatment of IDC patients by targeting prognostic ERGs. 3.7 Cell clusters identification in scRNA-seq dataset GSE195861 After integrating and filtering the original data, the data contained 15,894 cells and 23,486 genes before quality control (QC), retaining 11,629 cells and 23,486 genes after QC (Additional file 18a) , and the top 2,000 HVGs were picked for later analyses (Fig. 7 a ) . Cells from different IDC samples combined showed a compact distribution pattern without batch effects ( Additional file 18b ). The scree plot was employed to determine the optimal dimensionality, which was 20, and the top 20 PCs were retained for downstream analysis ( Additional file 18c-d ). Then, UMAP clustering identified a total of 23 distinct cell clusters(Fig. 7 b). Next, 9 cell clusters were annotated as T cells, NK cells, monocytes, epithelial cells, macrophages, B cells, fibroblasts, erythroid cells, and plasma cells. The bubble plot was generated to visualize the marker genes’ expressions (Fig. 7 c-d, Additional file 19 ). Specifically, PGK1, CALM2, GYPC, and IL16 were highly expressed in B and NK cells (Fig. 7 e). Combined with existing literature [ 38 ], this led to recognizing B and NK cells as key cells for further analysis. B and NK cells correlated with proline catabolism and MGMT-mediated DNA damage reversal (Fig. 7 f, Additional file 20 ). 3.8 Role of prognostic ERGs in NK cells and B cells Following the identification of NK cells as key cells, a pseudo-time analysis was conducted to infer that the differentiation trajectories of NK cells were divided into 5 subtypes. Notably, subtype 2 differentiates earlier, while subtype 1 differentiates later. The differentiation of NK cells progressed through 7 distinct states, indicating the heterogeneity within NK cells. Notably, state 1 differentiated earlier, while state 2 differentiated later (Fig. 8 a-c). In addition, the expression patterns of prognostic ERGs across pseudo-time trajectories were unveiled in NK cells. As NK cells differentiated, the overall expression level of IL16 showed a downward trend, while the expression level of PGK1 exhibited an upward trend. The expression levels of GYPC, CALM2, and PHKA1 demonstrated a pattern of first increasing and then decreasing. The expression of the other prognostic ERGs generally displayed no notable temporal changes (Fig. 8 d). Pseudo-time analysis to confirm the differentiation status of B cells: B cells were classified into 3 subtypes. Subtype 2 was predominant in the early stage, while subtype 1 was in the later stage. The differentiation of B cells progressed through 7 distinct states. States 4 and 5 differentiated earlier, whereas states 2 and 1 differentiated later (Fig. 8 e-g). Subsequently, the expression levels of prognostic ERGs were analyzed, as their alterations were particularly remarkable, thus providing insights into the temporal fluctuations of gene expression. The overall expression levels of GYPC and PHKA1 showed an upward trend during the differentiation process of B cells. Conversely, the overall expression levels of PGK1 and CALM2 exhibited a downward trend. The expression level of IL16 initially increased and then decreased (Fig. 8 h). These observations revealed the pivotal roles played by NK cells and B cells in the pathogenesis of IDC. 3.9 Cell communication landscaping The cell communication analysis network diagram illustrated the number and strength of interactions among annotated cells, revealing that NK cells and B cells communicated with several other cell types. Notably, there was a strong interaction between NK cells and macrophages in IDC tissues (Fig. 9 a). B cells had strong interactions with macrophages, epithelial cells, and NK cells (Fig. 9 b-c). It was worth emphasizing that the signaling molecules involved in the communication among different cell types were presented in the bubble plot. The connections between NK cells and macrophages were mainly established through the receptor-ligand pairs of SPP1-CD44 and LGALS9-CD45 (Fig. 9 d). Similarly, B cells formed connections with the aforementioned 3 types of cells mainly via the receptor-ligand pairs of MIF-(CD74 + CD44) and MIF-(CD74 + CXCR4) (Fig. 9 e). These findings regarding cellular communication not only highlighted the dynamic nature of key cell-cell relationships in IDC, but also opened new avenues for targeted therapies in IDC. 3.10 RT-qPCR confirmation of prognostic ERGs By analyzing prognostic ERGs, it was found that TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16 were important contributors to IDC ( Additional file 21 ). Subsequently, the results of an RT-qPCR experiment proved that compared to the control group, the mRNA expression levels of PGK1, SCG2, CALM2, and PHKA1 were notably increased in IDC patients (p < 0.05, Fig. 10 a-d). Additionally, significantly lower expression of GYPC and IL16 was observed in IDC patients (p 0.05, Fig. 10 g-h). Overall, the expression trends of these prognostic ERGs in were consistent with those observed in the IDC-training set, supporting the reliability of the prognostic ERGs. 4. Discussion IDC, constituting 70%-75% of breast cancer cases [ 4 ], is distinguished by its aggressive phenotype and potent metastatic capacity, resulting in markedly inferior survival outcomes relative to other subtypes (e.g., invasive lobular carcinoma, ILC) [ 39 , 40 ] Despite established molecular markers (ER, PR, Ki67, HER2) playing a pivotal role in diagnosis and treatment [ 39 , 41 ], their reliance on invasive pathological testing and the lack of efficacious therapeutic targets exacerbate high rates of recurrence, metastasis, and dismal prognosis in IDC [ 42 ]. Consequently, the discovery of innovative biomarkers and therapeutic targets is crucial. Emerging evidence indicates that exercise elicits anti-cancer effects by modulating metabolic, immune, and cellular stress pathways [ 11 , 43 ]. ERGs have shown promise in governing tumor progression across various cancers [ 44 ]. For instance, TLR1 has suppressed glioma proliferation and migration while enhancing prognosis [ 10 ]. Nevertheless, the mechanistic role of ERGs in IDC remains largely uncharacterized. Advances in scRNA-seq have unveiled unparalleled insights into the heterogeneity of the tumor microenvironment. The high-definition profiling afforded by scRNA-seq enables meticulous identification of critical cell subpopulations and regulatory networks [ 14 , 45 ], offering new opportunities to decipher the dynamic expression and functional mechanisms of ERGs in IDC. This study integrated publicly available transcriptomic and single-cell RNA-seq data to analyze exercise-associated survival-related genes in IDC comprehensively.A risk prediction model was built to evaluate the predictive efficacy of these genes, followed by in-depth analyses of their associations with immune infiltration, metabolic pathways, and drug sensitivity. We identified key cell populations and explored the expression patterns of prognostic genes within these cells. Ultimately, in vitro studies confirmed the prognostic genes' expression and functional significance in IDC cell lines, evaluating their potential as therapeutic targets. We built a prognostic model based on physical activity-associated genes from the IDC cohort data to evaluate their impact on patient survival and treatment outcomes. Based on prognostic ERGs, this model underwent additional validation through an internal validation set and the GSE26304 dataset, confirming its reliability and consistency over time. Eight independent prognostic endoplasmic reticulum stress-responsive genes (ERGs) were identified, encompassing Phosphoglycerate kinase 1 (PGK1). PGK1 also functions as a cofactor for polymerase α and promotes angiogenesis via secretion by tumor cells [ 39 ]. In the metabolic shift toward enhanced glycolysis in IDC, elevated PGK1 expression drives rapid cancer cell proliferation and invasion [ 46 , 47 ]. Secretogranin II (SCG2) belongs to the chromogranin family of acidic secretory proteins [ 48 ]. In colorectal cancer, SCG2 correlates with immune cell infiltration and macrophage polarization in the tumor microenvironment.. Analogous mechanisms may govern IDC progression by modulating immune responses and stromal crosstalk[ 49 , 50 ]. Calmodulin 2 (CALM2), a calcium-binding protein, regulates signal transduction, cell cycle progression, and proliferation [ 51 ]. Dysregulation of calcium signaling pathways in IDC may underlie aberrant cell growth and metastatic dissemination [ 52 , 53 ]. Phosphorylase kinase regulatory subunit alpha 1 (PHKA1) is a regulator of glycogen metabolism. PHKA1 may modulate energy homeostasis in IDC cells. Altered PHKA1 activity could impact cancer cell proliferation and invasion by disrupting metabolic equilibrium[ 54 – 56 ]. Muscle-rich A-type lamins interacting protein (MLIP) represents a newly identified protein involved in cellular homeostasis and stress adaptation [ 57 ]. MLIP may govern metabolic processes, DNA repair, and cell cycle progression in IDC. Glycophorin C (GYPC) [ 58 , 59 ], an erythrocyte membrane protein, exhibits aberrant expression in cancer. GYPC-mediated membrane stability could indirectly modulate tumor progression [ 59 – 61 ]. Interleukin 16 (IL16), a multifunctional cytokine, modulates immune responses by regulating T-cell migration and activation [ 62 ] IL16 may sculpt the tumor immune microenvironment in IDC, influencing immune surveillance and escape mechanisms [ 63 , 64 ]. Triadin (TRDN), a regulator of calcium homeostasis in muscle cells, is associated with cardiac and muscular disorders via mutations [ 65 , 66 ]. While its role in IDC remains undefined, dysregulated calcium signaling may facilitate cancer cell motility and survival [ 67 ]. The RT-qPCR validation of PGK1, SCG2, CALM2, PHKA1, GYPC, and IL16 corroborated the bioinformatics predictions, highlighting the robustness of the bioinformatics results. Collectively, these findings highlight TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16 as potential prognostic biomarkers in IDC. Further investigation into their functional roles may uncover novel therapeutic avenues for this aggressive breast cancer subtype. As detailed in Sections 3.1 , 3.4 , and 3.7 , our analysis unveiled significant enrichment of the insulin signaling pathway (ISP) and hematopoietic cell lineage in IDC, underscoring their critical roles in tumorigenesis and progression. The insulin/IGF-1 signaling pathway serves as a central regulator of cellular metabolism, proliferation, survival, and differentiation. Activated by insulin and insulin-like growth factors (IGF-1/IGF-2), this pathway engages membrane receptors (IGF1R and InsR), initiating downstream cascades such as PI3K/AKT/mTOR and MAPK/ERK [ 68 ]. In IDC, dysregulation of ISP manifests as receptor overexpression (e.g., IGF1R crosstalk with estrogen signaling in ER + subtypes, leading to uncontrolled proliferation and endocrine therapy resistance) and CDH1 loss-mediated metastasis (via β-catenin-independent EMT induction) [ 69 , 70 ]. The hematopoietic cell lineage, encompassing myeloid (e.g., tumor-associated macrophages, TAMs) and lymphoid cells, modulates the tumor microenvironment (TME) via immune regulation and pleiotropic genetic variants. Myeloid cells (e.g., M2-type TAMs secreting IL-10/TGF-β) establish an immunosuppressive niche, whereas lymphoid dysfunction (e.g., reduced eosinophils compromising immune surveillance) and 4,093 blood trait-associated pleiotropic variants (e.g., PIK3CA mutations enhancing glycolysis via PI3K activation) further influence IDC progression [ 71 , 72 ]. Notably, ISP synergizes with hormonal signaling to establish a metabolic-proliferative axis, whereas hematopoietic aberrations give rise to an immune evasion axis. Specific molecular events (e.g., CDH1 loss) may bridge these axes, exacerbating malignancy (e.g., amplifying IGF signaling while suppressing immune responses) [ 70 ]. These findings underscore the intricate interplay of hormonal, metabolic, immune, and genetic pathways in propelling IDC progression, advocating for multi-targeted therapeutic strategies based on systems biology. We used ssGSEA to compare immune cell infiltration between high- and low-risk IDC cohorts, identifying 13 immune cell types with differential abundance. For instance, triple-negative breast cancer (TNBC) exhibits pronounced lymphocyte infiltration, including tumor-infiltrating B cells (TIBs), which drive early-stage aggressiveness via IL-1β–NFκB–MMP axis activation, thereby promoting angiogenesis, proliferation, and invasion [ 73 ]. B cells in tumor-draining lymph nodes (TDLNs) secrete granzyme B (GZMB), implicating cytotoxic mechanisms in antitumor immunity or immune escape[ 74 ]. NK cells mediate direct tumor cytotoxicity, whereas B cells coordinate antigen presentation and immune activation. Early B-cell infiltration in TNBC augments IL-1β-driven invasiveness, facilitating DCIS-to-IDC transition, whereas senescent cancer-associated fibroblasts (senCAFs) suppress NK-cell cytotoxicity via ECM remodeling, thereby promoting immune evasion [ 73 , 75 ]. Hypoxia-induced proline hydroxylation (HYP) of collagen α-1(I) in TNBC TME underscores the ECM’s role in tumor survival[ 76 ]. Dual roles of TIBs (e.g., plasmablasts) hinge on contextual cues: IL-6 correlates with impaired dendritic/T-cell function [ 77 ], whereas IL-16 promotes macrophage-dependent protumorigenesis [ 78 ]. Conversely, IL-6 deficiency enhances Th1/IFN-γ responses, suggesting pleiotropic immunomodulatory effects [ 79 ]. TCGA somatic mutation data analysis identified high-risk IDC-associated mutations in PIK3CA, TP53, GATA3, and TTN. PIK3CA mutations (34% prevalence in breast cancer) predominantly occur in exons 9 (helical domain) and 20 (kinase domain), hyperactivating the PI3K/AKT/mTOR pathway, especially in HER2 + subtypes [ 80 ]. TP53 mutations, detectable as early as ductal carcinoma in situ (DCIS), drive carcinogenesis via SREBF2-mediated cholesterol metabolism, fueling the estrogen-ESR1 proliferative axis [ 81 , 82 ]. IDC’s heterogeneous drug responses necessitate personalized treatment. Farnesyltransferase inhibitors (FTIs, e.g., GW-441756) exhibit preclinical efficacy by inhibiting Ras farnesylation, inducing G2/M cell cycle, and promoting apoptosis. Although clinical trials of FTIs (e.g., tipifarnib) have shown limited efficacy, FNTB-high TNBC subsets may derive benefit from FTI-based combinational therapies [ 83 , 84 ]. Beyond oncology, FTIs are being investigated for progeria and parasitic infections [ 85 ]. 