Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer

Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer Zhen Xie, Du Cai, Runyi Ye, Zhen Shan, Ying Lin, Feng Gao, Nan Shao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1740378/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 Luminal B breast cancer is routinely treated with chemotherapy and endocrine therapy. However, its sensitivity to treatment remains heterogeneous; therefore, identifying patients who may most benefit, remains crucial. Immune-related genes are reportedly related to the prognosis of breast cancer. The purpose of this study was to evaluate the impact of an immune-related gene signature (IRGS) in predicting the prognosis of patients with Luminal B breast cancer. Methods We selected patients with Luminal B breast cancer from two large datasets: 488 from the Metabric dataset (training cohort) and 250 patients from the cancer genome atlas (TCGA) dataset (validation cohort). Prognostic analysis was performed to test the predictive value of IRGS, and enrichment analysis and ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) were used for deeper function analysis. Results A prognostic IRGS model containing 12 immune-related genes was developed. After which, we separated patients with Luminal B breast cancer into low-risk and high-risk groups in terms of disease-free survival (DFS) ( P < 0.001). Multivariate analysis identified IRGS as an independent prognostic factor. Furthermore, functional analysis showed that the 12 genes were mainly enriched in pathways related to chemotherapy response, whose expression levels showed completely opposing trends in low-risk and high-risk groups. Conclusions The novel IRGS is a satisfactory and reliable biomarker to predict the clinical outcome of patients with Luminal B breast cancer which potentially facilitating individualised management. Further studies are needed to assess the clinical potential in predicting prognosis and the treatment options for Luminal B breast cancer patients. immune-related gene signature Luminal B breast cancer clinical outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer, one of the most common causes of cancer in women, has drawn great attention around the globe. In recent years, breast cancer incidence and mortality rates are still on the rise, which seriously threatens women’s health [ 1 ]. At the molecular level, breast cancer can be generally divided into four subtypes: Luminal A, Luminal B, HER2-enriched, and basal-like [ 2 , 3 ]. Among them, Luminal A and Luminal B are both hormone receptor-positive. Luminal A breast cancer is usually sensitive to endocrine therapy and has a relatively good prognosis. Luminal B subtype includes all types of hormone receptor-positive breast cancer, except for Luminal A, accounting for about one-fourth of the breast cancer cases [ 4 ]. The definition of Luminal B breast cancer is relatively complicated, and its prognosis and treatment sensitivity are inconsistent and varied. Compared with Luminal A breast cancer, the survival rate of patients with Luminal B breast cancer is much lower in clinical practice, and they usually have a worse prognosis [ 5 , 6 ]. Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy as adjuvant treatment, and their prognosis cannot be accurately assessed and remains a matter of controversy. Only a few studies focus on the precise reclassification of Luminal B breast cancer and various appropriate treatment strategies. Therefore, it has become indispensable to find valuable biomarkers to reclassify Luminal B breast cancer more accurately. Immunotherapy is a newly emerging treatment modality in cancer therapy that has promising potential. However, most patients with breast cancer treated by immunotherapy show negative results, except for several trials in selected patients with triple-negative breast cancer. Some studies attempt to examine the effectiveness of immunotherapy by subtyping triple-negative breast cancer to classify their characteristics and prognosis [ 7 – 13 ], whereas no such positive evidence has been observed in patients with Luminal B breast cancer. In the previous studies, several reclassification methods for breast cancer have been proposed, and some of them have shown significant clinical benefits [ 3 , 14 , 15 ]. However, a more precise and effective treatment could be found by using machine learning algorithm to construct a prognostic prediction model based on immune-related genes to reclassify patients with Luminal B breast cancer. Therefore, we constructed an immune-related gene signature (IRGS) to predict clinical outcomes of patients with Luminal B breast cancer, aiming to bring a more accurate and individualized treatment plan with fewer-to-no side effects, which may also provide a foothold for the clinical practice of precision medicine. Methods Patients We collected the gene expression profile of patients with Luminal B breast cancer from two datasets to identify an IRGS. We included 738 patients with Luminal B breast cancer in this study: 488 from the Metabric dataset (training cohort) and 250 from the cancer genome atlas (TCGA) dataset (validation cohort). The level 3 RNA expression profile data of TCGA cohort were downloaded from the GDC Data Portal ( https://portal.gdc.cancer.gov/ ), and log2-transformed transcripts per million (TPM) were utilised. ‘Combat’ algorithm in R package ‘sva’ was used to remove batch effects. The exclusion criterion was patients without sound survival information. Construction and validation of a prognostic signature From the ImmPort database ( www.immport.org ), we downloaded the gene list of 2,498 immune-related genes. By overlapping them with the Metabric and TCGA dataset, 1,090 commonly expressed immune-related genes were retained. Then, we screened out top 50% highly variant and highly expressed genes with a median absolute deviation of > 0.5. After resampling 1,000 times and performing a univariate Cox regression analysis each time, those genes robustly associated with prognosis were retained. Least absolute shrinkage and selection operator (LASSO)-Cox regression were used to establish the final prognostic prediction model. The cut-off of the risk score was determined by the time-dependent receiver operating characteristic (ROC) curve at 5 years; subsequently, patients were divided into immune high-risk and low-risk groups. Kaplan–Meier survival analysis and log-rank tests were performed to compare disease-free survival (DFS) of the two risk groups. Univariate analysis and multivariate analysis integrating with clinical and pathologic variables were used to test the independent prognostic value of IRGS in patients with Luminal B breast cancer. Functional annotation and analysis Enrichment analysis and pathway annotations were used for signature genes and differentially expressed genes (DEGs) in the two risk groups by R package ‘gProfleR’. Gene Set Enrichment Analysis (GSEA) was conducted to further explore the underlying pathways through the Bioconductor package ‘HTSanalyzeR’. We investigated the distribution of immune and stromal composition by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. In addition, we compared the distribution of immune cells in low- and high-risk groups in the Metabric and TCGA datasets by EPIC algorithm[ 16 ]. Statistical analysis For comparison, the independent t-test was used for continuous variables, and the Chi-squared test or Fisher’s exact test was performed for categorical variables. LASSO regression was performed via the ‘glmnet’ R package (version 2.0–16). We performed univariate and multivariate analyses to test the association between IRGS and DFS. Variables with P < 0.05 in the univariate analysis were included in the multivariate analysis through the Cox proportional hazards regression model. Statistical significance was set at P < 0.05. Statistical analysis was performed in R software (version 3.5.1; http://www.Rproject.org ). Results Construction and definition of the IRGS In order to establish an IRGS to predict the prognosis of patients with Luminal B breast cancer, we collected data on the gene expression profiles of those patients from the Metabric and TCGA datasets for preprocessing. Next, we filtered out 488 patients from the Metabric and 250 patients from TCGA datasets. There was no significant difference in the characteristics of these two cohorts (Table 1 ). We obtained 2,498 immune-related genes from the ImmPort dataset. Then we screened out 396 highly expressed and highly variant immune-related genes as the candidate genes for constructing the prognostic model. Table 1 Characteristic of cohorts Characteristic Metabric LumB TCGA BRCA LumB Number of patients 488 250 Patients with survival data 488 242 Age(mean ± sd) 65.24 ± 11.48 59.47 ± 13.78 Degree of differentiation grade.1 18 NA grade.2 193 NA grade.3 259 NA NA 18 250 TNM stage Stage I 109 23 Stage II 215 146 Stage III 31 72 Stage IV 6 5 NA 127 4 tumor location Left breast 246 137 Right breast 212 113 NA 30 0 Hormone therapy Yes 178 165 No 310 85 NA 0 250 chemotherapy Yes 178 156 No 310 94 Radiotherapy Yes 178 NA No 310 NA NA 0 250 Cox regression analysis was performed to select the genes related to recurrence and metastasis in Luminal B breast cancer from the Metabric dataset. After resampling 1,000 times, 14 genes with a statistically significant times > 900 times ( P < 0.05) were retained. Through LASSO-cox regression, 12 immune-related genes were then chosen to construct the IRGS, and the corresponding parameters were obtained. Therefore, a preliminary prognostic model comprising 12 genes for patients with Luminal B breast cancer was established (Fig. 1 a and 1 b). The risk score (RS) in patients with Luminal B breast cancer was calculated using the following formula: Risk score = (0.090 × THBS1) + (0.213 × S100A1) – (0.047 × LANCL1) + (0.254 × PDGFRB) – (0.289 × ACO1) – (0.287 × SEMA3G) + (0.343 × ACVR1B) – (0.157 × IGF1R) + (0.093 × NR2F1) – (0.156 × PGRMC) – (0.555 × PPARA) – (0.070 × TNFRSF18). We defined a cut-off score of -1.169 by the time-dependent ROC at 5 years (Fig. 1 c, Supplementary Table S1 ). IRGS powerfully predicted DFS of patients with Luminal B breast cancer We classified the patients into high-risk (RS ≥-1.169) and low-risk groups (RS <-1.169) by constructing the IRGS. The distribution of the IRGS risk score for individual patients in the training and validation cohorts is shown in Fig. 2 a and 2 b. Furthermore, Kaplan–Meier analysis showed that the 5-year DFS in the high-risk group was significantly lower as compared to the low-risk group, both in the training cohort (HR = 4.95, 95% CI: 3.22–7.62, P < 0.001, Fig. 2 c) and the validation cohort (HR = 2.47, 95% CI: 1.29–4.75, P < 0.001, Fig. 2 d), using this novel IGRS model. IRGS is an independent risk factor for patients with Luminal B breast cancer To analyse the factors impacting the prognosis of Luminal B patients, we performed a univariate analysis of multiple prognostic-related indicators of breast tumours, such as risk score; age; tumour location; tumour, node, metastasis (TNM) stage; hormone therapy; and chemotherapy. Among them, the RS and TNM stage were independent prognostic factors for Luminal B breast cancer, both in the training and validation cohort ( P < 0.05). Subsequently, multivariate analysis was used to confirm the effect of these two independent prognostic factors (training cohort RS: HR = 4.96, 95% CI: 3.00–8.18, P < 0.001, TNM staging: HR = 1.69, 95% CI: 1.20–2.39, P = 0.003; validation cohort RS: HR = 2.56, 95% CI: 1.28–5.09, P = 0.007, TNM staging: HR = 2.25, 95% CI: 1.43–3.53, P < 0.001) (Table 2 ). Table 2 Univariate and multivariate analysis Charactristic Metabric LumB TCGA BRCA LumB Univariate Multivariate Univariate Multivariate HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value HR (95%CI) P-value Risk score 4.95 (3.22–7.62) < 0.001 4.96 (3.00–8.18) < 0.001 2.47 (1.29–4.75) 0.005 2.56 (1.28–5.09) 0.007 Age 1.00 (0.98–1.02) 0.92 1.02 (1.00–1.05) 0.068 Tumor location 0.90 (0.58–1.39) 0.62 0.73 (0.38–1.40) 0.35 TNM stage 2.04 (1.49–2.81) < 0.001 1.69 (1.20–2.39) 0.003 2.26 (1.40–3.64) < 0.001 2.25 (1.43–3.53) < 0.001 Hormone therapy 1.27 (0.81–1.99) 0.29 1.41 (0.74–2.68) 0.3 Chemotherapy 1.27 (0.81–1.99) 0.29 0.41 (0.19–0.90) 0.021 0.26 (0.11–0.63) 0.003 Function annotation of IRGS Furthermore, we carried out gene ontology analysis of the 12 immune-related genes in the predictive model to examine their functional pathways. The results revealed that the 12 immune-related genes were mainly enriched in pathways related to chemotherapy response (Fig. 3 a). The expression level of each gene showed a distinctly opposite trend between the high-risk and the low-risk groups in both the training and the validation cohorts. (Fig. 3 b). The result of gene ontology analysis of differentially expressed genes (DEGs) showed an enrichment trend in stromal related microenvironment pathways in the Metabric cohort (Fig. 3 c), which was consistent with the results of the ESIMATE (Fig. 4 a). Gene Set Enrichment Analysis (GSEA) showed that epithelial-mesenchymal transit, angiogenesis, interferon-alpha response, and interferon-gamma response were the leading pathways (Fig. 3 d). Through the ESIMATE, immune infiltration of the high-risk group was significantly higher than the low-risk group with a significant difference in the immune score ( P = 0.0068), the stromal score ( P < 0.001), and the ESIMATE score ( P < 0.001) in the Metabric cohort (Fig. 4 a). Interestingly, we found that the immune high-risk group had a higher percentage of cancer-associated fibroblast (CAF) ( P < 0.001) in both the Metabric and TCGA cohorts (Fig. 4 b, Supplementary Fig. S1, Supplementary Fig. S2 ). Discussion In this study, we constructed an immune-related gene signature to predict clinical outcomes of patients with Luminal B breast cancer, with an aim to provide a more accurate and individualized treatment plan with fewer-to-no side effects, in turn ensuring a much better quality of life of a patient post-treatment. Further, our study found 12 immune-related genes specific to Luminal B breast cancer from two datasets, and interestingly, all of those genes were mainly seen to exist in chemotherapy-related pathways, with opposing gene expression seen in high-risk and low-risk groups. Precision medicine has received widespread attention since its discovery, and treatment based on the molecular biology of breast cancer has become a consensus in clinical practice. Previous studies have shown that breast cancer is a heterogeneous malignant tumour [ 17 , 18 ]. The tumour of patients with the same clinicopathological characteristics can still exhibit different biological behaviours, and their responses to treatment and their prognosis are not the same [ 19 ]. This is especially seen in patients with Luminal B breast cancer, where the sensitivity of personalised treatment shows strong heterogeneity [ 20 , 21 ]. Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy. However, there is still no effective and feasible biomarker that predicts the prognosis and the therapeutic effects of drugs on patients with Luminal B breast cancer. Therefore, it is necessary to find a valuable biomarker that reveals the molecular mechanisms affecting the prognosis of patients with Luminal B breast cancer. Furthermore, more effective treatment options to guide individualised and precise treatment can then be provided in the future. At present, there are several precision treatment options at the genetic level. In patients with hormone receptor-positive breast cancer, Oncotype DX (21-gene) and Mamaprint (70-gene) can be used to detect patients with a low risk of tumour recurrence, which may be avoided with additional chemotherapy [ 22 – 24 ]. Patients with germline BRCA gene mutations in triple-negative breast cancer can be specifically treated with PARP (Poly ADP-ribose Polymerase) inhibitors which have shown excellent clinical effect. There are also a small group of patients specifically treated with androgen receptor (AR) [ 25 , 26 ]. Therefore, we have constructed this 12-gene prognostic prediction model through deep-learning analysis of the database, and this project has been identified to have a good prognostic prediction performance for patients with Luminal B breast cancer. For patients in different risk groups, the recommended treatment plans may differ. The result of gene ontology analysis revealed that the 12 genes are mainly enriched in pathways related to chemotherapy response, which may indicate its clinical potential in predicting the therapeutic response to chemotherapy. The genes constituting the IRGS ( Supplementary Table S2 ) have been reported to correlate with tumour progression and immunotherapy. IGF1R tightly links with cancer incidence, which might be related to BRCA1 [ 27 ]. A combined inhibiting effect of IGF1R and EGFR could promote immune activation in pancreatic cancer treatment [ 28 ]. In breast cancer, lncRNA NR2F1 has been reported to promote angiogenesis by activating the IGF1R pathway [ 29 ]. THBS1, an angiogenesis inhibitor as well as a potential therapeutic target, plays a vital role in tumour progression and acts as a promotor in certain pathways but as an inhibitor in others [ 30 ]. THBS1 accelerates liver metastasis via epithelial-mesenchymal transition in patients with colorectal cancer [ 31 ], while activating the THBS1 signal axis in breast cancer facilitates focal adhesion and cancer aggressiveness [ 32 ]. Furthermore, activating the interferon pathway with suppression of the tumour microenvironment and resistance to the immune checkpoint is an effective treatment option for breast cancer [ 33 , 34 ]. Those echoed with GSEA analysis. The tumour stroma is closely related to the malignancy of epithelial cells, and studies have reported that the genetic inactivation of PTEN in mouse mammary stromal fibroblasts changed the tumour microenvironment through the large-scale remodelling of the extracellular matrix (ECM), innate immune cell infiltration, and increased angiogenesis, thereby affecting the development and deterioration of breast cancer [ 35 ]. Moreover, the amount of stromal collagen affects the infiltration of immune-competent cells in gastric cancer, thereby changing the immune characteristics [ 36 ]. Another report focused on the stromal and immune infiltration genes related to the prognosis of breast cancer, highlighting the underlying genes behind the regulation of the tumour microenvironment and cell infiltration [ 37 ]. The above findings are consistent with the results of DEGs and ESIMATE in our model. In the four consensus molecular subtypes (CMSs) of colorectal cancer, CMS1, with stronger immune activity, showed better prognosis while CMS4, with higher stromal invasion, usually had a poorer prognosis [ 38 ] ; comparing this to the results of the DEGs and ESIMATE in our model, the high-risk group also exhibited the characteristics of higher stromal infiltration. Through single-cell analysis, the coordinated relationship between stromal heterogeneity and cancer immune response has been previously validated [ 39 ]. In solid cancers, mesenchymal stromal cells have become vital mediators of immune function and immunotherapy response and even serve as novel predictors of drug response and new drug targets. Distinct subclasses of cancer-associated fibroblasts (CAF) play a critical role in immune regulation [ 39 ]. A growing number of studies have reported that CAF, which are highly heterogeneous components in the tumour microenvironment, can regulate immune activity and anti-tumour immune response [ 40 , 41 ]. Evidence shows that CAF, which plays a key role in breast cancer progression, is influenced by PDGFRB [ 42 ], which blocks invasive growth and migration in triple-negative breast cancer [ 43 ]. The immune-checkpoint blockade treatment with PDGFRB aptamer in triple-negative breast cancer [ 44 ] gives enlightenment in treating Luminal B breast cancer. This indicates that IGRS may have potential immunotherapy significance; however, more studies are needed for further exploration and validation. Interestingly, in our research, we found that the 12 immune-related genes mainly existed in chemotherapy-related pathways, and the gene expression is inconsistent in high-risk and low-risk patients. This might suggest that patients in different risk groups have different sensitivity to chemotherapy and immunotherapy. At present, immunotherapy has been selectively used for patients with triple-negative breast cancer in neoadjuvant and palliative treatment [ 45 , 46 ]. Based on these results, we suppose that similar therapeutic plans containing immunotherapy seem feasible for high-risk patients with Luminal B breast cancer. A prospective phase II clinical trial of neoadjuvant chemotherapy plus immunotherapy in Luminal B breast cancer provided evidence for our results[ 47 ]. On the other hand, the better prognosis of patients with low risk showed a possibility of reducing treatment in patients with low immune risk in the future. Because the database divided patients by PAM50, there were 9.2% HER2 + Luminal B breast cancer patients in our study. Due to the application of targeted therapy, luminal B patients with HER2 + is considered to have better prognosis than HER2- in recent years. In the database whether those patients received targeted therapy was unclear, so further analysis could not be performed which is a shortcoming of this study. Conclusions we have developed a novel IRGS for predicting clinical outcomes in patients with Luminal B breast cancer. Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. More studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for Luminal B breast cancer patients, thereby assisting clinicians to make individualised treatment plans. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the study are available in the European Genome-Phenome Archive (https://ega-archive.org/studies/EGAS00000000083) and the GDC Data Portal (https://portal.gdc.cancer.gov/). Competing interests The authors declare that they have no competing interests. Funding This study was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596), the Fundamental Research Funds for the Central Universities of Sun Yat-sen University (No. 19ykpy57), Science and Technology Planning Project of Guangzhou City (No. 202102020641). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Du Cai, Feng Gao, Xiaying Kuang, Runyi Ye and Zhen Shan. The first draft of the manuscript was written by Zhen Xie, Xiaying Kuang, Ying Lin and Nan Shao. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors acknowledge all the participants and useful discussions with other members of The First Affiliated Hospital and The Sixth Affiliated Hospital of Sun Yat-sen University. References 1 Fahad, U. M. (2019) Breast Cancer: Current Perspectives on the Disease Status. ADV EXP MED BIOL. 1152 , 51-64 2 Parker, J. S., Mullins, M., Cheang, M. C., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., Quackenbush, J. F., Stijleman, I. J., Palazzo, J., Marron, J. S., Nobel, A. B., Mardis, E., Nielsen, T. O., Ellis, M. J., Perou, C. M. and Bernard, P. S. (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J CLIN ONCOL. 27 , 1160-1167 3 Jiang, Y. Z., Ma, D., Suo, C., Shi, J., Xue, M., Hu, X., Xiao, Y., Yu, K. 