5. Conclusion This study integrates ERGs to construct a robust prognostic model for IDC, validated across multiple datasets. However, limitations encompass the requirement for larger cohorts and functional validation of ERG-mediated crosstalk with other pathways. The discrepancy between the RT-qPCR results of TRDN/ MLIP and the bioinformatics analysis may stem from multiple factors: potential differences in sample disease stages, the reliance of bioinformatics on large-scale data, versus the limited sample size in experimental validation, which may not fully capture population characteristics. Single-cell RNA sequencing could further clarify B/NK-cell differentiation and their dynamics in the IDC microenvironment. Abbreviations Abbreviation full name breast cancer BRCA Invasive ductal carcinoma IDC ductal carcinoma in situ DCIS exercise-related genes ERGs insulin-like growth factor 1 receptor IGF1R copy number variation CNV the Cancer Genome Atlas TCGA Gene Expression Omnibus GEO differentially expressed genes DEGs differentially expressed ERGs DE-ERGs Gene Ontology GO Kyoto Encyclopedia of Genes and Genomes KEGG protein-protein interaction PPI proportional hazards PH least absolute shrinkage and selection perator LASSO high-risk group HRG low-risk group LRG Kaplan-Meier KM receiver operating characteristic ROC area under the curve AUC overall survival OS Gene set enrichment analysis GSEA normalized enrichment score NES differentially infiltrating immune cells DICs tumor mutation burden TMB half-maximal inhibitory concentration IC50 highly variable genes HVGs principal component analysis PCA principal components PCs uniform manifold approximation and projection UMAP Branched Expression Analysis Modeling BEAM reverse transcription quantitative PCR RT-qPCR quality control QC Muscle-rich A-type lamins interacting protein MLIP Glycophorin C GYPC Calmodulin 2 CALM2 Phosphorylase kinase regulatory subunit alpha 1 PHKA1 Triadin TRDN Interleukin 16 IL16 tumor microenvironment TME tumor-associated macrophages TAMs triple-negative breast cancer TNBC tumor-infiltrating B cells TIBs tumor-draining lymph nodes TDLNs granzyme B GZMB senescent cancer-associated fibroblasts senCAFs hydroxylation HYP ductal carcinoma in situ DCIS Farnesyltransferase inhibitors FTIs Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the Ethics Committee of the The First Affiliated Hospital of Xinjiang Medical University. The approval number and date of approval are as follows: [ K202504- 66] and [April, 2025]. All patients provided written informed consent at the time of clinical sample collection for experiments, ensuring that the research process complied with ethical norms and that patients' rights and wishes were fully respected. Consent to p ublish N/A Availability of data and materials The data that support the findings of this study are openly available in [Gene Expression Omnibus (GEO)] at [https://www.ncbi.nlm.nih.gov/geo/], GSE26304, GSE195861, GSE26304, GSE195861. The IDC patient database at [https://www.cancer.gov/ccg/research/genome-sequencing/tcga]. The exercise-related genes (ERGs) in [ Molecular Signatures Database (MSigDB)] at [https://www.gsea-msigdb.org/gsea/msigdb].All data in this study can be obtained from the authors of this study.All the data of this study can be obtained from the corresponding author Gaojunxi. Conflict of Interest The authors declare that they have no competing interests. Funding details: This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Junxi Gao: Conceptualization, Data curation, Validation, Visualization, Writing–original draft. Youxin Tang: Visualization, Writing–review & editing. Peng Zhang: Writing–review & editing.Yuan Yuan: Writing–review & editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.All authors reviewed the manuscript. Acknowledgments We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. References Filho AM, Laversanne M, Ferlay J, Colombet M, Piñeros M, Znaor A et al. The GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide. Int J Cancer 2025;156:1336-1346. Anderson WF, Matsuno R. Breast cancer heterogeneity: a mixture of at least two main types? J Natl Cancer Inst 2006;98:948-951. Li CI, Uribe DJ, Daling JR. Clinical characteristics of different histologic types of breast cancer. Br J Cancer 2005;93:1046-1052. Duraker N, Hot S, Akan A, Nayır P. A Comparison of the Clinicopathological Features, Metastasis Sites and Survival Outcomes of Invasive Lobular, Invasive Ductal and Mixed Invasive Ductal and Lobular Breast Carcinoma. Eur J Breast Health 2020;16:22-31. Chen Z, Yang J, Li S, Lv M, Shen Y, Wang B et al. Invasive lobular carcinoma of the breast: A special histological type compared with invasive ductal carcinoma. PLoS One 2017;12:e0182397. Fan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD et al. Breast cancer in China. Lancet Oncol 2014;15:e279-289. Deng JL, Xu YH, Wang G. Identification of Potential Crucial Genes and Key Pathways in Breast Cancer Using Bioinformatic Analysis. Front Genet 2019;10:695. Hojman P, Gehl J, Christensen JF, Pedersen BK. Molecular Mechanisms Linking Exercise to Cancer Prevention and Treatment. Cell Metab 2018;27:10-21. Zhu C, Ma H, He A, Li Y, He C, Xia Y. Exercise in cancer prevention and anticancer therapy: Efficacy, molecular mechanisms and clinical information. Cancer Lett 2022;544:215814. Lu L, Hu Y, Wang C, Jiang F, Wu C. Methylation and Expression of the Exercise-Related TLR1 Gene Is Associated With Low Grade Glioma Prognosis and Outcome. Front Mol Biosci 2021;8:747933. Chen Y, Chen X, Luo Z, Kang X, Ge Y, Wan R et al. Exercise-Induced Reduction of IGF1R Sumoylation Attenuates Neuroinflammation in APP/PS1 Transgenic Mice. J Adv Res 2025;69:279-297. Zhang Y, Wang D, Peng M, Tang L, Ouyang J, Xiong F et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res 2021;40:81. Wang S, Sun ST, Zhang XY, Ding HR, Yuan Y, He JJ et al. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 2023;24. Liu S, Zhao Y, Zhang J, Liu Z. Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes. Funct Integr Genomics 2023;23:173. Qin X, Strand SH, Lee MR, Saraswathibhatla A, van IDGP, Zhu C et al. Single Cell Expression Analysis of Ductal Carcinoma in Situ Identifies Complex Genotypic-Phenotypic Relationships Altering Epithelial Composition. Cancer Res 2025. Zhang T, Zhao F, Lin Y, Liu M, Zhou H, Cui F et al. Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression. Theranostics 2024;14:1065-1080. Zhang X, Peng L, Luo Y, Zhang S, Pu Y, Chen Y et al. Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nat Commun 2021;12:5291. Yan C, Li K, Meng F, Chen L, Zhao J, Zhang Z et al. Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer. J Adv Res 2023;44:149-160. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. Wodrich MD, Sawatlon B, Busch M, Corminboeuf C. The Genesis of Molecular Volcano Plots. Acc Chem Res 2021;54:1107-1117. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016;32:2847-2849. Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 2011;12:35. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012;16:284-287. Liu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evid Based Complement Alternat Med 2020;2020:3023127. Lei J, Qu T, Cha L, Tian L, Qiu F, Guo W et al. Clinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers. J Surg Oncol 2023;128:510-518. Li Y, Lu F, Yin Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease. Sci Rep 2022;12:11340. Liu TT, Li R, Huo C, Li JP, Yao J, Ji XL et al. Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis. Front Cell Dev Biol 2021;9:682002. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56:337-344. Sachs MC. plotROC: A Tool for Plotting ROC Curves. J Stat Softw 2017;79. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017;18:248-262. Orifjon S, Jammatov J, Sousa C, Barros R, Vasconcelos O, Rodrigues P. Translation and Adaptation of the Adult Developmental Coordination Disorder/Dyspraxia Checklist (ADC) into Asian Uzbekistan. Sports (Basel) 2023;11. Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018;28:1747-1756. Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014;9:e107468. Hao Y, Hao S, Andersen-Nissen E, Mauck WM, 3rd, Zheng S, Butler A et al. Integrated analysis of multimodal single-cell data. Cell 2021;184:3573-3587.e3529. Zhang Z, Zhu M, Xie Q, Larkin RM, Shi X, Zheng B. CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences. Brief Bioinform 2022;23. Wang X, Wang G, Xu Q, Li Y, Song W, Liu Z et al. Potential tumor-specific antigens and immune landscapes identification for mRNA vaccine in thyroid cancer. Front Oncol 2024;14:1480028. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021;12:1088. Sun Y, Pan Z, Wang Z, Wang H, Wei L, Cui F et al. Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes. J Transl Med 2024;22:894. Li J, Du J, Wang Y, Jia H. A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma. Front Genet 2021;12:722992. Yang Y, Luo D, Gao W, Wang Q, Yao W, Xue D et al. Combination Analysis of Ferroptosis and Immune Status Predicts Patients Survival in Breast Invasive Ductal Carcinoma. Biomolecules 2023;13. Burstein HJ. Systemic Therapy for Estrogen Receptor-Positive, HER2-Negative Breast Cancer. N Engl J Med 2020;383:2557-2570. Yin L, Duan JJ, Bian XW, Yu SC. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res 2020;22:61. Chen Y, Huang L, Luo Z, Han D, Luo W, Wan R et al. Pantothenate-encapsulated liposomes combined with exercise for effective inhibition of CRM1-mediated PKM2 translocation in Alzheimer's therapy. J Control Release 2024;373:336-357. Shi Q, Ying H, Weng W. Targeting exercise-related genes and placental growth factor for therapeutic development in head and neck squamous cell carcinoma. Front Pharmacol 2024;15:1476076. Ding M, Zhang Z, Chen Z, Song J, Wang B, Jin F. Association between periodontitis and breast cancer: two-sample Mendelian randomization study. Clin Oral Investig 2023;27:2843-2849. Qiu S, Zou L, Qiu R, Wang X. Circular RNA circHMCU promotes breast tumorigenesis through miR-4458/PGK1 regulatory cascade. Hereditas 2023;160:12. Xiong Y, Zhang X, Xie W, Yin Y, Qian Y, Ying X et al. DUSP4 inhibited tumor cell proliferation by downregulating glycolysis via p-ERK/p-PGK1 signaling in ovarian cancer. Cancer Cell Int 2025;25:87. Miao C, You X, Zhang Z, Jiang Z, Liu L, Jia Y et al. SCG2 Mediates HNSCC Progression With CCL2/TGFβ1(high) M2 Macrophage Infiltration. Oral Dis 2025;31:782-795. Shi L, Wang H, Sun Y, Xu N, Pei A, Zhang N. Development and validation of a disulfidptosis-related prognostic model for colorectal cancer using multi-omics analysis. Discov Oncol 2025;16:338. Xie Y, Guan S, Li Z, Cai G, Liu Y, Li G et al. Identification of a metabolic-immune signature associated with prognosis in colon cancer and exploration of potential predictive efficacy of immunotherapy response. Clin Exp Med 2025;25:46. Lv J, Chen F, Lv L, Zhang L, Zou H, Liu Y et al. LncRNA ABHD11-AS1 Elevates CALM2 to Promote Metastasis of Thyroid Cancer Through Sponging miR-876-5p. Biochem Genet 2025. Yang J, Li S, Wang J, Liu G, Zhang C, Li X et al. Calmodulin 2 expression is associated with poor prognosis in breast cancer. Pathol Res Pract 2024;258:155326. Lang Y, Kong X, Liu B, Jin X, Chen L, Xu S. microRNA-651-5p affects the proliferation, migration, and invasion of lung cancer cells by regulating Calmodulin 2 expression. Clin Respir J 2023;17:754-763. Guo QR, Zhang GB, Zhou WM, Lu Y, Chen XZ, Deng ZF et al. m(6)A modification of lncRNA PHKA1-AS1 enhances Actinin Alpha 4 stability and promotes non-small cell lung cancer metastasis. MedComm (2020) 2024;5:e547. Chen J, Cao S, Situ B, Zhong J, Hu Y, Li S et al. Metabolic reprogramming-based characterization of circulating tumor cells in prostate cancer. J Exp Clin Cancer Res 2018;37:127. Chen Y, Gao Y, Jiang L. Phosphorylase kinase regulatory subunit alpha 1 as a novel biomarker involved in development and progression of lung cancer: comprehensive bioinformatic analysis and experiment validation. J Physiol Pharmacol 2024;75. Hamwi MN, Elsayed E, Dabash H, Abuawad A, Aweer NA, Al Zeir F et al. MLIP and Its Potential Influence on Key Oncogenic Pathways. Cells 2024;13. Cattin ME, Deeke SA, Dick SA, Verret-Borsos ZJA, Tennakoon G, Gupta R et al. Expression of murine muscle-enriched A-type lamin-interacting protein (MLIP) is regulated by tissue-specific alternative transcription start sites. J Biol Chem 2018;293:19761-19770. Liu S, Liu D, Bender CM, Erickson KI, Sereika SM, Shaffer JR et al. Associations between DNA methylation and cognitive function in early-stage hormone receptor-positive breast cancer patients. medRxiv 2024. Guo C, Tang Y, Zhang Y, Li G. Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis. Front Mol Biosci 2021;8:645388. Wang J, Chen T, Yu X, N OU, Tan L, Jia B et al. Identification and validation of smoking-related genes in lung adenocarcinoma using an in vitro carcinogenesis model and bioinformatics analysis. J Transl Med 2020;18:313. Wen Z, Liu T, Xu X, Acharya N, Shen Z, Lu Y et al. Interleukin-16 enhances anti-tumor immune responses by establishing a Th1 cell-macrophage crosstalk through reprogramming glutamine metabolism in mice. Nat Commun 2025;16:2362. Huang Y, Fan M, Liu Y, Jiang X, Du K, Wu A et al. Novel biomarkers and drug correlations of non-canonical WNT signaling in prostate and breast cancer. Discov Oncol 2024;15:511. Tzeng YT, Chu PY, Yong SB, Hsu TS, Tseng LM, Hou MF et al. Multi-omic profiling of breast tumor microenvironment uncovers a role of mitochondrial calcium gatekeepers. J Cancer 2024;15:3663-3674. Marty I. Triadin: a multi-protein family for which purpose? Cell Mol Life Sci 2004;61:1850-1853. Manno C, Tammineni E, Figueroa L, Marty I, Ríos E. Quantification of the calcium signaling deficit in muscles devoid of triadin. PLoS One 2022;17:e0264146. Mussa BM, Venkatachalam T, Srivastava A, Al-Habshi A, Abdelgadir E, Bashier A et al. Identification of novel differentially expressed genes in type 1 diabetes mellitus complications using transcriptomic profiling of UAE patients: a multicenter study. Sci Rep 2022;12:16316. Albogami SM, Asiri Y, Asiri A, Alnefaie AA, Alnefaie S. Effects of neoadjuvant therapies on genetic regulation of targeted pathways in ER+ primary ductal breast carcinoma: A meta-analysis of microarray datasets. Saudi Pharm J 2021;29:656-669. Iwase T, Harano K, Masuda H, Kida K, Hess KR, Wang Y et al. Quantitative hormone receptor (HR) expression and gene expression analysis in HR+ inflammatory breast cancer (IBC) vs non-IBC. BMC Cancer 2020;20:430. Nagle AM, Levine KM, Tasdemir N, Scott JA, Burlbaugh K, Kehm J et al. Loss of E-cadherin Enhances IGF1-IGF1R Pathway Activation and Sensitizes Breast Cancers to Anti-IGF1R/InsR Inhibitors. Clin Cancer Res 2018;24:5165-5177. Barsky SH, McPhail K, Wang J, Hoffman RM, Ye Y. Bone Marrow Origin of Mammary Phagocytic Intraductal Macrophages (Foam Cells). Int J Mol Sci 2025;26. Pardo-Cea MA, Farré X, Esteve A, Palade J, Espín R, Mateo F et al. Biological basis of extensive pleiotropy between blood traits and cancer risk. Genome Med 2024;16:21. Toney NJ, Opdenaker LM, Frerichs L, Modarai SR, Ma A, Archinal H et al. B cells enhance IL-1 beta driven invasiveness in triple negative breast cancer. Sci Rep 2025;15:2211. da Silveira GG, Oliveira-Costa JP, Soave DF, Zanetti JS, Soares FA, Ribeiro-Silva A. Relationship between B-cell-specific Moloney murine leukemia virus integration site 1 (BMI-1) and homologous recombination regulatory genes in invasive ductal breast carcinomas. Histol Histopathol 2012;27:1353-1359. Almekinders MM, Bismeijer T, Kumar T, Yang F, Thijssen B, van der Linden R et al. Comprehensive multiplexed immune profiling of the ductal carcinoma in situ immune microenvironment regarding subsequent ipsilateral invasive breast cancer risk. Br J Cancer 2022;127:1201-1213. Taylor H, Spruill L, Jensen-Smith H, Rujchanarong D, Hulahan T, Ivey A et al. Spatial localization of collagen hydroxylated proline site variation as an ancestral trait in the breast cancer microenvironment. Matrix Biol 2025;136:71-86. Pilcher W, Thomas BE, Bhasin SS, Jayasinghe RG, Yao L, Gonzalez-Kozlova E et al. Cross center single-cell RNA sequencing study of the immune microenvironment in rapid progressing multiple myeloma. NPJ Genom Med 2023;8:3. Guan X, Wang Y, Fang T, Wang J, Li R, Hao M et al. Lymphoma cell-driven IL-16 is expressed in activated B-cell-like diffuse large B-cell lymphomas and regulates the pro-tumor microenvironment. Haematologica 2025;110:425-438. Liu S, Lei Z, Li J, Wang L, Jia R, Liu Z et al. Interleukin 16 contributes to gammaherpesvirus pathogenesis by inhibiting viral reactivation. PLoS Pathog 2020;16:e1008701. Kandula M, Chennaboina KK, Ys AR, Raju S. Phosphatidylinositol 3-kinase (PI3KCA) oncogene mutation analysis and gene expression profiling in primary breast cancer patients. Asian Pac J Cancer Prev 2013;14:5067-5072. Xu G, Yu J, Lyu J, Zhan M, Xu J, Huang M et al. Proteogenomic Landscape of Breast Ductal Carcinoma Reveals Tumor Progression Characteristics and Therapeutic Targets. Adv Sci (Weinh) 2024;11:e2401041. Zhang Q, Li Y, Yu BH, Bi R, Xu X, Cheng Y et al. Carcinoma arising in microglandular adenosis of the breast: clinicopathological and genetic analysis. J Clin Pathol 2025. Song SY, Meszoely IM, Coffey RJ, Pietenpol JA, Leach SD. K-Ras-independent effects of the farnesyl transferase inhibitor L-744,832 on cyclin B1/Cdc2 kinase activity, G2/M cell cycle progression and apoptosis in human pancreatic ductal adenocarcinoma cells. Neoplasia 2000;2:261-272. Bachmann HS, Jung D, Link T, Arnold A, Kantelhardt E, Thomssen C et al. FNTB Promoter Polymorphisms Are Independent Predictors of Survival in Patients with Triple Negative Breast Cancer. Cancers (Basel) 2022;14. Moorthy NS, Sousa SF, Ramos MJ, Fernandes PA. Farnesyltransferase inhibitors: a comprehensive review based on quantitative structural analysis. Curr Med Chem 2013;20:4888-4923. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":8859293,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of differentially expressed genes. (\u003cstrong\u003ea\u003c/strong\u003e) Volcano plot of differentially expressed genes between sample groups. (\u003cstrong\u003eb\u003c/strong\u003e) Heat map of differentially expressed genes between sample groups, where blue represents control samples and red represents disease group samples; the middle density heat map color represents the expression density of samples, and the redder the color, the greater the density; in the lower heat map, the abscissa represents samples and the ordinate represents genes. (\u003cstrong\u003ec\u003c/strong\u003e) Venn diagram of candidate genes. (\u003cstrong\u003ed\u003c/strong\u003e) Gene ontology (GO)enrichment pathways of candidate genes, each diagram consists of two parts: the left part is a circular diagram of GO enrichment analysis, with a bar chart in the inner circle, where the height of the bar chart indicates the significance of the pathway, and the higher the bar chart, the more significant the pathway; the color of the bar chart is the z-score, and the darker the color, the larger the z-score, which can imply whether the pathway is upregulated/downregulated; the outer circle shows a scatter plot of the expression levels of genes in each pathway, with upregulated and downregulated indicating the upregulated and downregulated genes in each pathway respectively. The right part is the description information of GO enrichment. (\u003cstrong\u003ee\u003c/strong\u003e) kKyoto encyclopedia of genes and genomes (KEGG) enrichment pathways of candidate genes, with the right part being the enriched functional pathways, different colors representing different pathways, and the larger the color square, the more genes enriched in the pathway. (\u003cstrong\u003ef\u003c/strong\u003e) Protein-protein interaction (PPI) network, where circles represent genes, and the color from blue to red represents the stronger connectivity.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/d9657e3c1d784f4e33ff87b2.png"},{"id":100342499,"identity":"f8be0330-1d9a-4a28-83b9-bf9f22e7cacc","added_by":"auto","created_at":"2026-01-16 00:06:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the prognostic model. (\u003cstrong\u003ea\u003c/strong\u003e) Univariate Cox regression analysis, where the hazard ratio represents the risk value, indicating the ratio of event occurrence risks between two groups of patients. A value greater than 1 signifies a higher risk, while a value less than 1 indicates a lower risk. (\u003cstrong\u003eb\u003c/strong\u003e) Least absolute shrinkage and selection operator (LASSO) logistic coefficient penalty plot and cross-validation error curve, with the abscissa being the logarithm of lambdas and the ordinate being the variable coefficient. Each line represents a gene. (\u003cstrong\u003ec\u003c/strong\u003e) Risk scores in the training set. (\u003cstrong\u003ed\u003c/strong\u003e) Survival status in the training set. (\u003cstrong\u003ee\u003c/strong\u003e) Survival curve of the training set. (\u003cstrong\u003ef\u003c/strong\u003e) ROC curves for 1-year, 3-year, and 5-year survival in the training set.\u003c/p\u003e","description":"","filename":"placeholderimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/c0d46914bf3254bf6ba03dc7.png"},{"id":100373296,"identity":"8fd15bdd-dd30-498d-bdf2-1bbffd83c577","added_by":"auto","created_at":"2026-01-16 08:14:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1750793,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent prognostic model. (\u003cstrong\u003ea\u003c/strong\u003e) Forest plot of univariate independent prognostic analysis. (\u003cstrong\u003eb\u003c/strong\u003e) Forest plot of multivariate independent prognostic analysis. (\u003cstrong\u003ec\u003c/strong\u003e) Nomogram for predicting 1-year, 3-year, and 5-year survival rates of patients. On the left side are variable names: each variable's corresponding line is marked with a scale representing the range of values that the variable can take, and the length of the line reflects the contribution of the factor to invasive ductal carcinoma (IDC). Feature weight (feature importance): the longer the line, the greater the weight. Score: the score corresponding to a single patient's certain index can be obtained through the point scale, and the total point can be obtained by accumulating the scores; predicted probability: the probability of the event occurrence can be obtained by comparing the calculated total point with the Nomogram prediction scale, where the abscissa is the Nomogram predicted proportion and the ordinate is the actual survival proportion of patients. (\u003cstrong\u003ed\u003c/strong\u003e) Calibration curve of the nomogram for predicting 1-year, 3-year, and 5-year survival rates of patients. (\u003cstrong\u003ee\u003c/strong\u003e) Receiver operating characteristic (ROC) curves of the nomogram for predicting 1-year, 3-year, and 5-year survival rates of patients.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/a1d042ddf0e0df2118bfd7f0.png"},{"id":100378066,"identity":"ab554a0c-843b-4208-a3c1-6324717c7a57","added_by":"auto","created_at":"2026-01-16 08:49:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6780965,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis and immune infiltration analysis. (\u003cstrong\u003ea\u003c/strong\u003e) Gene set enrichment analysis (GSEA) enrichment analysis. (\u003cstrong\u003eb\u003c/strong\u003e) Functional pathway analysis of prognostic genes. (\u003cstrong\u003ec\u003c/strong\u003e) Immune cell infiltration between high and low-risk groups. The Figure displays the immune cell infiltration levels of each sample between high and low-risk groups. The ordinate represents different immune cell types, the abscissa represents samples of different groups, and the color from red to blue indicates the ssgsea score from high to low. (\u003cstrong\u003ed\u003c/strong\u003e) Differences in immune cell infiltration between invasive ductal carcinoma (IDC) tissues and high/low-risk groups. (\u003cstrong\u003ee\u003c/strong\u003e) Correlation analysis of differentially infiltrated immune cells. The color from red to blue represents the transition from positive to negative correlation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/e59a6f0c69b3f5a05ec18f6f.png"},{"id":100342575,"identity":"caa51a8b-aaf9-49be-89f2-58c9a3447205","added_by":"auto","created_at":"2026-01-16 00:06:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4680413,"visible":true,"origin":"","legend":"\u003cp\u003eSomatic mutation analysis. (\u003cstrong\u003ea\u003c/strong\u003e) Analysis of somatic mutations in tumors of the high-risk group. (\u003cstrong\u003eb\u003c/strong\u003e) Analysis of somatic mutations in tumors of the low-risk group. (\u003cstrong\u003ec\u003c/strong\u003e) The significance of tumor mutation burden (TMB) differences between the high-risk group and the risk score.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/4f94f6088b8785e1adce376d.png"},{"id":100377950,"identity":"2501fe9e-9862-407f-80c8-5f095b7ffdb6","added_by":"auto","created_at":"2026-01-16 08:49:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3945320,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of chemotherapy drug sensitivity. (\u003cstrong\u003ea\u003c/strong\u003e) Differences in immune cells between invasive ductal carcinoma (IDC) tissues and low-risk groups, with the horizontal axis representing the names of 20 drugs and the vertical axis representing the IC50 values of each sample in each cell type. (\u003cstrong\u003eb\u003c/strong\u003e) The correlation between significantly different drugs and prognostic genes. (\u003cstrong\u003ec\u003c/strong\u003e) The correlation between significantly different drugs and risk scores.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/cc695a7bdb621c3b8d45ffe9.png"},{"id":100378121,"identity":"592211b3-8b66-461a-9f15-dee33a508c3c","added_by":"auto","created_at":"2026-01-16 08:50:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2720314,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell data analysis. (\u003cstrong\u003ea\u003c/strong\u003e) Visualization results of highly variable genes. (\u003cstrong\u003eb\u003c/strong\u003e) Visualization of cell umap clustering. (\u003cstrong\u003ec\u003c/strong\u003e) umap cell clustering distribution map of cell subsets. (\u003cstrong\u003ed\u003c/strong\u003e) The significance of TMB differences between the high and low-risk groups and the risk score, with each cell type as the vertical coordinate. The dot color ranging from blue to transparent represents the gene expression level from strong to weak, and the dot size represents the proportion of gene expression level. (\u003cstrong\u003ee\u003c/strong\u003e) Prognostic gene expression levels map of each cell type in IDC tissue. (\u003cstrong\u003ef\u003c/strong\u003e) Enrichment analysis of various types of cells. The horizontal axis in the figure represents each type of cell, and the vertical axis represents the top10 signal pathways enriched in each cell. The color of the heat map from red to blue indicates the correlation of the pathways from strong to weak.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/990fef209eccc73cc2b940cf.png"},{"id":100342529,"identity":"2b3aa052-7a24-4116-ab85-f0058fe05ac9","added_by":"auto","created_at":"2026-01-16 00:06:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1815833,"visible":true,"origin":"","legend":"\u003cp\u003eCell pseudo-temporal analysis. (\u003cstrong\u003ea\u003c/strong\u003e) The difference in the differentiation time of NK cells: dark blue indicates the early stage of differentiation, while light blue indicates the late stage. (\u003cstrong\u003eb\u003c/strong\u003e) Different subsets of natural killer (NK) cells. (\u003cstrong\u003ec\u003c/strong\u003e) Different differentiation states of NK cells. (\u003cstrong\u003ed\u003c/strong\u003e) The relative expression profile of prognostic genes in the pseudo-temporal sequence of NK cells, with the horizontal coordinate being the pseudo-timeline. (\u003cstrong\u003ee\u003c/strong\u003e) Differences in the differentiation time of B cells. (\u003cstrong\u003ef\u003c/strong\u003e) Different subgroups of B cells. (\u003cstrong\u003eg\u003c/strong\u003e) Different differentiation states of B cells. (\u003cstrong\u003eh\u003c/strong\u003e) The relative expression profile of prognostic genes in the pseudo-temporal sequence of B cells, with the horizontal coordinate being the pseudo-timeline.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/49d69da3dfb398073c081eb8.png"},{"id":100378070,"identity":"66984a7e-df84-485f-ab9d-e10337b399f1","added_by":"auto","created_at":"2026-01-16 08:49:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2232697,"visible":true,"origin":"","legend":"\u003cp\u003eCell communication analysis. (\u003cstrong\u003ea\u003c/strong\u003e) The number of interactions and their weights between NK cells in IDC tissues and other cells. (\u003cstrong\u003eb\u003c/strong\u003e) The number of interactions and their weights between invasive ductal carcinoma (IDC) tissue B cells and other cells. (\u003cstrong\u003ec\u003c/strong\u003e) The number and correlation heat map of ligand-receptor interactions among different cell clusters. (\u003cstrong\u003ed\u003c/strong\u003e) Receptor and ligand interactions between NK cells. (\u003cstrong\u003ee\u003c/strong\u003e) Receptor-ligand interactions between B cells.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/380b731955b3618d54c8d2ea.png"},{"id":100373546,"identity":"7987b5c2-0901-4860-9389-5214bd171768","added_by":"auto","created_at":"2026-01-16 08:14:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of prognostic genes related to exercise. (\u003cstrong\u003ea\u003c/strong\u003e) PGK1. (\u003cstrong\u003eb\u003c/strong\u003e) SCG2. (\u003cstrong\u003ec\u003c/strong\u003e) CALM2. 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(\u003cstrong\u003eh\u003c/strong\u003e) MLIP.\u003c/p\u003e","description":"","filename":"placeholderimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/9f6297cf7eb26e2ff38cea5f.png"},{"id":104403997,"identity":"4b9102e3-ed2d-457c-a7f6-3ab0ce0ef8df","added_by":"auto","created_at":"2026-03-11 12:19:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31870366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/2152fe73-af53-4c3a-b8ba-e08202ac116b.pdf"},{"id":100342493,"identity":"90c1af17-79cf-414e-aa1f-41902a65edbe","added_by":"auto","created_at":"2026-01-16 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08:49:53","extension":"pptx","order_by":23,"title":"","display":"","copyAsset":false,"role":"supplement","size":6045071,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/e4ef3252c0593630980682f0.pptx"},{"id":100378061,"identity":"17125b9b-58b0-4b4c-b658-380dc7d0eaf3","added_by":"auto","created_at":"2026-01-16 08:49:44","extension":"docx","order_by":24,"title":"","display":"","copyAsset":false,"role":"supplement","size":14810,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/98b0b9cbf332159eaef9fef9.docx"},{"id":100373184,"identity":"f52757d4-d158-4a98-9cf5-b2d203a788e8","added_by":"auto","created_at":"2026-01-16 08:13:48","extension":"pptx","order_by":25,"title":"","display":"","copyAsset":false,"role":"supplement","size":6045071,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8444169/v1/6e642c9c454364a64075de0e.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma","fulltext":[{"header":"1. Background","content":"\u003cp\u003ePer the GLOBOCAN 2022 report on global cancer statistics, breast cancer (BRCA) has emerged as the most prevalent malignancy among women globally, leading both in incidence and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a highly heterogeneous tumor, the prognosis of BRCA patients varies significantly among its molecular subtypes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Invasive ductal carcinoma (IDC), the most common histological variant, constitutes 70\u0026ndash;75% of all cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the predominant form of BRCA [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], IDC specifically breaches the basement membrane and spreads to remote locations, distinguishing it from ductal carcinoma in situ (DCIS). This aggressive, metastatic, and invasive phenotype of IDC is a primary driver of BRCA-related fatalities. Cancer invasion and metastasis, orchestrated by intricate molecular mechanisms\u0026mdash;including oncogene activation, tumor suppressor gene silencing, and dysregulated signaling pathways\u0026mdash;remain enigmatic [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Elucidating pivotal molecular biomarkers and therapeutic targets associated with BRCA invasion and metastasis may accelerate the development of novel diagnostic and treatment strategies for IDC.\u003c/p\u003e \u003cp\u003eExercise, defined as purposeful and planned physical activity designed to optimize health, has been shown to diminish the incidence of diverse cancers and augment the efficacy of cancer therapies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition to reducing cancer incidence, exercise has been demonstrated to potentiate the efficacy of anticancer therapies and alleviate symptoms/adverse effects associated with cancer and its treatment. These benefits are mediated by modulating tumor angiogenesis, myokines, adipokines, related pathways, tumor metabolism, and anti-cancer immune responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research indicates that exercise-related genes (ERGs) are pivotal in cancer biology, impacting tumor progression and clinical outcomes. For instance, Lu et al. demonstrated that the exercise-related gene TLR1 markedly decelerates the cell cycle and inhibits glioma cell growth and motility, significantly affecting prognosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, Physical activity has also been demonstrated to regulate the nuclear transport of SUMO1 and insulin-like growth factor 1 receptor (IGF1R) in the human hippocampal region, enhancing neurogenesis. KPT-330 suppresses IGF1R nuclear transport, inflammatory responses, and neuronal cell death by disrupting the IGF1R/RanBP2/SUMO1 complex [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notwithstanding, the role of ERGs in IDC remains largely elusive.