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CLIN TRANSL ONCOL. 22 , 1730-1740 32 Shen, J., Cao, B., Wang, Y., Ma, C., Zeng, Z., Liu, L., Li, X., Tao, D., Gong, J. and Xie, D. (2018) Hippo component YAP promotes focal adhesion and tumour aggressiveness via transcriptionally activating THBS1/FAK signalling in breast cancer. J Exp Clin Cancer Res. 37 , 175 33 Yamashita, N., Long, M., Fushimi, A., Yamamoto, M., Hata, T., Hagiwara, M., Bhattacharya, A., Hu, Q., Wong, K. K., Liu, S. and Kufe, D. (2021) MUC1-C integrates activation of the IFN-gamma pathway with suppression of the tumor immune microenvironment in triple-negative breast cancer. J IMMUNOTHER CANCER. 9 34 De Angelis, C., Fu, X., Cataldo, M. L., Nardone, A., Pereira, R., Veeraraghavan, J., Nanda, S., Qin, L., Sethunath, V., Wang, T., Hilsenbeck, S. G., Benelli, M., Migliaccio, I., Guarducci, C., Malorni, L., Litchfield, L. M., Liu, J., Donaldson, J., Selenica, P., Brown, D. N., Weigelt, B., Reis-Filho, J. S., Park, B. H., Hurvitz, S. A., Slamon, D. J., Rimawi, M. 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(2021) Single-cell advances in stromal-leukocyte interactions in cancer. IMMUNOL REV. 302 , 286-298 40 Mhaidly, R. and Mechta-Grigoriou, F. (2021) Role of cancer-associated fibroblast subpopulations in immune infiltration, as a new means of treatment in cancer. IMMUNOL REV. 302 , 259-272 41 Chen, P. Y., Wei, W. F., Wu, H. Z., Fan, L. S. and Wang, W. (2021) Cancer-Associated Fibroblast Heterogeneity: A Factor That Cannot Be Ignored in Immune Microenvironment Remodeling. FRONT IMMUNOL. 12 , 671595 42 Primac, I., Maquoi, E., Blacher, S., Heljasvaara, R., Van Deun, J., Smeland, H. Y., Canale, A., Louis, T., Stuhr, L., Sounni, N. E., Cataldo, D., Pihlajaniemi, T., Pequeux, C., De Wever, O., Gullberg, D. and Noel, A. (2019) Stromal integrin alpha11 regulates PDGFR-beta signaling and promotes breast cancer progression. J CLIN INVEST. 129 , 4609-4628 43 Camorani, S., Hill, B. S., Collina, F., Gargiulo, S., Napolitano, M., Cantile, M., Di Bonito, M., Botti, G., Fedele, M., Zannetti, A. and Cerchia, L. (2018) Targeted imaging and inhibition of triple-negative breast cancer metastases by a PDGFRbeta aptamer. THERANOSTICS. 8 , 5178-5199 44 Camorani, S., Passariello, M., Agnello, L., Esposito, S., Collina, F., Cantile, M., Di Bonito, M., Ulasov, I. V., Fedele, M., Zannetti, A., De Lorenzo, C. and Cerchia, L. (2020) Aptamer targeted therapy potentiates immune checkpoint blockade in triple-negative breast cancer. J Exp Clin Cancer Res. 39 , 180 45 Keenan, T. E. and Tolaney, S. M. (2020) Role of Immunotherapy in Triple-Negative Breast Cancer. J Natl Compr Canc Netw. 18 , 479-489 46 Katz, H. and Alsharedi, M. (2017) Immunotherapy in triple-negative breast cancer. MED ONCOL. 35 , 13 47 Dieci, M. V., Guarneri, V., Tosi, A., Bisagni, G., Musolino, A., Spazzapan, S., Moretti, G., Vernaci, G. M., Griguolo, G., Giarratano, T., Urso, L., Schiavi, F., Pinato, C., Magni, G., Lo, M. M., De Salvo, G. L., Rosato, A. and Conte, P. (2022) Neoadjuvant Chemotherapy and Immunotherapy in Luminal B-like Breast Cancer: Results of the Phase II GIADA Trial. CLIN CANCER RES. 28 , 308-317 Supplementary Files SupplementaryFig.S1.tiff Through ESIMATE, Stromal score, immune score and ESTIMATE score were calculated in the Metabric cohort (a). Immune cells are estimated based on data from Metabric (b). SupplementaryFig.S2.tiff Through ESIMATE, Stromal score, immune score and ESTIMATE score were calculated in the TCGA cohort (a). Immune cells are estimated based on data from TCGA (b). SupplementaryTableS1.xlsx SupplementaryTableS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1740378","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":124487805,"identity":"0f787cc5-b665-4206-aa88-a044f47209bc","order_by":0,"name":"Zhen Xie","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xie","suffix":""},{"id":124487806,"identity":"5f66b6b4-0f39-4420-82c5-553d4477cfee","order_by":1,"name":"Du Cai","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Du","middleName":"","lastName":"Cai","suffix":""},{"id":124487807,"identity":"9bbf25b9-c9d8-4353-b277-accb2d1b83e7","order_by":2,"name":"Runyi Ye","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Runyi","middleName":"","lastName":"Ye","suffix":""},{"id":124487808,"identity":"77199e77-f76c-493d-b43b-8e62b43f4eec","order_by":3,"name":"Zhen Shan","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Shan","suffix":""},{"id":124487809,"identity":"f0733069-42dc-4ec4-8cb0-b89f43eab3fb","order_by":4,"name":"Ying Lin","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Lin","suffix":""},{"id":124487810,"identity":"b6870c5e-802e-4865-a9a5-6f1390673e80","order_by":5,"name":"Feng Gao","email":"","orcid":"","institution":"Sun Yat-sen University Sixth Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Gao","suffix":""},{"id":124487811,"identity":"7c09c0d0-04d2-4312-9b6a-277824aaeb07","order_by":6,"name":"Nan Shao","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Shao","suffix":""},{"id":124487812,"identity":"f1d50200-6468-41e2-86ab-97e03e5e563f","order_by":7,"name":"Xiaying Kuang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBACCShdzw8kDjyACfMQoSVBsgGoJYEkLQYHQCQxWiRnJD97+LXtcJ7xtcMPgbbUJc6fkcD44G0bg7w5Di3SEmnmxjJnDheb3U4zAGo5nLjhRgKz4dw2BsOdDdi1yEkkmElLVBxm3HY7AaTlQOIGiQQ2ad42qFOxakn/Ji1hcJhx8+z0DzCHsf/Gp0VaIsdM8kMF0D3SOSBbmBMbbiSwMePTItnzpkya4Uy6scTtnIIDCQaHjTecedgsOeechOEGHFokjqdvk/zZZi3HPzt984cPFXWy89uTD354U2Yjj8sWEGDmYWiGMg0YHBsYGBsYEBGGHTD+YKiDc+zxKh0Fo2AUjIIRCQAtm2BPP9zB+wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3552-3808","institution":"Sun Yat-sen University First Affiliated Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaying","middleName":"","lastName":"Kuang","suffix":""}],"badges":[],"createdAt":"2022-06-09 05:35:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1740378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1740378/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24624685,"identity":"dbaed6c1-6a1b-4160-b6d1-d702ec61b03f","added_by":"auto","created_at":"2022-08-01 19:46:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":622427,"visible":true,"origin":"","legend":"\u003cp\u003eThe establish\u003cstrong\u003e \u003c/strong\u003eof immune-related gene signature (IRGS).\u003cstrong\u003e \u003c/strong\u003eSchematic flow chart of the study procedure (a). Twelve immune-related genes selected in LASSO COX regression(b). Time-dependent ROC analysis(c).\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/b8b510ac512c9cd8283a1e3e.jpg"},{"id":24625513,"identity":"95647796-850b-4a1e-af6d-53db0cb2b641","added_by":"auto","created_at":"2022-08-01 19:51:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":842610,"visible":true,"origin":"","legend":"\u003cp\u003eThe outcomes of low and high immune risk in Luminal B breast cancer patients. Distribution of the IRGS risk score for individual patients in the Metabric and the TCGA cohorts (a and b). Kaplan–Meier curves comparing patients with low or high immune risk in Metabric and TCGA (c and d). P-values comparing patients with low or high immune risk were calculated using the log-rank test and HR is short for hazard ratio.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/b16a5f54d0e63c08a9b142c8.jpg"},{"id":24624688,"identity":"27b89a31-60bf-4720-aafd-70bf74f7c4f3","added_by":"auto","created_at":"2022-08-01 19:46:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1546507,"visible":true,"origin":"","legend":"\u003cp\u003eGO analysis of 12 genes in the prognostic prediction model(a); Expression distribution of 12 genes in the high-risk and low-risk groups in the training cohort and validation cohort(b); GO analysis of DEGs in the Metabric cohort(c); GSEA analysis showed epithelial mesenchymaltransit, angiogenesis, IFN-αresponse, IFN-γ response were leading pathways (d).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/3ea2e40d9980318bc7f5c15b.jpg"},{"id":24625515,"identity":"f9b29562-8e3f-467e-b988-4213835a6cb0","added_by":"auto","created_at":"2022-08-01 19:51:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":782858,"visible":true,"origin":"","legend":"\u003cp\u003eThrough ESIMATE, Stromal score, immune score and ESTIMATE score were calculated in the Metabric cohort (a). CAF had a higher percentage in high immune risk group (b).\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/3e99633b125716a22b698a5a.jpg"},{"id":28267426,"identity":"5e414c3c-be1c-4fcb-b1cd-b313ba445f30","added_by":"auto","created_at":"2022-10-26 09:25:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/871e53b0-6d78-4b7b-b7c5-fc5aeb769bb9.pdf"},{"id":24624691,"identity":"b30a0c8f-e65e-470b-a48b-ad79f9d215be","added_by":"auto","created_at":"2022-08-01 19:46:03","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25271403,"visible":true,"origin":"","legend":"\u003cp\u003eThrough ESIMATE, Stromal score, immune score and ESTIMATE score were calculated in the Metabric cohort (a). Immune cells are estimated based on data from Metabric (b).\u003c/p\u003e","description":"","filename":"SupplementaryFig.S1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/6c28c9ff8378cfee48790365.tiff"},{"id":24624692,"identity":"1ca19cc5-0f5a-4a73-99b1-e5e9ea612aa0","added_by":"auto","created_at":"2022-08-01 19:46:03","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25271407,"visible":true,"origin":"","legend":"\u003cp\u003eThrough ESIMATE, Stromal score, immune score and ESTIMATE score were calculated in the TCGA cohort (a). Immune cells are estimated based on data from TCGA (b).\u003c/p\u003e","description":"","filename":"SupplementaryFig.S2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/c4a7dfb5120fcbee75d6234e.tiff"},{"id":24625514,"identity":"2eddba82-1841-44cf-bae9-55cb2575ebfa","added_by":"auto","created_at":"2022-08-01 19:51:03","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":36486,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/48d2fe2799b905f4b35d1876.xlsx"},{"id":24624690,"identity":"8154c97a-9c32-4201-b39e-4d6968b6ba61","added_by":"auto","created_at":"2022-08-01 19:46:03","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17421,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1740378/v1/18aa8268b59c775a7b2da549.docx"}],"financialInterests":"","formattedTitle":"Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer, one of the most common causes of cancer in women, has drawn great attention around the globe. In recent years, breast cancer incidence and mortality rates are still on the rise, which seriously threatens women\u0026rsquo;s health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At the molecular level, breast cancer can be generally divided into four subtypes: Luminal A, Luminal B, HER2-enriched, and basal-like [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among them, Luminal A and Luminal B are both hormone receptor-positive. Luminal A breast cancer is usually sensitive to endocrine therapy and has a relatively good prognosis. Luminal B subtype includes all types of hormone receptor-positive breast cancer, except for Luminal A, accounting for about one-fourth of the breast cancer cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The definition of Luminal B breast cancer is relatively complicated, and its prognosis and treatment sensitivity are inconsistent and varied. Compared with Luminal A breast cancer, the survival rate of patients with Luminal B breast cancer is much lower in clinical practice, and they usually have a worse prognosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy as adjuvant treatment, and their prognosis cannot be accurately assessed and remains a matter of controversy. Only a few studies focus on the precise reclassification of Luminal B breast cancer and various appropriate treatment strategies. Therefore, it has become indispensable to find valuable biomarkers to reclassify Luminal B breast cancer more accurately.\u003c/p\u003e \u003cp\u003eImmunotherapy is a newly emerging treatment modality in cancer therapy that has promising potential. However, most patients with breast cancer treated by immunotherapy show negative results, except for several trials in selected patients with triple-negative breast cancer. Some studies attempt to examine the effectiveness of immunotherapy by subtyping triple-negative breast cancer to classify their characteristics and prognosis [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], whereas no such positive evidence has been observed in patients with Luminal B breast cancer.\u003c/p\u003e \u003cp\u003eIn the previous studies, several reclassification methods for breast cancer have been proposed, and some of them have shown significant clinical benefits [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, a more precise and effective treatment could be found by using machine learning algorithm to construct a prognostic prediction model based on immune-related genes to reclassify patients with Luminal B breast cancer. Therefore, we constructed an immune-related gene signature (IRGS) to predict clinical outcomes of patients with Luminal B breast cancer, aiming to bring a more accurate and individualized treatment plan with fewer-to-no side effects, which may also provide a foothold for the clinical practice of precision medicine.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe collected the gene expression profile of patients with Luminal B breast cancer from two datasets to identify an IRGS. We included 738 patients with Luminal B breast cancer in this study: 488 from the Metabric dataset (training cohort) and 250 from the cancer genome atlas (TCGA) dataset (validation cohort). The level 3 RNA expression profile data of TCGA cohort were downloaded from the GDC Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and log2-transformed transcripts per million (TPM) were utilised. \u0026lsquo;Combat\u0026rsquo; algorithm in R package \u0026lsquo;sva\u0026rsquo; was used to remove batch effects. The exclusion criterion was patients without sound survival information.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of a prognostic signature\u003c/h2\u003e \u003cp\u003eFrom the ImmPort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://portal.gdc.cancer.gov/\" target=\"_blank\"\u003ewww.immport.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.immport.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we downloaded the gene list of 2,498 immune-related genes. By overlapping them with the Metabric and TCGA dataset, 1,090 commonly expressed immune-related genes were retained. Then, we screened out top 50% highly variant and highly expressed genes with a median absolute deviation of \u0026gt;\u0026thinsp;0.5. After resampling 1,000 times and performing a univariate Cox regression analysis each time, those genes robustly associated with prognosis were retained. Least absolute shrinkage and selection operator (LASSO)-Cox regression were used to establish the final prognostic prediction model.