\u003c/p\u003e \u003cp\u003eScRNA-seq is a powerful technique enabling comprehensive analysis of genomic, transcriptomic, and epigenomic profiles at individual cellular resolution. This technology facilitates the identification of clinically relevant tumor subpopulations, the investigation of tissue heterogeneity, and high-resolution analysis of intercellular communication, thereby enabling a more comprehensive and precise transcriptomic characterization [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, scRNA-seq reveals tumor cellular heterogeneity and mechanisms of cancer progression and drug resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As an illustration, research established a prognostic model linked to ferroptosis activation by analyzing differentially expressed genes in high versus low ferroptotic activity subgroups. This model exhibits robust predictive capabilities, discerning the divergent tumor microenvironments and drug resistance profiles of high- and low-risk patients, thereby revealing the ferroptosis-associated molecular pathways in BRCA[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A separate study conducted scRNA-seq on DCIS lesions and paired normal breast tissues, utilizing deduced copy number variation (CNV) profiling to identify tumor cells in DCIS and standard duct samples. Phylogenetic analysis revealed tumor clonal diversity linked to significant transcriptional variations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBulk transcriptomics enables the revelation of tissue-wide transcriptional expression profiles but obfuscates intercellular heterogeneity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Conversely, single-cell transcriptomics delves into single-cell gene expression, uncovering the distribution and functional states of heterogeneous cell populations within tissues. This methodology significantly elevates data resolution and accuracy, enabling researchers to interrogate cellular heterogeneity and track temporal dynamics in cell states during disease progression [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Integrating both analytical modalities empowers researchers to examine gene function and regulatory networks from complementary vantage points, thereby elucidating complex molecular interactions and signaling cascades [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Previous research has not combined single-cell and bulk RNA-seq data to investigate exercise-associated gene mechanisms in IDC.\u003c/p\u003e \u003cp\u003eThus, this study capitalizes on public databases, integrating bulk transcriptomic and single-cell analyses, to discern exercise-related prognostic ERGs in IDC. We establish a prognostic risk model and perform comprehensive bioinformatic analyses, encompassing immune infiltration, functional enrichment, and drug sensitivity assessments.We identify crucial cell populations and interrogate the expression patterns of prognostic ERGs within these cells. Lastly, experimental validation is conducted to ascertain whether the expression of these ERGs aligns with bioinformatic predictions. Our findings could potentially offer novel therapeutic targets and directions for IDC management.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e \u003cp\u003eIDC patient data (RNA-seq, clinical, somatic mutations, survival) was acquired from the Cancer Genome Atlas (TCGA) database. The TCGA-BRCA comprised 772 tumor tissue samples as the IDC group, and 78 adjacent normal tissue samples as the normal group. Among these, 754 tumor tissue samples had available survival information. These samples were then divided into an IDC-training set (7 parts, 526 samples) and an IDC-internal validation set (3 parts, 228 samples) at a ratio of 7:3 for subsequent analysis. Gene expression data from the IDC-related external validation set (GSE26304) and scRNA-seq dataset (GSE195861) were retrieved from the Gene Expression Omnibus (GEO) database. GSE26304 (platform: GPL6848) retained 34 IDC tumor tissue samples with survival data, and GSE195861 (platform: GPL20795) included 6 IDC tumor tissue samples. Additionally, 206 ERGs were downloaded after being searched for the keyword \u0026ldquo;exercise\u0026rdquo; in the Molecular Signatures Database (MSigDB) (\u003cb\u003eAdditional file 1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eThe differentially expressed genes (DEGs) were determined between IDC and normal groups in the IDC-training set through the DESeq2 package [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Afterwards, ggVolcano [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and ComplexHeatmap packages [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] were utilized to establish the volcano plot and heatmap, respectively, to display the markers and expression patterns of DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination and functional enrichment analysis of differentially expressed ERGs (DE-ERGs)\u003c/h2\u003e \u003cp\u003eThe intersection of ERGs and DEGs was identified to obtain DE-ERGs using the VennDiagram package [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To make clear the biological functions and processes of the DE-ERGs in IDC, the clusterProfiler package [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, a protein-protein interaction (PPI) network was established using the STRING database and visualized with Cytoscape software [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to explore the interaction relationships among proteins associated with DE-ERGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction of IDC risk model\u003c/h2\u003e \u003cp\u003eIn IDC patients from the IDC-training set, the univariate Cox regression analysis and proportional hazards (PH) assumption test were conducted on the DE-ERGs to select candidate prognostic ERGs via the survival package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To further construct the risk model and prevent the model from over-fitting, the least absolute shrinkage and selection perator (LASSO) regression method (10-fold cross-validation) was conducted to select prognostic ERGs through the glmnet package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These prognostic ERGs were then employed to establish the risk model with the below equation:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUcAAAAnCAYAAACcyoqFAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAwJSURBVHhe7Z2/iyRFG8e/8+ai7RqJJtNrJHIgfQgyCmfSExkJPWgiCK49gqDCnsx6GJwiPcFFMtsLm0rPcRjOeXOhNQinp/QERm4biGGP4/4FZfD201Q/Xd3Tu7N7u+PVByqYqur68dRTT1U9VXvXklJKGAwGg6HA/3iEwWAwGIxxNBgMBi3GOBoMBoMGYxwNBoNBgzGOBoPBoMEYxw2j3++j1Wqh1WphOBwW0rrdbp7W7/cLaQaD4WS0zFOezaTb7WI6nUIIgU6nk8ePx2M899xzhTiDwXByzM5xQ2m32/B9H6+99hpPMhgMZ4AxjhvMaDSCbdvodrs8yWAwrIkxjhfIeDzOfYRNwmw240Xg/v37mE6nJf+jwWBYj7WM43A4xNNPP43FYsGTSiwWCxwcHGB7e1s7yR9Her0eXNcFAPi+DyllKQghYFkW/zSn3W4jiiJcv359I+U6HA6xvb2NVquFq1evYj6f52nz+RwHBweF/JeV2Wy2MW09KTR3//jjD550Kdnb2zuTtq5lHE/C8fExACBJEp70WDMajWBZFvb393H37l2ejE6ng88++4xHFyAju2n+x729Pfz66684OjqCEALL5RJff/01kO2qp9MpdnZ2+GeXkk6ng+eff/4/90pgPp/jww8/xM7ODtrtNk++lHzyyScYDAaFhfZUyEeIEEICkEIInvRYE4ahBCAty5JpmvJkmSRJSW6+7xfyyP+/Oijlu8wAkEEQ8GgphJCu6/LojSAIAjkYDHi0dlyJurSLJE1Tadv2pW1fHdT2JEl4UmMe2c7RUM3Ozg48z8NyucQ777zDk9FutyGlXPk8RwjBozaSd999Fx999BGP3gjee+897O/vl1wct27dwng8LsQhe7f64MEDHn0puHHjBjzPw9bWFk+69GxtbeH9999fbyfPraVKkiRyMBjkOxrXdaVlWVIIIeM4ztNUoiiSlmVJANJxnMIOh+8c6TcF3S6CGAwGeT7XdWUYhoX0yWQiHcfJd2C8rDRN8/ZS2yaTSZ5e11f63vf9vA2e553pipqmad423jcV2kVS4LvEIAhKcSdhXTkSk8lE2rYtAUjbtmUURXma67qFPkAZ+yiKJCrUMo5j7bcACuXX1R1FkXRdVwZBUCjPdd3SeNaVU4fnedLzPB4tfd8vlMF/E2q7LMsq7ER5vyWTpxBCJkkiwzCUtm1LIUQ+Xnw863Red1pRCYIg1wE12Lad56nrhxBC+r6fy53mlm3bMo7jPJ9cY+7FcSwBlMpril4LM+I4lp7nSWTKSxNnMplIIUSepua3LCvfyoZhWDgekTFUBU6KWrf9pXLSNM0FpQ4yGWQqlwypqnhkqNUy1Dx1fU3TVDqOkxstIYS0LCvvW9WE5aFK0YjJZJLnPe2ArsNZyJHKcRxHJklSyMP7T7JWcRynoDNEkiTSsqx8sSVdszSLc1XdaZrmMvY8T/q+L4UQMggCCbYo1ZWzCipPp9NkEOsMIxk1qbhcSE5pmuZ6Su2N4zhvK/2msfM8T0ZRVFj0mug8fa+DDCrpKMlGld+qfgghpG3buT5NJpPC2BCr5t4qAOQ6c1L0vVeoG2hKI6jDKqoCCMU4ksI1VTbeQbVcy7IKv2nFIIFG2W5EXW3SzCehTq6qvoZhWKqflOesjRgpq27ncd6clRzVBZLy6PqkThY1jueTFWNDcaoONakbQGEXQ3FqW5qUUwXJSbejllJK27ZL+kSQMVNxHKcgXzIYZKB0u16aa2ob0ux0os5RnVxltuDzhYeAxuAg230TTfrhuq50HKeQx3XdQjnrzj0ywKehsXHUwdNooriuqzV6NGBhGBZWulXQauH7fukb1eBW4bpuyWhLRcj0Le8PUbcz5AqwLkl2nOGT97w5KzlSObrAvwUzSPQtN5hS2Xmo7eMTu2ndujqgTO6m5VRR149VO0denxpU3aedMyqMRNV48tNelc6DGTsV3c4NzGDytuv6wQ0hxantWXfu8fJOwpleyGxtbeHBgwd444038Oabb6Lb7Wqv04+Pj/Hw4UMMBgOepKXT6eDhw4cAANu20e/3G72tVPn77795FJ588kkeVUkQBMgWk0Lo9XqFf/ChLnAnvY5+vw/HcfDVV1/xpALdbvdED79ns1njNtTRVI5cTlJKHB0d8WyNeeutt+A4Dr755hssFgssFgvcuXMHruuWnpjwek9bNy/jtOUQ/X4fr7/+Onq9HkajEX744QftJY0QolSvlLLQzytXrsD3fQDAdDpVvq7n5Zdf5lEnZjQaYTqd5rpEfXj77bcL+Zr0owl1c+88OVPjiMxA7u7u4vfff8dTTz2Fa9eulR5kvvrqq4iiCLdv3258m9RutzEajRDHMX7++efSn8z99ttvhd9QBg0AlstlpUF94okneFSJO3fu8CgsFgvMZjPcu3evNHC6sOq2eTgc4qeffsLt27d5Uol79+5hd3eXR2sZj8eN30CelRx1bzZ1cU3Z2trC4eEh/vnnH7zwwgt45plncPXqVXz77bc8q7YeXdwqdN/o4pqwt7eXG0aCDCQv8/vvvy/8hqbe2WyG5XIJ3/dP9AcA9N54HXq9Hnzfx5dffolWq4XPP/8ck8mkpN9N+tGEurl3rvCtJKdq2y01aXSsUgFzwkJzjF21PQ7DsOBTUcshP4rF3gjGcZz7yrgzmBgMBlr/C4e+5+0cDAYlX89poT5V+anOAlX2nLOUo2VZ0nGcQjlJkhS+Ix8eLwsVxzlq3yqa1N2k3ibl2BU32HSxoKbV6Yma5nle7ktUUY+scRwXfpMvTz1287lGcH9nlc7XHUeDICjJj9OkH02O1U3mXhAEpXIIx3EauUJ06HuvwG/GdGkkACGEdBwnHxA+QOSoVv1pdIOmK58IgkB6npcPfpA9I1CFg8wfFGSPcF3FSZ0qDmy1bZZlFYxRVV/TzJdKE4gGY5WCNIXKr/MzqnKVNROzDt1kUTkrOZJCW9nzDTKeqhGgPPyCw9U46aWiS052m+26rhwMBjJgLx1W1U2XTOpkIkOt1ruqnDpIjtw/3gRqHzIfXhAE0lZufcnXqJZNxlj146vy4vNG/bZK56kPuv662WUNjYPneTIIAu1lXlU/SOfVeSwVe6Dq3Dpzj+o/DbXGkaw4BbVBPE1ku0Y1Xp3AJGwKbnZpw+N0hNkFDuVzHKe0ItHAo+IdVMreSnFjw/vDhZ8kSa5Ilub93zp4nqc1CARd0lB7qa1RFOULTlVQUcuoYl05EmH2zg7ZuKrjxWWttov6wyHDrOoBBT7Bqurm+kb18rhV5azC87xKXW6CyDYZyOaQuvBQG9Xyde2nftEOTtcHPg6qTnOdU4miKDdsvG61jCb9oKAbB6r7tHOPDLSuD00oa6HhkRKGYWk155AxUgf5PHaOlwU+kSQ73qvQxLmIflXJ01J21hcFGZt12uH7vvY0U+VOmkwmay0Kp8X3fe3uMGTvrE/KmV/IGJozn8/xwQcfYLlcwrbt0u02hf39ff5pzqp/9mwT+e677/DFF1/kvxeLBa5du4YXX3yxkA/ZRZ1t23j22Wd50rmyvb3NowAABwcH8H2/dDmxidy8eRP3798vXKgOh0P88ssv2j8pfOWVV058E70uw+FQOz8WiwUODw8xGo14UmOMcbxArl+/zqNq0RmAXq+X34brwiZy5coVfPrpp/lLBrph/fjjjzEejzGfz/PbyuFwiJdeeumRT0rdk575fI4///xz5TOsR8GPP/4IAPjrr794UmPohYD6dO74+BjT6RR7e3uYzWZYLBaYz+e4e/cubty4gZs3b/JizpXd3d38SZPKrVu3cHh4uJ5e8K2kYTP4Lx+riTiO8z7SDS35uSzLurDjNKHKU23rRcN9iescLWXm7w2VFyO6O4AgCLRH7UcBP1aHYVjrpmqK+Q+2NpDt7e3838Xk/8GWjtlsVnjnGEXRuT+gfRxotVqN5G84X+iEsc4RWoc5Vm8gR0dH+bG5ycTsdDqFo7YxjAbDaszO0WA4BeplV5Ik6/m2DKem3+/nFzJBEDT+q7EmGONoMBgMGsyx2mAwGDQY42gwGAwajHE0GAwGDcY4GgwGgwZjHA0Gg0GDMY4Gg8GgwRhHg8Fg0PAv8OgH+1b9WnMAAAAASUVORK5CYII=\" width=\"327\" height=\"39\"\u003e\u003c/p\u003e\u003cp\u003eThe coef denotes the coefficient, and the expr denotes each gene\u0026rsquo;s expression value. Next, IDC patients from the IDC-training set, IDC-internal validation set, and GSE26304 were sorted into high-risk group (HRG) and low-risk group (LRG), based on the median value of risk scores, separately. Risk curves and survival status charts were plotted to visualize the distribution of IDC patients. The survminer package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and survivalROC package [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were utilized to generate Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves to appraise the prognostic effectiveness of the risk model, respectively. If the area under the curve (AUC) values for 1-, 3-, and 5-year survival exceeded 0.6, the model had moderate discriminatory power.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Analyses of the clinical features and nomogram\u003c/h2\u003e \u003cp\u003eTo appraise the significance of the risk model's impact on prognosis for forecasting overall survival (OS) in IDC patients in the IDC-training set, clinical features (gender, age, tumor stage) and risk scores were subjected to Cox regression modeling (both univariate and multivariate approaches, with PH assumption verification) utilizing the survival package for determination of independent predictive variables.\u003c/p\u003e \u003cp\u003eSubsequently, a nomogram related to independent prognostic factors was developed via the rms package [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] for the prognosis of 1-, 3-, and 5-year survival in IDC patients. The nomogram assigned points to each factor separately. Each factor was associated with a specific point, and the sum of the points of all factors yielded the total points. Then, patients' 1-year, 3-year, and 5-year survival probabilities could be predicted based on the total points. To appraise the reliability of the nomogram, calibration curves and ROC curves were produced via the rms and survivalROC packages, respectively. The AUC values of the nomogram at 1-year, 3-year, and 5-year time points were all greater than 0.7, implying its favorable predictive ability.\u003c/p\u003e \u003cp\u003eTo deepen the understanding of how clinical traits correlate with risk assessment, disparities in risk scores among patient subgroups stratified by clinical features were analyzed ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene set enrichment analysis (GSEA) and immune infiltration analysis\u003c/h2\u003e \u003cp\u003eIn the IDC-training set, the DESeq2 package was applied to investigate the distinctions between the HRG and LRG, and the correlations of DEGs with other genes were analyzed. Subsequently, the correlated genes were sorted by log\u003csub\u003e2\u003c/sub\u003eFC from most significant to least. Notably, \u0026ldquo;c2.cp.kegg.symbols.gm\u0026rdquo; acquired from MsigDB was utilized as a background set. Then, GSEA was performed via the clusterProfiler package to explore biological processes significantly enriched between the two groups of IDC patients, with |normalized enrichment score (NES)| \u0026gt; 1 and adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Moreover, GSEA was conducted to explore the biological functions and signaling pathways associated with the prognostic ERGs. The reference gene set used was \u0026ldquo;h.all.v2023.1.Hs.symbols.gmt\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe ssGSEA approach was applied to assess infiltration scores of 28 immune cell populations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] across risk categories in the IDC-training dataset, followed by Wilcoxon analysis to detect differentially infiltrating immune cells (DICs). Subsequently, Spearman correlation analysis among DICs was utilized to probe their relationships via the psych package [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Somatic mutation analysis\u003c/h2\u003e \u003cp\u003eSomatic mutation analysis can potentially uncover driver and therapy-related mutations within tumor cells. This analysis offers crucial information for cancer diagnosis, prognosis assessment, and the formulation of personalized treatment plans. To explore the variations in somatic mutations between the HRG and LRG in the IDC-training set, somatic mutation data were analyzed using the maftools package [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Meanwhile, waterfall plots were drawn to illustrate the distribution of the top 20 mutated sites in the two risk groups. Furthermore, tumor mutation burden (TMB) profiles were contrasted between the two risk categories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Drug sensitivity