\u003c/p\u003e \u003cp\u003eThe cut-off of the risk score was determined by the time-dependent receiver operating characteristic (ROC) curve at 5 years; subsequently, patients were divided into immune high-risk and low-risk groups. Kaplan\u0026ndash;Meier survival analysis and log-rank tests were performed to compare disease-free survival (DFS) of the two risk groups. Univariate analysis and multivariate analysis integrating with clinical and pathologic variables were used to test the independent prognostic value of IRGS in patients with Luminal B breast cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and analysis\u003c/h2\u003e \u003cp\u003eEnrichment analysis and pathway annotations were used for signature genes and differentially expressed genes (DEGs) in the two risk groups by R package \u0026lsquo;gProfleR\u0026rsquo;. Gene Set Enrichment Analysis (GSEA) was conducted to further explore the underlying pathways through the Bioconductor package \u0026lsquo;HTSanalyzeR\u0026rsquo;. We investigated the distribution of immune and stromal composition by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. In addition, we compared the distribution of immune cells in low- and high-risk groups in the Metabric and TCGA datasets by EPIC algorithm[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor comparison, the independent t-test was used for continuous variables, and the Chi-squared test or Fisher\u0026rsquo;s exact test was performed for categorical variables. LASSO regression was performed via the \u0026lsquo;glmnet\u0026rsquo; R package (version 2.0\u0026ndash;16). We performed univariate and multivariate analyses to test the association between IRGS and DFS. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariate analysis through the Cox proportional hazards regression model. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analysis was performed in R software (version 3.5.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and definition of the IRGS\u003c/h2\u003e \u003cp\u003eIn order to establish an IRGS to predict the prognosis of patients with Luminal B breast cancer, we collected data on the gene expression profiles of those patients from the Metabric and TCGA datasets for preprocessing. Next, we filtered out 488 patients from the Metabric and 250 patients from TCGA datasets. There was no significant difference in the characteristics of these two cohorts (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We obtained 2,498 immune-related genes from the ImmPort dataset. Then we screened out 396 highly expressed and highly variant immune-related genes as the candidate genes for constructing the prognostic model.\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\u003eCharacteristic of cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabric LumB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCGA BRCA LumB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients with survival data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.24\u0026thinsp;\u0026plusmn;\u0026thinsp;11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.47\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDegree of differentiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egrade.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNM stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003etumor location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight breast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHormone therapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003echemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCox regression analysis was performed to select the genes related to recurrence and metastasis in Luminal B breast cancer from the Metabric dataset. After resampling 1,000 times, 14 genes with a statistically significant times\u0026thinsp;\u0026gt;\u0026thinsp;900 times (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained. Through LASSO-cox regression, 12 immune-related genes were then chosen to construct the IRGS, and the corresponding parameters were obtained. Therefore, a preliminary prognostic model comprising 12 genes for patients with Luminal B breast cancer was established (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The risk score (RS) in patients with Luminal B breast cancer was calculated using the following formula:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRisk score = (0.090 \u0026times; THBS1) + (0.213 \u0026times; S100A1) \u0026ndash; (0.047 \u0026times; LANCL1) + (0.254 \u0026times; PDGFRB) \u0026ndash; (0.289 \u0026times; ACO1) \u0026ndash; (0.287 \u0026times; SEMA3G) + (0.343 \u0026times; ACVR1B) \u0026ndash; (0.157 \u0026times; IGF1R) + (0.093 \u0026times; NR2F1) \u0026ndash; (0.156 \u0026times; PGRMC) \u0026ndash; (0.555 \u0026times; PPARA) \u0026ndash; (0.070 \u0026times; TNFRSF18).\u003c/p\u003e \u003cp\u003eWe defined a cut-off score of -1.169 by the time-dependent ROC at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, \u003cb\u003eSupplementary Table S1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIRGS powerfully predicted DFS of patients with Luminal B breast cancer\u003c/h2\u003e \u003cp\u003eWe classified the patients into high-risk (RS \u0026ge;-1.169) and low-risk groups (RS \u0026lt;-1.169) by constructing the IRGS. The distribution of the IRGS risk score for individual patients in the training and validation cohorts is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Furthermore, Kaplan\u0026ndash;Meier analysis showed that the 5-year DFS in the high-risk group was significantly lower as compared to the low-risk group, both in the training cohort (HR\u0026thinsp;=\u0026thinsp;4.95, 95% CI: 3.22\u0026ndash;7.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) and the validation cohort (HR\u0026thinsp;=\u0026thinsp;2.47, 95% CI: 1.29\u0026ndash;4.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), using this novel IGRS model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIRGS is an independent risk factor for patients with Luminal B breast cancer\u003c/h2\u003e \u003cp\u003eTo analyse the factors impacting the prognosis of Luminal B patients, we performed a univariate analysis of multiple prognostic-related indicators of breast tumours, such as risk score; age; tumour location; tumour, node, metastasis (TNM) stage; hormone therapy; and chemotherapy. Among them, the RS and TNM stage were independent prognostic factors for Luminal B breast cancer, both in the training and validation cohort (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, multivariate analysis was used to confirm the effect of these two independent prognostic factors (training cohort RS: HR\u0026thinsp;=\u0026thinsp;4.96, 95% CI: 3.00\u0026ndash;8.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, TNM staging: HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI: 1.20\u0026ndash;2.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; validation cohort RS: HR\u0026thinsp;=\u0026thinsp;2.56, 95% CI: 1.28\u0026ndash;5.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, TNM staging: HR\u0026thinsp;=\u0026thinsp;2.25, 95% CI: 1.43\u0026ndash;3.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCharactristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMetabric LumB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTCGA BRCA LumB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMultivariate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMultivariate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.95 (3.22\u0026ndash;7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.96 (3.00\u0026ndash;8.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.47 (1.29\u0026ndash;4.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.56 (1.28\u0026ndash;5.