analyses\u003c/h2\u003e \u003cp\u003eThe drug sensitivity analysis was conducted using the GDSC database to provide management recommendations for IDC. The pRRophetic package [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was utilized to compute the half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) of 138 common chemotherapeutic and molecular-targeted agents in the IDC-training set to infer drug sensitivity. The Wilcoxon test was conducted to contrast the variations in sensitivity of medications for IDC clinical treatment between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The most significantly different 20 drugs were identified and visualized through box plot methodology. Furthermore, the correlations between prognostic ERGs and drugs, as well as between risk scores and drugs, were evaluated using Spearman analysis, emphasizing the top 10 compounds showing the lowest p-values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 The scRNA-seq data processing\u003c/h2\u003e \u003cp\u003eIn GSE195861, scRNA-seq data were filtered via the Seurat package [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] to select high-quality cells (Cells with \u0026lt;\u0026thinsp;200 genes and genes in \u0026lt;\u0026thinsp;3 cells were excluded; retained cells met criteria: nFeature RNA 200-4,000, nCount RNA\u0026thinsp;\u0026lt;\u0026thinsp;20,000, mitochondrial content\u0026thinsp;\u0026lt;\u0026thinsp;15%). After normalizing the data, the VST method was employed to extract and display the top 2,000 highly variable genes (HVGs). The principal component analysis (PCA) results were analyzed using the ScaleData, JackStrawPlot, and JackStraw functions to determine the top significant principal components (PCs) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Cell clustering was achieved by applying uniform manifold approximation and projection (UMAP) for dimension reduction based on leading PCs, with cluster determination conducted using FindNeighbors and FindClusters functions (resolution\u0026thinsp;=\u0026thinsp;0.6). Then, cells were annotated as different types according to marker genes via the FindAllMarkers function (min.pct\u0026thinsp;=\u0026thinsp;0.6, only.pos\u0026thinsp;=\u0026thinsp;TRUE, logfc.threshold\u0026thinsp;=\u0026thinsp;0.5) and the CellMarker database. Bubble plots were employed to depict marker gene expression patterns across various cell types. To understand the biological pathways in which the annotated cells were involved and the biological functions they performed, the ReactomeGSA package was used to explore the functional enrichment of the annotated cells. Furthermore, the annotated cells with higher expression levels of prognostic ERGs were regarded as key cells (considering the existing literature).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Pseudo-time analysis and cell communication\u003c/h2\u003e \u003cp\u003eKey cells were subjected to dimensionality reduction (resolution\u0026thinsp;=\u0026thinsp;0.6) employing the DDRTree package [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Subsequently, the orderCell function was employed to determine the cells' differentiation states. Moreover, the Branched Expression Analysis Modeling (BEAM) method from the monocle package [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was utilized for pseudo-time analysis to explore variations in the expression levels of prognostic ERGs during key cell differentiation.\u003c/p\u003e \u003cp\u003eThe CellChat package [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] analyzed the communication between the key and other cell clusters separately. The critical ligand-receptor interactions linking key cell clusters with other cellular groups were then visualized through bubble plot methodology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Experimental validation by reverse transcription quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003e A total of 6 IDC patients and 6 healthy controls were enrolled from the First Affiliated Hospital of Xinjiang Medical University, where tissue specimens were obtained after acquiring written informed consent. The study received ethical clearance from the institutional Ethics Committee (K202504- 66)(Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University,April, 2025),Samples were collected from 2025.4-2025.5. Total RNA was separated using Trizol reagent (Ambion, Texas, USA). Then, cDNA was synthesized from the RNA via the SweScript First Strand cDNA synthesis kit (Servicebio, Wuhan, China). The amplification conditions were determined according to the instructions. PCR amplification occurred in 10 \u0026micro;L reaction volumes. The 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method was utilized to determine prognostic ERG mRNA expression levels. RT-qPCR primer pairs were designed by Sangon, Shanghai, with sequences shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. GAPDH served as the internal control for normalization.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYPC-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCCTCGAGCCTGATCCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGYPC-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCATGACGAAGAGGAGGGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRDN-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCACAGAAGACATAGTGACGACG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRDN-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGCAATAGAGCTTGCTGAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGK1-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTGACCGAATCACCGACCTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGK1-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCATAACGACCCGCTTCCCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCG2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGGCTCCCTTATGGTGCTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCG2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCATCCTGGCCAAGTACTCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALM2-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGCGAATTAGTCCGAGTGGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCALM2-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCTTCTGTGGGATTCTGCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHKA1-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGCGTTCGTCCCACTGATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHKA1-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGGATGACCACCATTGGACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLIP-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTGACACGTCTGGTCCTTGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLIP-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGCCCATGTTCTTGCCATTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL16-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTACAGCAGAGGCCACAGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL16-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTGCCACCCAGCTGTAAGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGGGCAGCCGTTAGGAAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGAAAAGCATCACCCGGAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Statistical analysis\u003c/h2\u003e \u003cp\u003eData was analyzed using R software and GraphPad Prism (version 10). Relative mRNA expression levels of prognostic ERGs were compared using t-test analysis. Statistical comparisons were performed via the Wilcoxon test, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification and functional analysis of 120 differentially expressed ERGs (DE-ERGs)\u003c/h2\u003e \u003cp\u003ehe IDC-training set revealed 16,340 DEGs, including 9,530 genes with up-regulation and 6,810 with down-regulation in the IDC group. The volcano plot depicted the top 5 (up/down) regulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These DEGs had diverse expression patterns between the normal and IDC groups in the heat map (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The intersection of 16,340 DEGs and 206 ERGs revealed 120 DE-ERGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cb\u003eAdditional file 2\u003c/b\u003e). Following this, GO and KEGG analyses were conducted on the 120 DE-ERGs to elucidate the molecular biological processes. A total of 663 GO terms indicated that DE-ERGs were mainly enriched in muscle contraction, striated muscle contraction and muscle system process (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, \u003cb\u003eAdditional file 3\u003c/b\u003e). Furthermore, KEGG analysis demonstrated that DE-ERGs were enriched in 37 KEGG pathways, such as cytoskeleton in muscle cells, insulin signaling pathway, and adrenergic signaling in cardiomyocytes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, \u003cb\u003eAdditional file 4\u003c/b\u003e). The protein interactions among the 120 DE-ERGs were investigated, yielding a PPI network comprising 119 proteins and 469 interaction pairs. Within this network, TTN, MYH6, ACTC1, ANK2, PGK1, and ACTA1 exhibited extensive interactions with other proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). These analyses provided insights on the molecular mechanisms underlying IDC related to exercise.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Establishment and validation of the risk models in IDC\u003c/h2\u003e \u003cp\u003eThe univariate Cox regression analysis and PH assumption tests identified 8 candidate prognostic ERGs notably linked to OS in IDC patients in the IDC-training set. Specifically, TRDN, PGK1, SCG2, CALM2, PHKA1, and MLIP were considered as risk factors (HR\u0026thinsp;\u0026gt;\u0026thinsp;1). GYPC and IL16 were considered as protective factors (HR\u0026thinsp;\u0026lt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003eAdditional file 5\u003c/b\u003e). To optimize gene selection and derive a more reliable model, 8 prognostic ERGs (TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16) were identified using LASSO regression analysis (optimal lambda\u0026thinsp;=\u0026thinsp;0.001227376) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Subsequently, the specific formula for IDC patients\u0026rsquo; risk model was: risk score = (0.656) * TRDN expression + (0.536) * PGK1 expression + (0.327) * SCG2 expression + (0.424) * CALM2 expression + (0.120) * PHKA1 expression + (1.740) * MLIP expression + (-0.208) * GYPC expression + (-0.187) * IL16 expression. Moreover, to evaluate the risk model of the 8 prognostic ERGs, the IDC samples were classified into HRG and LRG using a median value for the risk score of 6.079661 (high/low risk patients\u0026thinsp;=\u0026thinsp;263/263) in the IDC-training set. The risk profile and survival status plot illustrated that in the HRG, as the risk scores rose, the survival time decreased, and the number of death cases increased accordingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). KM curves disclosed that the survival probability was lower in the HRG, suggesting that IDC patients with high risk scores experienced a poorer prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The AUC values verified that the risk model had a high efficacy for forecasting the OS for IDC patients (AUC\u0026thinsp;=\u0026thinsp;0.644, 0.721 and 0.711 at 1, 3 and 5 years, 95% CI\u0026thinsp;=\u0026thinsp;0.544\u0026ndash;0.744, 0.681\u0026ndash;0.761, 0.667\u0026ndash;0.755 at 1, 3 and 5 years, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Moreover, the stability of the prognosis model was appraised in the IDC-internal validation set and GSE26304, which yielded comparable results to the IDC-training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-h). These findings further proved the universality of the risk model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Clinical features between the HRG and LRG\u003c/h2\u003e \u003cp\u003eIndependent prognostic analyses were essential for establishing robust clinical decision support systems. Hence, in the IDC-training set, tumor stage and risk score were regarded as independent prognostic factors through univariate, multivariate Cox regression analyses, and PH assumption tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b and \u003cb\u003eAdditional file 6\u0026ndash;7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, according to the risk model from the IDC-training set, a nomogram was established by employing risk score and tumor stage to demonstrate the predictive accuracy and clinical utility for IDC patients at 1-year, 3-year, and 5-year (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The calibration curves suggested a great fit between the ideal curve and actual curve of survival in the nomogram at 1-year, 3-year, and 5-year. That was to say, the smaller the divergence between the model\u0026rsquo;s predicted outcomes and the actual outcomes, the superior the efficiency of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Then ROC curves demonstrated the nomogram\u0026rsquo;s accurate predictive capabilities, and AUC values at 1, 3, and 5 years were 0.825, 0.794, and 0.750, respectively (95% CI\u0026thinsp;=\u0026thinsp;0.072\u0026ndash;0.907, 0.753\u0026ndash;0.834, 0.706\u0026ndash;0.795 at 1-year, 3-year, and 5-year, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). A nomogram based on independent prognostic factors could forecast both short-term and long-term OS in IDC patients and facilitate their medical management.\u003c/p\u003e \u003cp\u003eVisualization of the correlations between various clinical features and risk scores demonstrated notable differences across tumor stages (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, there were notable differences in the risk score distributions between stage I and II, stage I and III, as well as stage I and IV (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, there was no notably difference in the magnitude of risk scores across genders and age subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Biological mechanisms and immune cell infiltration landscape associated with risk scores in IDC\u003c/h2\u003e \u003cp\u003eTo explore the molecular mechanisms underlying the correlation between risk scores and IDC prognosis, GSEA was performed. It was observed that there was significant enrichment between the HRG and LRG in 45 biological pathways, such as hematopoietic cell lineage, primary immunodeficiency, allograft rejection, and cell cycle (adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cb\u003eAdditional file 8\u003c/b\u003e). Furthermore, GSEA revealed that the key pathways enriched in prognostic ERGs include epithelial mesenchymal transition, TNFA signaling via NFKB, hallmark inflammatory response, oxidative phosphorylation, G2M checkpoint, and IL6 JAK STAT3 signaling (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cb\u003eAdditional file 9\u0026ndash;16\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTumor-infiltrating immune cells had a remarkable impact on cancer progression and were closely associated with the clinical outcomes of patients. The immune cell infiltration abundance socres were compared in the IDC-training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Among them, 13 DICs showed significant dissimilarities, such as immature B cells, activated B cells, and natural killer (NK) cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). A correlation heatmap revealed that there were positive correlations among the most of DICs (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among them, the strongest relationship was noticed between activated B cells and immature B cells (cor\u0026thinsp;=\u0026thinsp;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The aforementioned findings indicated that biological functions and abnormal immune infiltration could offer valuable insights for IDC associated with exercise, possessing crucial clinical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of TMB under different risk score levels\u003c/h2\u003e \u003cp\u003eAfter detecting the transcriptional changes in the aforementioned section, the interaction between TMB and risk score was explored. The frequency of TMB in the top 20 sites for both HRG and LRG was analyzed. Analysis of the TMB data revealed that missense mutation was the most common classification of genetic variation in the IDC-training set. The waterfall plot demonstrated that typical gene mutations were present in both the HRG and the LRG. In the HRG, the top 5 mutant sites were TP53, PI3KCA, TTN, CATA3, and KMT2C, with TP53 boasting a dominant mutation rate of 50% and PI3KCA registering 28% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Conversely, the top 5 mutant sites comprised PI3KCA, TP53,CATA3, TTN, and MAP3K1 in the LRG, where PI3KCA and TP53 displayed mutation rates of 36% and 28%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Interestingly, PI3KCA, TP53, CATA3, and TTN consistently occupied the top 4 positions in both groups, playing pivotal roles in modulating IDC. Notably, patients in the HRG exhibited higher TMB values compared to those in the LRG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In conclusion, the integration of TMB and risk score demonstrated improved predictive ability regarding the prognosis of IDC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlations between drugs and prognostic ERGs\u003c/h2\u003e \u003cp\u003eDrug sensitivity analyses of anticancer drugs were carried out, and statistically remarkable disparities in the sensitivity of 118 drugs were identified (\u003cb\u003eAdditional file 17\u003c/b\u003e), with the top 20 drugs showing the smallest p-values selected for further analysis among different risk groups. Notably, low-risk patients showed significantly greater sensitivities to drugs like A.769662, AP.25354, Gemcitabine, Sunitinib, and VX.680 compared to higher risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Nevertheless, higher risk patients exhibited lower IC\u003csub\u003e50\u003c/sub\u003e for GW-441756 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), implying that IDC patients at higher risk were inclined to display reduced resistance to chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Subsequently, the correlation heatmap revealed that there was the strongest positive relationship between vinorelbine and PGK1 (cor\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and the strongest negative association was found between AZ682 and GYPC (cor = -0.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Furthermore, there was a significant positive correlation between the risk score and these 10 drugs (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).The research findings suggested that these drugs had the potential to improve the treatment of IDC patients by targeting prognostic ERGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cell clusters identification in scRNA-seq dataset GSE195861\u003c/h2\u003e \u003cp\u003eAfter integrating and filtering the original data, the data contained 15,894 cells and 23,486 genes before quality control (QC), retaining 11,629 cells and 23,486 genes after QC\u003cb\u003e(Additional file 18a)\u003c/b\u003e, and the top 2,000 HVGs were picked for later analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Cells from different IDC samples combined showed a compact distribution pattern without batch effects (\u003cb\u003eAdditional file 18b\u003c/b\u003e). The scree plot was employed to determine the optimal dimensionality, which was 20, and the top 20 PCs were retained for downstream analysis (\u003cb\u003eAdditional file 18c-d\u003c/b\u003e). Then, UMAP clustering identified a total of 23 distinct cell clusters(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). Next, 9 cell clusters were annotated as T cells, NK cells, monocytes, epithelial cells, macrophages, B cells, fibroblasts, erythroid cells, and plasma cells. The bubble plot was generated to visualize the marker genes\u0026rsquo; expressions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec-d, \u003cb\u003eAdditional file 19\u003c/b\u003e). Specifically, PGK1, CALM2, GYPC, and IL16 were highly expressed in B and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). Combined with existing literature [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], this led to recognizing B and NK cells as key cells for further analysis. B and NK cells correlated with proline catabolism and MGMT-mediated DNA damage reversal (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef, \u003cb\u003eAdditional file 20\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Role of prognostic ERGs in NK cells and B cells\u003c/h2\u003e \u003cp\u003eFollowing the identification of NK cells as key cells, a pseudo-time analysis was conducted to infer that the differentiation trajectories of NK cells were divided into 5 subtypes. Notably, subtype 2 differentiates earlier, while subtype 1 differentiates later. The differentiation of NK cells progressed through 7 distinct states, indicating the heterogeneity within NK cells. Notably, state 1 differentiated earlier, while state 2 differentiated later (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-c). In addition, the expression patterns of prognostic ERGs across pseudo-time trajectories were unveiled in NK cells. As NK cells differentiated, the overall expression level of IL16 showed a downward trend, while the expression level of PGK1 exhibited an upward trend. The expression levels of GYPC, CALM2, and PHKA1 demonstrated a pattern of first increasing and then decreasing. The expression of the other prognostic ERGs generally displayed no notable temporal changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePseudo-time analysis to confirm the differentiation status of B cells: B cells were classified into 3 subtypes. Subtype 2 was predominant in the early stage, while subtype 1 was in the later stage. The differentiation of B cells progressed through 7 distinct states. States 4 and 5 differentiated earlier, whereas states 2 and 1 differentiated later (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee-g). Subsequently, the expression levels of prognostic ERGs were analyzed, as their alterations were particularly remarkable, thus providing insights into the temporal fluctuations of gene expression. The overall expression levels of GYPC and PHKA1 showed an upward trend during the differentiation process of B cells. Conversely, the overall expression levels of PGK1 and CALM2 exhibited a downward trend. The expression level of IL16 initially increased and then decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eh). These observations revealed the pivotal roles played by NK cells and B cells in the pathogenesis of IDC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Cell communication landscaping\u003c/h2\u003e \u003cp\u003eThe cell communication analysis network diagram illustrated the number and strength of interactions among annotated cells, revealing that NK cells and B cells communicated with several other cell types. Notably, there was a strong interaction between NK cells and macrophages in IDC tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). B cells had strong interactions with macrophages, epithelial cells, and NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb-c). It was worth emphasizing that the signaling molecules involved in the communication among different cell types were presented in the bubble plot. The connections between NK cells and macrophages were mainly established through the receptor-ligand pairs of SPP1-CD44 and LGALS9-CD45 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). Similarly, B cells formed connections with the aforementioned 3 types of cells mainly via the receptor-ligand pairs of MIF-(CD74\u0026thinsp;+\u0026thinsp;CD44) and MIF-(CD74\u0026thinsp;+\u0026thinsp;CXCR4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee). These findings regarding cellular communication not only highlighted the dynamic nature of key cell-cell relationships in IDC, but also opened new avenues for targeted therapies in IDC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.10 RT-qPCR confirmation of prognostic ERGs\u003c/h2\u003e \u003cp\u003eBy analyzing prognostic ERGs, it was found that TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16 were important contributors to IDC (\u003cb\u003eAdditional file 21\u003c/b\u003e). Subsequently, the results of an RT-qPCR experiment proved that compared to the control group, the mRNA expression levels of PGK1, SCG2, CALM2, and PHKA1 were notably increased in IDC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea-d). Additionally, significantly lower expression of GYPC and IL16 was observed in IDC patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ee-f). However, TRDN and MLIP was found to be expressed at lower levels in IDC patients, with no significant difference being observed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eg-h). Overall, the expression trends of these prognostic ERGs in were consistent with those observed in the IDC-training set, supporting the reliability of the prognostic ERGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIDC, constituting 70%-75% of breast cancer cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], is distinguished by its aggressive phenotype and potent metastatic capacity, resulting in markedly inferior survival outcomes relative to other subtypes (e.g., invasive lobular carcinoma, ILC) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] Despite established molecular markers (ER, PR, Ki67, HER2) playing a pivotal role in diagnosis and treatment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], their reliance on invasive pathological testing and the lack of efficacious therapeutic targets exacerbate high rates of recurrence, metastasis, and dismal prognosis in IDC [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Consequently, the discovery of innovative biomarkers and therapeutic targets is crucial.\u003c/p\u003e \u003cp\u003eEmerging evidence indicates that exercise elicits anti-cancer effects by modulating metabolic, immune, and cellular stress pathways [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. ERGs have shown promise in governing tumor progression across various cancers [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. For instance, TLR1 has suppressed glioma proliferation and migration while enhancing prognosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, the mechanistic role of ERGs in IDC remains largely uncharacterized.\u003c/p\u003e \u003cp\u003eAdvances in scRNA-seq have unveiled unparalleled insights into the heterogeneity of the tumor microenvironment. The high-definition profiling afforded by scRNA-seq enables meticulous identification of critical cell subpopulations and regulatory networks [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], offering new opportunities to decipher the dynamic expression and functional mechanisms of ERGs in IDC.\u003c/p\u003e \u003cp\u003e This study integrated publicly available transcriptomic and single-cell RNA-seq data to analyze exercise-associated survival-related genes in IDC comprehensively.A risk prediction model was built to evaluate the predictive efficacy of these genes, followed by in-depth analyses of their associations with immune infiltration, metabolic pathways, and drug sensitivity. We identified key cell populations and explored the expression patterns of prognostic genes within these cells. Ultimately, in vitro studies confirmed the prognostic genes' expression and functional significance in IDC cell lines, evaluating their potential as therapeutic targets.\u003c/p\u003e \u003cp\u003eWe built a prognostic model based on physical activity-associated genes from the IDC cohort data to evaluate their impact on patient survival and treatment outcomes. Based on prognostic ERGs, this model underwent additional validation through an internal validation set and the GSE26304 dataset, confirming its reliability and consistency over time.\u003c/p\u003e \u003cp\u003eEight independent prognostic endoplasmic reticulum stress-responsive genes (ERGs) were identified, encompassing Phosphoglycerate kinase 1 (PGK1). PGK1 also functions as a cofactor for polymerase α and promotes angiogenesis via secretion by tumor cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the metabolic shift toward enhanced glycolysis in IDC, elevated PGK1 expression drives rapid cancer cell proliferation and invasion [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Secretogranin II (SCG2) belongs to the chromogranin family of acidic secretory proteins [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In colorectal cancer, SCG2 correlates with immune cell infiltration and macrophage polarization in the tumor microenvironment.. Analogous mechanisms may govern IDC progression by modulating immune responses and stromal crosstalk[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Calmodulin 2 (CALM2), a calcium-binding protein, regulates signal transduction, cell cycle progression, and proliferation [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Dysregulation of calcium signaling pathways in IDC may underlie aberrant cell growth and metastatic dissemination [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Phosphorylase kinase regulatory subunit alpha 1 (PHKA1) is a regulator of glycogen metabolism. PHKA1 may modulate energy homeostasis in IDC cells. Altered PHKA1 activity could impact cancer cell proliferation and invasion by disrupting metabolic equilibrium[\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Muscle-rich A-type lamins interacting protein (MLIP) represents a newly identified protein involved in cellular homeostasis and stress adaptation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. MLIP may govern metabolic processes, DNA repair, and cell cycle progression in IDC. Glycophorin C (GYPC) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], an erythrocyte membrane protein, exhibits aberrant expression in cancer. GYPC-mediated membrane stability could indirectly modulate tumor progression [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Interleukin 16 (IL16), a multifunctional cytokine, modulates immune responses by regulating T-cell migration and activation [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] IL16 may sculpt the tumor immune microenvironment in IDC, influencing immune surveillance and escape mechanisms [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Triadin (TRDN), a regulator of calcium homeostasis in muscle cells, is associated with cardiac and muscular disorders via mutations [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. While its role in IDC remains undefined, dysregulated calcium signaling may facilitate cancer cell motility and survival [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The RT-qPCR validation of PGK1, SCG2, CALM2, PHKA1, GYPC, and IL16 corroborated the bioinformatics predictions, highlighting the robustness of the bioinformatics results. Collectively, these findings highlight TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16 as potential prognostic biomarkers in IDC. Further investigation into their functional roles may uncover novel therapeutic avenues for this aggressive breast cancer subtype.