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.98\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (1.00\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.58\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73 (0.38\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04 (1.49\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.20\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.26 (1.40\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.25 (1.43\u0026ndash;3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.81\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41 (0.74\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.81\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.19\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.26 (0.11\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunction annotation of IRGS\u003c/h2\u003e \u003cp\u003eFurthermore, we carried out gene ontology analysis of the 12 immune-related genes in the predictive model to examine their functional pathways. The results revealed that the 12 immune-related genes were mainly enriched in pathways related to chemotherapy response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The expression level of each gene showed a distinctly opposite trend between the high-risk and the low-risk groups in both the training and the validation cohorts. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The result of gene ontology analysis of differentially expressed genes (DEGs) showed an enrichment trend in stromal related microenvironment pathways in the Metabric cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), which was consistent with the results of the ESIMATE (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Gene Set Enrichment Analysis (GSEA) showed that epithelial-mesenchymal transit, angiogenesis, interferon-alpha response, and interferon-gamma response were the leading pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough the ESIMATE, immune infiltration of the high-risk group was significantly higher than the low-risk group with a significant difference in the immune score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0068), the stromal score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the ESIMATE score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the Metabric cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Interestingly, we found that the immune high-risk group had a higher percentage of cancer-associated fibroblast (CAF) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in both the Metabric and TCGA cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cb\u003eSupplementary Fig. S1, Supplementary Fig. S2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we constructed an immune-related gene signature to predict clinical outcomes of patients with Luminal B breast cancer, with an aim to provide a more accurate and individualized treatment plan with fewer-to-no side effects, in turn ensuring a much better quality of life of a patient post-treatment. Further, our study found 12 immune-related genes specific to Luminal B breast cancer from two datasets, and interestingly, all of those genes were mainly seen to exist in chemotherapy-related pathways, with opposing gene expression seen in high-risk and low-risk groups.\u003c/p\u003e \u003cp\u003ePrecision medicine has received widespread attention since its discovery, and treatment based on the molecular biology of breast cancer has become a consensus in clinical practice. Previous studies have shown that breast cancer is a heterogeneous malignant tumour [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The tumour of patients with the same clinicopathological characteristics can still exhibit different biological behaviours, and their responses to treatment and their prognosis are not the same [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This is especially seen in patients with Luminal B breast cancer, where the sensitivity of personalised treatment shows strong heterogeneity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients with Luminal B breast cancer are routinely treated with chemotherapy and endocrine therapy. However, there is still no effective and feasible biomarker that predicts the prognosis and the therapeutic effects of drugs on patients with Luminal B breast cancer. Therefore, it is necessary to find a valuable biomarker that reveals the molecular mechanisms affecting the prognosis of patients with Luminal B breast cancer. Furthermore, more effective treatment options to guide individualised and precise treatment can then be provided in the future.\u003c/p\u003e \u003cp\u003eAt present, there are several precision treatment options at the genetic level. In patients with hormone receptor-positive breast cancer, Oncotype DX (21-gene) and Mamaprint (70-gene) can be used to detect patients with a low risk of tumour recurrence, which may be avoided with additional chemotherapy [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Patients with germline BRCA gene mutations in triple-negative breast cancer can be specifically treated with PARP (Poly ADP-ribose Polymerase) inhibitors which have shown excellent clinical effect. There are also a small group of patients specifically treated with androgen receptor (AR) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, we have constructed this 12-gene prognostic prediction model through deep-learning analysis of the database, and this project has been identified to have a good prognostic prediction performance for patients with Luminal B breast cancer. For patients in different risk groups, the recommended treatment plans may differ. The result of gene ontology analysis revealed that the 12 genes are mainly enriched in pathways related to chemotherapy response, which may indicate its clinical potential in predicting the therapeutic response to chemotherapy.\u003c/p\u003e \u003cp\u003eThe genes constituting the IRGS (\u003cb\u003eSupplementary Table S2\u003c/b\u003e) have been reported to correlate with tumour progression and immunotherapy. IGF1R tightly links with cancer incidence, which might be related to BRCA1 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A combined inhibiting effect of IGF1R and EGFR could promote immune activation in pancreatic cancer treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In breast cancer, lncRNA NR2F1 has been reported to promote angiogenesis by activating the IGF1R pathway [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. THBS1, an angiogenesis inhibitor as well as a potential therapeutic target, plays a vital role in tumour progression and acts as a promotor in certain pathways but as an inhibitor in others [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. THBS1 accelerates liver metastasis via epithelial-mesenchymal transition in patients with colorectal cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], while activating the THBS1 signal axis in breast cancer facilitates focal adhesion and cancer aggressiveness [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, activating the interferon pathway with suppression of the tumour microenvironment and resistance to the immune checkpoint is an effective treatment option for breast cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Those echoed with GSEA analysis. The tumour stroma is closely related to the malignancy of epithelial cells, and studies have reported that the genetic inactivation of PTEN in mouse mammary stromal fibroblasts changed the tumour microenvironment through the large-scale remodelling of the extracellular matrix (ECM), innate immune cell infiltration, and increased angiogenesis, thereby affecting the development and deterioration of breast cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, the amount of stromal collagen affects the infiltration of immune-competent cells in gastric cancer, thereby changing the immune characteristics [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Another report focused on the stromal and immune infiltration genes related to the prognosis of breast cancer, highlighting the underlying genes behind the regulation of the tumour microenvironment and cell infiltration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The above findings are