\u003c/p\u003e \u003cp\u003eAs detailed in Sections \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e, \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e, and \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003e3.7\u003c/span\u003e, our analysis unveiled significant enrichment of the insulin signaling pathway (ISP) and hematopoietic cell lineage in IDC, underscoring their critical roles in tumorigenesis and progression. The insulin/IGF-1 signaling pathway serves as a central regulator of cellular metabolism, proliferation, survival, and differentiation. Activated by insulin and insulin-like growth factors (IGF-1/IGF-2), this pathway engages membrane receptors (IGF1R and InsR), initiating downstream cascades such as PI3K/AKT/mTOR and MAPK/ERK [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In IDC, dysregulation of ISP manifests as receptor overexpression (e.g., IGF1R crosstalk with estrogen signaling in ER\u0026thinsp;+\u0026thinsp;subtypes, leading to uncontrolled proliferation and endocrine therapy resistance) and CDH1 loss-mediated metastasis (via β-catenin-independent EMT induction) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The hematopoietic cell lineage, encompassing myeloid (e.g., tumor-associated macrophages, TAMs) and lymphoid cells, modulates the tumor microenvironment (TME) via immune regulation and pleiotropic genetic variants. Myeloid cells (e.g., M2-type TAMs secreting IL-10/TGF-β) establish an immunosuppressive niche, whereas lymphoid dysfunction (e.g., reduced eosinophils compromising immune surveillance) and 4,093 blood trait-associated pleiotropic variants (e.g., PIK3CA mutations enhancing glycolysis via PI3K activation) further influence IDC progression [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Notably, ISP synergizes with hormonal signaling to establish a metabolic-proliferative axis, whereas hematopoietic aberrations give rise to an immune evasion axis. Specific molecular events (e.g., CDH1 loss) may bridge these axes, exacerbating malignancy (e.g., amplifying IGF signaling while suppressing immune responses) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. These findings underscore the intricate interplay of hormonal, metabolic, immune, and genetic pathways in propelling IDC progression, advocating for multi-targeted therapeutic strategies based on systems biology.\u003c/p\u003e \u003cp\u003eWe used ssGSEA to compare immune cell infiltration between high- and low-risk IDC cohorts, identifying 13 immune cell types with differential abundance. For instance, triple-negative breast cancer (TNBC) exhibits pronounced lymphocyte infiltration, including tumor-infiltrating B cells (TIBs), which drive early-stage aggressiveness via IL-1β\u0026ndash;NFκB\u0026ndash;MMP axis activation, thereby promoting angiogenesis, proliferation, and invasion [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. B cells in tumor-draining lymph nodes (TDLNs) secrete granzyme B (GZMB), implicating cytotoxic mechanisms in antitumor immunity or immune escape[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. NK cells mediate direct tumor cytotoxicity, whereas B cells coordinate antigen presentation and immune activation. Early B-cell infiltration in TNBC augments IL-1β-driven invasiveness, facilitating DCIS-to-IDC transition, whereas senescent cancer-associated fibroblasts (senCAFs) suppress NK-cell cytotoxicity via ECM remodeling, thereby promoting immune evasion [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Hypoxia-induced proline hydroxylation (HYP) of collagen α-1(I) in TNBC TME underscores the ECM\u0026rsquo;s role in tumor survival[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Dual roles of TIBs (e.g., plasmablasts) hinge on contextual cues: IL-6 correlates with impaired dendritic/T-cell function [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], whereas IL-16 promotes macrophage-dependent protumorigenesis [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Conversely, IL-6 deficiency enhances Th1/IFN-γ responses, suggesting pleiotropic immunomodulatory effects [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTCGA somatic mutation data analysis identified high-risk IDC-associated mutations in PIK3CA, TP53, GATA3, and TTN. PIK3CA mutations (34% prevalence in breast cancer) predominantly occur in exons 9 (helical domain) and 20 (kinase domain), hyperactivating the PI3K/AKT/mTOR pathway, especially in HER2\u0026thinsp;+\u0026thinsp;subtypes [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. TP53 mutations, detectable as early as ductal carcinoma in situ (DCIS), drive carcinogenesis via SREBF2-mediated cholesterol metabolism, fueling the estrogen-ESR1 proliferative axis [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIDC\u0026rsquo;s heterogeneous drug responses necessitate personalized treatment. Farnesyltransferase inhibitors (FTIs, e.g., GW-441756) exhibit preclinical efficacy by inhibiting Ras farnesylation, inducing G2/M cell cycle, and promoting apoptosis. Although clinical trials of FTIs (e.g., tipifarnib) have shown limited efficacy, FNTB-high TNBC subsets may derive benefit from FTI-based combinational therapies [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Beyond oncology, FTIs are being investigated for progeria and parasitic infections [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study integrates ERGs to construct a robust prognostic model for IDC, validated across multiple datasets. However, limitations encompass the requirement for larger cohorts and functional validation of ERG-mediated crosstalk with other pathways. The discrepancy between the RT-qPCR results of TRDN/ MLIP and the bioinformatics analysis may stem from multiple factors: potential differences in sample disease stages, the reliance of bioinformatics on large-scale data, versus the limited sample size in experimental validation, which may not fully capture population characteristics. Single-cell RNA sequencing could further clarify B/NK-cell differentiation and their dynamics in the IDC microenvironment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"210\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003eAbbreviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003efull name\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ebreast cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBRCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInvasive ductal carcinoma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eductal carcinoma in situ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eexercise-related genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eERGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003einsulin-like growth factor 1 receptor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIGF1R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecopy number variation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ethe Cancer Genome Atlas\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edifferentially expressed genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edifferentially expressed ERGs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDE-ERGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprotein-protein interaction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eproportional hazards\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eleast absolute shrinkage and selection perator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehigh-risk group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHRG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elow-risk group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLRG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKaplan-Meier\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ereceiver operating characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003earea under the curve\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eoverall survival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGene set enrichment analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003enormalized enrichment score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edifferentially infiltrating immune cells\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDICs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etumor mutation burden\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehalf-maximal inhibitory concentration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIC50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehighly variable genes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHVGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprincipal component analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprincipal components\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePCs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003euniform manifold approximation and projection\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBranched Expression Analysis Modeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBEAM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ereverse transcription quantitative PCR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003equality control\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMuscle-rich A-type lamins interacting protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMLIP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGlycophorin C\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGYPC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCalmodulin 2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCALM2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhosphorylase kinase regulatory subunit alpha 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePHKA1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTriadin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRDN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin 16\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIL16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etumor microenvironment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etumor-associated macrophages\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTAMs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etriple-negative breast cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etumor-infiltrating B cells\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTIBs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etumor-draining lymph nodes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTDLNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003egranzyme B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGZMB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esenescent cancer-associated fibroblasts\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003esenCAFs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehydroxylation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHYP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eductal carcinoma in situ\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFarnesyltransferase inhibitors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFTIs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the Ethics Committee of the The First Affiliated Hospital of Xinjiang Medical University. The approval number and date of approval are as follows: [ K202504- 66] and [April, 2025]. All patients provided written informed consent at the time of clinical sample collection for experiments, ensuring that the research process complied with ethical norms and that patients' rights and wishes were fully respected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eublish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in [Gene Expression Omnibus (GEO)] at [https://www.ncbi.nlm.nih.gov/geo/], GSE26304, GSE195861, GSE26304, GSE195861. The IDC patient database at [https://www.cancer.gov/ccg/research/genome-sequencing/tcga]. The exercise-related genes (ERGs) in [ Molecular Signatures Database (MSigDB)] at [https://www.gsea-msigdb.org/gsea/msigdb].All data in this study can be obtained from the authors of this study.All the data of this study can be obtained from the corresponding author Gaojunxi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding details:\u003c/strong\u003e This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJunxi Gao: Conceptualization, Data curation, Validation, Visualization, Writing–original draft. Youxin Tang: Visualization, Writing–review \u0026amp; editing. Peng Zhang: Writing–review \u0026amp; editing.Yuan Yuan: Writing–review \u0026amp; editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFilho AM, Laversanne M, Ferlay J, Colombet M, Pi\u0026ntilde;eros M, Znaor A et al. The GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide. Int J Cancer 2025;156:1336-1346.\u003c/li\u003e\n\u003cli\u003eAnderson WF, Matsuno R. Breast cancer heterogeneity: a mixture of at least two main types? J Natl Cancer Inst 2006;98:948-951.\u003c/li\u003e\n\u003cli\u003eLi CI, Uribe DJ, Daling JR. Clinical characteristics of different histologic types of breast cancer. Br J Cancer 2005;93:1046-1052.\u003c/li\u003e\n\u003cli\u003eDuraker N, Hot S, Akan A, Nayır P. A Comparison of the Clinicopathological Features, Metastasis Sites and Survival Outcomes of Invasive Lobular, Invasive Ductal and Mixed Invasive Ductal and Lobular Breast Carcinoma. Eur J Breast Health 2020;16:22-31.\u003c/li\u003e\n\u003cli\u003eChen Z, Yang J, Li S, Lv M, Shen Y, Wang B et al. Invasive lobular carcinoma of the breast: A special histological type compared with invasive ductal carcinoma. PLoS One 2017;12:e0182397.\u003c/li\u003e\n\u003cli\u003eFan L, Strasser-Weippl K, Li JJ, St Louis J, Finkelstein DM, Yu KD et al. Breast cancer in China. Lancet Oncol 2014;15:e279-289.\u003c/li\u003e\n\u003cli\u003eDeng JL, Xu YH, Wang G. Identification of Potential Crucial Genes and Key Pathways in Breast Cancer Using Bioinformatic Analysis. Front Genet 2019;10:695.\u003c/li\u003e\n\u003cli\u003eHojman P, Gehl J, Christensen JF, Pedersen BK. Molecular Mechanisms Linking Exercise to Cancer Prevention and Treatment. Cell Metab 2018;27:10-21.\u003c/li\u003e\n\u003cli\u003eZhu C, Ma H, He A, Li Y, He C, Xia Y. Exercise in cancer prevention and anticancer therapy: Efficacy, molecular mechanisms and clinical information. Cancer Lett 2022;544:215814.\u003c/li\u003e\n\u003cli\u003eLu L, Hu Y, Wang C, Jiang F, Wu C. Methylation and Expression of the Exercise-Related TLR1 Gene Is Associated With Low Grade Glioma Prognosis and Outcome. Front Mol Biosci 2021;8:747933.\u003c/li\u003e\n\u003cli\u003eChen Y, Chen X, Luo Z, Kang X, Ge Y, Wan R et al. Exercise-Induced Reduction of IGF1R Sumoylation Attenuates Neuroinflammation in APP/PS1 Transgenic Mice. J Adv Res 2025;69:279-297.\u003c/li\u003e\n\u003cli\u003eZhang Y, Wang D, Peng M, Tang L, Ouyang J, Xiong F et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res 2021;40:81.\u003c/li\u003e\n\u003cli\u003eWang S, Sun ST, Zhang XY, Ding HR, Yuan Y, He JJ et al. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 2023;24.\u003c/li\u003e\n\u003cli\u003eLiu S, Zhao Y, Zhang J, Liu Z. Application of single-cell RNA sequencing analysis of novel breast cancer phenotypes based on the activation of ferroptosis-related genes. Funct Integr Genomics 2023;23:173.\u003c/li\u003e\n\u003cli\u003eQin X, Strand SH, Lee MR, Saraswathibhatla A, van IDGP, Zhu C et al. Single Cell Expression Analysis of Ductal Carcinoma in Situ Identifies Complex Genotypic-Phenotypic Relationships Altering Epithelial Composition. Cancer Res 2025.\u003c/li\u003e\n\u003cli\u003eZhang T, Zhao F, Lin Y, Liu M, Zhou H, Cui F et al. Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression. Theranostics 2024;14:1065-1080.\u003c/li\u003e\n\u003cli\u003eZhang X, Peng L, Luo Y, Zhang S, Pu Y, Chen Y et al. Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nat Commun 2021;12:5291.\u003c/li\u003e\n\u003cli\u003eYan C, Li K, Meng F, Chen L, Zhao J, Zhang Z et al. Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer. J Adv Res 2023;44:149-160.\u003c/li\u003e\n\u003cli\u003eLove MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.\u003c/li\u003e\n\u003cli\u003eWodrich MD, Sawatlon B, Busch M, Corminboeuf C. The Genesis of Molecular Volcano Plots. Acc Chem Res 2021;54:1107-1117.\u003c/li\u003e\n\u003cli\u003eGu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016;32:2847-2849.\u003c/li\u003e\n\u003cli\u003eChen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 2011;12:35.\u003c/li\u003e\n\u003cli\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012;16:284-287.\u003c/li\u003e\n\u003cli\u003eLiu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evid Based Complement Alternat Med 2020;2020:3023127.\u003c/li\u003e\n\u003cli\u003eLei J, Qu T, Cha L, Tian L, Qiu F, Guo W et al. Clinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers. J Surg Oncol 2023;128:510-518.\u003c/li\u003e\n\u003cli\u003eLi Y, Lu F, Yin Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn\u0026apos;s disease. Sci Rep 2022;12:11340.\u003c/li\u003e\n\u003cli\u003eLiu TT, Li R, Huo C, Li JP, Yao J, Ji XL et al. Identification of CDK2-Related Immune Forecast Model and ceRNA in Lung Adenocarcinoma, a Pan-Cancer Analysis. Front Cell Dev Biol 2021;9:682002.\u003c/li\u003e\n\u003cli\u003eHeagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56:337-344.\u003c/li\u003e\n\u003cli\u003eSachs MC. plotROC: A Tool for Plotting ROC Curves. J Stat Softw 2017;79.\u003c/li\u003e\n\u003cli\u003eCharoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017;18:248-262.\u003c/li\u003e\n\u003cli\u003eOrifjon S, Jammatov J, Sousa C, Barros R, Vasconcelos O, Rodrigues P. Translation and Adaptation of the Adult Developmental Coordination Disorder/Dyspraxia Checklist (ADC) into Asian Uzbekistan. Sports (Basel) 2023;11.\u003c/li\u003e\n\u003cli\u003eMayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018;28:1747-1756.\u003c/li\u003e\n\u003cli\u003eGeeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014;9:e107468.\u003c/li\u003e\n\u003cli\u003eHao Y, Hao S, Andersen-Nissen E, Mauck WM, 3rd, Zheng S, Butler A et al. Integrated analysis of multimodal single-cell data. Cell 2021;184:3573-3587.e3529.\u003c/li\u003e\n\u003cli\u003eZhang Z, Zhu M, Xie Q, Larkin RM, Shi X, Zheng B. CProtMEDIAS: clustering of amino acid sequences encoded by gene families by MErging and DIgitizing Aligned Sequences. Brief Bioinform 2022;23.\u003c/li\u003e\n\u003cli\u003eWang X, Wang G, Xu Q, Li Y, Song W, Liu Z et al. Potential tumor-specific antigens and immune landscapes identification for mRNA vaccine in thyroid cancer. Front Oncol 2024;14:1480028.\u003c/li\u003e\n\u003cli\u003eJin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021;12:1088.\u003c/li\u003e\n\u003cli\u003eSun Y, Pan Z, Wang Z, Wang H, Wei L, Cui F et al. Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes. J Transl Med 2024;22:894.\u003c/li\u003e\n\u003cli\u003eLi J, Du J, Wang Y, Jia H. A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma. Front Genet 2021;12:722992.\u003c/li\u003e\n\u003cli\u003eYang Y, Luo D, Gao W, Wang Q, Yao W, Xue D et al. Combination Analysis of Ferroptosis and Immune Status Predicts Patients Survival in Breast Invasive Ductal Carcinoma. Biomolecules 2023;13.\u003c/li\u003e\n\u003cli\u003eBurstein HJ. Systemic Therapy for Estrogen Receptor-Positive, HER2-Negative Breast Cancer. N Engl J Med 2020;383:2557-2570.\u003c/li\u003e\n\u003cli\u003eYin L, Duan JJ, Bian XW, Yu SC. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res 2020;22:61.\u003c/li\u003e\n\u003cli\u003eChen Y, Huang L, Luo Z, Han D, Luo W, Wan R et al. Pantothenate-encapsulated liposomes combined with exercise for effective inhibition of CRM1-mediated PKM2 translocation in Alzheimer\u0026apos;s therapy. J Control Release 2024;373:336-357.\u003c/li\u003e\n\u003cli\u003eShi Q, Ying H, Weng W. Targeting exercise-related genes and placental growth factor for therapeutic development in head and neck squamous cell carcinoma. Front Pharmacol 2024;15:1476076.\u003c/li\u003e\n\u003cli\u003eDing M, Zhang Z, Chen Z, Song J, Wang B, Jin F. Association between periodontitis and breast cancer: two-sample Mendelian randomization study. Clin Oral Investig 2023;27:2843-2849.\u003c/li\u003e\n\u003cli\u003eQiu S, Zou L, Qiu R, Wang X. Circular RNA circHMCU promotes breast tumorigenesis through miR-4458/PGK1 regulatory cascade. Hereditas 2023;160:12.\u003c/li\u003e\n\u003cli\u003eXiong Y, Zhang X, Xie W, Yin Y, Qian Y, Ying X et al. DUSP4 inhibited tumor cell proliferation by downregulating glycolysis via p-ERK/p-PGK1 signaling in ovarian cancer. Cancer Cell Int 2025;25:87.\u003c/li\u003e\n\u003cli\u003eMiao C, You X, Zhang Z, Jiang Z, Liu L, Jia Y et al. SCG2 Mediates HNSCC Progression With CCL2/TGF\u0026beta;1(high) M2 Macrophage Infiltration. Oral Dis 2025;31:782-795.\u003c/li\u003e\n\u003cli\u003eShi L, Wang H, Sun Y, Xu N, Pei A, Zhang N. Development and validation of a disulfidptosis-related prognostic model for colorectal cancer using multi-omics analysis. Discov Oncol 2025;16:338.\u003c/li\u003e\n\u003cli\u003eXie Y, Guan S, Li Z, Cai G, Liu Y, Li G et al. Identification of a metabolic-immune signature associated with prognosis in colon cancer and exploration of potential predictive efficacy of immunotherapy response. Clin Exp Med 2025;25:46.\u003c/li\u003e\n\u003cli\u003eLv J, Chen F, Lv L, Zhang L, Zou H, Liu Y et al. LncRNA ABHD11-AS1 Elevates CALM2 to Promote Metastasis of Thyroid Cancer Through Sponging miR-876-5p. Biochem Genet 2025.\u003c/li\u003e\n\u003cli\u003eYang J, Li S, Wang J, Liu G, Zhang C, Li X et al. Calmodulin 2 expression is associated with poor prognosis in breast cancer. Pathol Res Pract 2024;258:155326.\u003c/li\u003e\n\u003cli\u003eLang Y, Kong X, Liu B, Jin X, Chen L, Xu S. microRNA-651-5p affects the proliferation, migration, and invasion of lung cancer cells by regulating Calmodulin 2 expression. Clin Respir J 2023;17:754-763.\u003c/li\u003e\n\u003cli\u003eGuo QR, Zhang GB, Zhou WM, Lu Y, Chen XZ, Deng ZF et al. m(6)A modification of lncRNA PHKA1-AS1 enhances Actinin Alpha 4 stability and promotes non-small cell lung cancer metastasis. MedComm (2020) 2024;5:e547.\u003c/li\u003e\n\u003cli\u003eChen J, Cao S, Situ B, Zhong J, Hu Y, Li S et al. Metabolic reprogramming-based characterization of circulating tumor cells in prostate cancer. J Exp Clin Cancer Res 2018;37:127.\u003c/li\u003e\n\u003cli\u003eChen Y, Gao Y, Jiang L. Phosphorylase kinase regulatory subunit alpha 1 as a novel biomarker involved in development and progression of lung cancer: comprehensive bioinformatic analysis and experiment validation. J Physiol Pharmacol 2024;75.\u003c/li\u003e\n\u003cli\u003eHamwi MN, Elsayed E, Dabash H, Abuawad A, Aweer NA, Al Zeir F et al. MLIP and Its Potential Influence on Key Oncogenic Pathways. Cells 2024;13.\u003c/li\u003e\n\u003cli\u003eCattin ME, Deeke SA, Dick SA, Verret-Borsos ZJA, Tennakoon G, Gupta R et al. Expression of murine muscle-enriched A-type lamin-interacting protein (MLIP) is regulated by tissue-specific alternative transcription start sites. J Biol Chem 2018;293:19761-19770.\u003c/li\u003e\n\u003cli\u003eLiu S, Liu D, Bender CM, Erickson KI, Sereika SM, Shaffer JR et al. Associations between DNA methylation and cognitive function in early-stage hormone receptor-positive breast cancer patients. medRxiv 2024.\u003c/li\u003e\n\u003cli\u003eGuo C, Tang Y, Zhang Y, Li G. Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis. Front Mol Biosci 2021;8:645388.\u003c/li\u003e\n\u003cli\u003eWang J, Chen T, Yu X, N OU, Tan L, Jia B et al. Identification and validation of smoking-related genes in lung adenocarcinoma using an in vitro carcinogenesis model and bioinformatics analysis. J Transl Med 2020;18:313.\u003c/li\u003e\n\u003cli\u003eWen Z, Liu T, Xu X, Acharya N, Shen Z, Lu Y et al. Interleukin-16 enhances anti-tumor immune responses by establishing a Th1 cell-macrophage crosstalk through reprogramming glutamine metabolism in mice. Nat Commun 2025;16:2362.\u003c/li\u003e\n\u003cli\u003eHuang Y, Fan M, Liu Y, Jiang X, Du K, Wu A et al. Novel biomarkers and drug correlations of non-canonical WNT signaling in prostate and breast cancer. Discov Oncol 2024;15:511.\u003c/li\u003e\n\u003cli\u003eTzeng YT, Chu PY, Yong SB, Hsu TS, Tseng LM, Hou MF et al. Multi-omic profiling of breast tumor microenvironment uncovers a role of mitochondrial calcium gatekeepers. J Cancer 2024;15:3663-3674.\u003c/li\u003e\n\u003cli\u003eMarty I. Triadin: a multi-protein family for which purpose? Cell Mol Life Sci 2004;61:1850-1853.\u003c/li\u003e\n\u003cli\u003eManno C, Tammineni E, Figueroa L, Marty I, R\u0026iacute;os E. Quantification of the calcium signaling deficit in muscles devoid of triadin. PLoS One 2022;17:e0264146.\u003c/li\u003e\n\u003cli\u003eMussa BM, Venkatachalam T, Srivastava A, Al-Habshi A, Abdelgadir E, Bashier A et al. Identification of novel differentially expressed genes in type 1 diabetes mellitus complications using transcriptomic profiling of UAE patients: a multicenter study. Sci Rep 2022;12:16316.\u003c/li\u003e\n\u003cli\u003eAlbogami SM, Asiri Y, Asiri A, Alnefaie AA, Alnefaie S. Effects of neoadjuvant therapies on genetic regulation of targeted pathways in ER+ primary ductal breast carcinoma: A meta-analysis of microarray datasets. Saudi Pharm J 2021;29:656-669.\u003c/li\u003e\n\u003cli\u003eIwase T, Harano K, Masuda H, Kida K, Hess KR, Wang Y et al. Quantitative hormone receptor (HR) expression and gene expression analysis in HR+ inflammatory breast cancer (IBC) vs non-IBC. BMC Cancer 2020;20:430.\u003c/li\u003e\n\u003cli\u003eNagle AM, Levine KM, Tasdemir N, Scott JA, Burlbaugh K, Kehm J et al. Loss of E-cadherin Enhances IGF1-IGF1R Pathway Activation and Sensitizes Breast Cancers to Anti-IGF1R/InsR Inhibitors. Clin Cancer Res 2018;24:5165-5177.\u003c/li\u003e\n\u003cli\u003eBarsky SH, McPhail K, Wang J, Hoffman RM, Ye Y. Bone Marrow Origin of Mammary Phagocytic Intraductal Macrophages (Foam Cells). Int J Mol Sci 2025;26.\u003c/li\u003e\n\u003cli\u003ePardo-Cea MA, Farr\u0026eacute; X, Esteve A, Palade J, Esp\u0026iacute;n R, Mateo F et al. Biological basis of extensive pleiotropy between blood traits and cancer risk. Genome Med 2024;16:21.\u003c/li\u003e\n\u003cli\u003eToney NJ, Opdenaker LM, Frerichs L, Modarai SR, Ma A, Archinal H et al. B cells enhance IL-1 beta driven invasiveness in triple negative breast cancer. Sci Rep 2025;15:2211.\u003c/li\u003e\n\u003cli\u003eda Silveira GG, Oliveira-Costa JP, Soave DF, Zanetti JS, Soares FA, Ribeiro-Silva A. Relationship between B-cell-specific Moloney murine leukemia virus integration site 1 (BMI-1) and homologous recombination regulatory genes in invasive ductal breast carcinomas. Histol Histopathol 2012;27:1353-1359.\u003c/li\u003e\n\u003cli\u003eAlmekinders MM, Bismeijer T, Kumar T, Yang F, Thijssen B, van der Linden R et al. Comprehensive multiplexed immune profiling of the ductal carcinoma in situ immune microenvironment regarding subsequent ipsilateral invasive breast cancer risk. Br J Cancer 2022;127:1201-1213.\u003c/li\u003e\n\u003cli\u003eTaylor H, Spruill L, Jensen-Smith H, Rujchanarong D, Hulahan T, Ivey A et al. Spatial localization of collagen hydroxylated proline site variation as an ancestral trait in the breast cancer microenvironment. Matrix Biol 2025;136:71-86.\u003c/li\u003e\n\u003cli\u003ePilcher W, Thomas BE, Bhasin SS, Jayasinghe RG, Yao L, Gonzalez-Kozlova E et al. Cross center single-cell RNA sequencing study of the immune microenvironment in rapid progressing multiple myeloma. NPJ Genom Med 2023;8:3.\u003c/li\u003e\n\u003cli\u003eGuan X, Wang Y, Fang T, Wang J, Li R, Hao M et al. Lymphoma cell-driven IL-16 is expressed in activated B-cell-like diffuse large B-cell lymphomas and regulates the pro-tumor microenvironment. Haematologica 2025;110:425-438.\u003c/li\u003e\n\u003cli\u003eLiu S, Lei Z, Li J, Wang L, Jia R, Liu Z et al. Interleukin 16 contributes to gammaherpesvirus pathogenesis by inhibiting viral reactivation. PLoS Pathog 2020;16:e1008701.\u003c/li\u003e\n\u003cli\u003eKandula M, Chennaboina KK, Ys AR, Raju S. Phosphatidylinositol 3-kinase (PI3KCA) oncogene mutation analysis and gene expression profiling in primary breast cancer patients. Asian Pac J Cancer Prev 2013;14:5067-5072.\u003c/li\u003e\n\u003cli\u003eXu G, Yu J, Lyu J, Zhan M, Xu J, Huang M et al. Proteogenomic Landscape of Breast Ductal Carcinoma Reveals Tumor Progression Characteristics and Therapeutic Targets. Adv Sci (Weinh) 2024;11:e2401041.\u003c/li\u003e\n\u003cli\u003eZhang Q, Li Y, Yu BH, Bi R, Xu X, Cheng Y et al. Carcinoma arising in microglandular adenosis of the breast: clinicopathological and genetic analysis. J Clin Pathol 2025.\u003c/li\u003e\n\u003cli\u003eSong SY, Meszoely IM, Coffey RJ, Pietenpol JA, Leach SD. K-Ras-independent effects of the farnesyl transferase inhibitor L-744,832 on cyclin B1/Cdc2 kinase activity, G2/M cell cycle progression and apoptosis in human pancreatic ductal adenocarcinoma cells. Neoplasia 2000;2:261-272.\u003c/li\u003e\n\u003cli\u003eBachmann HS, Jung D, Link T, Arnold A, Kantelhardt E, Thomssen C et al. FNTB Promoter Polymorphisms Are Independent Predictors of Survival in Patients with Triple Negative Breast Cancer. Cancers (Basel) 2022;14.\u003c/li\u003e\n\u003cli\u003eMoorthy NS, Sousa SF, Ramos MJ, Fernandes PA. Farnesyltransferase inhibitors: a comprehensive review based on quantitative structural analysis. Curr Med Chem 2013;20:4888-4923.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Invasive ductal carcinoma, Exercise, Single-cell RNA sequencing, Bulk RNA","lastPublishedDoi":"10.21203/rs.3.rs-8444169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8444169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs the predominant subtype of breast cancer, invasive ductal carcinoma (IDC) is characterized by its aggressive invasive behavior and strong metastatic capacity. Exercise has been shown to confer multiple benefits in cancer prevention. This research sought elucidate the exercise-related mechanisms in IDC, emphasizing risk stratification therapeutic implications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIDC-related datasets downloaded were from the gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases. Differential expression analysis, Cox univariable survival analysis, and machine learning methods were used to select exercise-related genes (ERGs) and construct a risk model. Subsequently, the prognostic evaluations were enhanced through independent survival analysis, nomogram development, enrichment profiling, tumor immune microenvironment assessment, and chemosensitivity testing. Besides, GSE195861 was analyzed to determine key cells and perform pseudo-time and cell communication analyses. Finally, Prognostic ERG gene expression was confirmed by reverse transcription quantitative polymerase chain reaction (RT-qPCR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA prognostic risk model with 8 prognostic ERGs (TRDN, PGK1, SCG2, CALM2, PHKA1, MLIP, GYPC, and IL16) was constructed and demonstrated a strong prognostic effect. Subsequently, a nomogram was developed according to tumor stage and gender, showing strong predictive power for IDC prognosis. Subsequently, immune cells like immature B cells, pathways like hematopoietic cell lineage, and drug sensitivities to GW-441756 were detected to be linked to the risk stratification of IDC patients. Moreover, pseudo-time analysis revealed a notable correlation between prognostic ERGs' expression about differentiation status of key cells (NK cells and B cells), and cell signaling revealed key cell-macrophage interplay. Importantly, RT-qPCR confirmed that PGK1, SCG2, CALM2, and PHKA1 were abundantly expressed, while GYPC and IL16 were lowly expressed in IDC patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlighted the pivotal role of exercise in IDC progression. A novel IDC-related risk model based on prognostic ERGs was developed and validated, and it exhibited robust predictive efficacy for IDC patient outcomes.\u003c/p\u003e","manuscriptTitle":"Integrated bulk RNA and single-cell RNA sequencing to identify and validate exercise-related genes for predicting the prognosis of invasive ductal carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 00:06:42","doi":"10.21203/rs.3.rs-8444169/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":"575327d1-3df3-481d-b831-98b9897feab4","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T12:40:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 00:06:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8444169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8444169","identity":"rs-8444169","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}