consistent with the results of DEGs and ESIMATE in our model. In the four consensus molecular subtypes (CMSs) of colorectal cancer, CMS1, with stronger immune activity, showed better prognosis while CMS4, with higher stromal invasion, usually had a poorer prognosis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] ; comparing this to the results of the DEGs and ESIMATE in our model, the high-risk group also exhibited the characteristics of higher stromal infiltration. Through single-cell analysis, the coordinated relationship between stromal heterogeneity and cancer immune response has been previously validated [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In solid cancers, mesenchymal stromal cells have become vital mediators of immune function and immunotherapy response and even serve as novel predictors of drug response and new drug targets. Distinct subclasses of cancer-associated fibroblasts (CAF) play a critical role in immune regulation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A growing number of studies have reported that CAF, which are highly heterogeneous components in the tumour microenvironment, can regulate immune activity and anti-tumour immune response [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Evidence shows that CAF, which plays a key role in breast cancer progression, is influenced by PDGFRB [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which blocks invasive growth and migration in triple-negative breast cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The immune-checkpoint blockade treatment with PDGFRB aptamer in triple-negative breast cancer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] gives enlightenment in treating Luminal B breast cancer. This indicates that IGRS may have potential immunotherapy significance; however, more studies are needed for further exploration and validation.\u003c/p\u003e \u003cp\u003eInterestingly, in our research, we found that the 12 immune-related genes mainly existed in chemotherapy-related pathways, and the gene expression is inconsistent in high-risk and low-risk patients. This might suggest that patients in different risk groups have different sensitivity to chemotherapy and immunotherapy. At present, immunotherapy has been selectively used for patients with triple-negative breast cancer in neoadjuvant and palliative treatment [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Based on these results, we suppose that similar therapeutic plans containing immunotherapy seem feasible for high-risk patients with Luminal B breast cancer. A prospective phase II clinical trial of neoadjuvant chemotherapy plus immunotherapy in Luminal B breast cancer provided evidence for our results[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. On the other hand, the better prognosis of patients with low risk showed a possibility of reducing treatment in patients with low immune risk in the future. Because the database divided patients by PAM50, there were 9.2% HER2\u0026thinsp;+\u0026thinsp;Luminal B breast cancer patients in our study. Due to the application of targeted therapy, luminal B patients with HER2\u0026thinsp;+\u0026thinsp;is considered to have better prognosis than HER2- in recent years. In the database whether those patients received targeted therapy was unclear, so further analysis could not be performed which is a shortcoming of this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ewe have developed a novel IRGS for predicting clinical outcomes in patients with Luminal B breast cancer. Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. More studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for Luminal B breast cancer patients, thereby assisting clinicians to make individualised treatment plans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval \u003c/strong\u003e\u003cstrong\u003eand consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the study are available in the European Genome-Phenome Archive (https://ega-archive.org/studies/EGAS00000000083) and the GDC Data Portal (https://portal.gdc.cancer.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\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\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596), the Fundamental Research Funds for the Central Universities of Sun Yat-sen University (No. 19ykpy57), Science and Technology Planning Project of Guangzhou City (No. 202102020641). The funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Du Cai, Feng Gao, Xiaying Kuang, Runyi Ye and Zhen Shan. The first draft of the manuscript was written by Zhen Xie, Xiaying Kuang, Ying Lin and Nan Shao. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge all the participants and useful discussions with other members of The First Affiliated Hospital and The Sixth Affiliated Hospital of Sun Yat-sen University.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1 Fahad, U. 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CLIN CANCER RES. \u003cstrong\u003e28\u003c/strong\u003e, 308-317\u003c/p\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":"immune-related gene signature, Luminal B breast cancer, clinical outcome","lastPublishedDoi":"10.21203/rs.3.rs-1740378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1740378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLuminal B breast cancer is routinely treated with chemotherapy and endocrine therapy. However, its sensitivity to treatment remains heterogeneous; therefore, identifying patients who may most benefit, remains crucial. Immune-related genes are reportedly related to the prognosis of breast cancer. The purpose of this study was to evaluate the impact of an immune-related gene signature (IRGS) in predicting the prognosis of patients with Luminal B breast cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe selected patients with Luminal B breast cancer from two large datasets: 488 from the Metabric dataset (training cohort) and 250 patients from the cancer genome atlas (TCGA) dataset (validation cohort). Prognostic analysis was performed to test the predictive value of IRGS, and enrichment analysis and ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) were used for deeper function analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA prognostic IRGS model containing 12 immune-related genes was developed. After which, we separated patients with Luminal B breast cancer into low-risk and high-risk groups in terms of disease-free survival (DFS) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis identified IRGS as an independent prognostic factor. Furthermore, functional analysis showed that the 12 genes were mainly enriched in pathways related to chemotherapy response, whose expression levels showed completely opposing trends in low-risk and high-risk groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe novel IRGS is a satisfactory and reliable biomarker to predict the clinical outcome of patients with Luminal B breast cancer which potentially facilitating individualised management. Further studies are needed to assess the clinical potential in predicting prognosis and the treatment options for Luminal B breast cancer patients.\u003c/p\u003e","manuscriptTitle":"Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-01 19:46:01","doi":"10.21203/rs.3.rs-1740378/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":"78488ef2-b514-48ff-abed-f8d88cdbe93b","owner":[],"postedDate":"August 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-26T09:25:23+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-01 19:46:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1740378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1740378","identity":"rs-1740378","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}