A novel immune-related lncRNA as a predictor of survival in HER2+ breast cancer screened via bioinformatics analyses

A novel immune-related lncRNA as a predictor of survival in HER2+ breast cancer screened via bioinformatics analyses | 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 A novel immune-related lncRNA as a predictor of survival in HER2+ breast cancer screened via bioinformatics analyses Xinwei Li, Yue Meng, Bing Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3188760/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 HER2+ breast cancer is highly malignant with a poor prognosis. Long non-coding RNAs have been shown to play an significant role in the progression and prognosis of breast cancer, especially in tumor associated immune processes. Therefore, this study aimed to obtain differentially expressed immune-related lncRNAs by bioinformatics analysis to provide novel diagnostic and prognostic targets for HER2+ breast cancer. Methods We downloaded breast cancer patient samples and corresponding clinical data from the TCGA database and downloaded Immune gene data from the Immport database. We performed a separate screen for differentially expressed lncRNAs and differentially expressed immune related genes. The immune-related differential lncRNAs were screened by Pearson correlation analysis.ROC curves were constructed for the immune-related differential lncRNAs, and diagnostic lncRNAs were obtained based on AUC values. The prognostic biomarker was subjected to correlation analysis with clinicopathological features, analysis of subcellular localization, and analysis of immune infiltration. We also constructed a lncRNA-mRNA co-expression network and a ceRNA network for the biomarker and performed a functional enrichment analysis for the associated mRNA. Results There were 22 total differential lncRNAs and 23 total differential immune genes, of which 19 differential lncRNAs were associated with immune genes. 13 of the 19 immune-related lncRNAs were found to have good diagnostic value, and one lncRNA (CTC-537E7.2) was found to be significantly associated with overall survival time as a prognostic biomarker. Subsequent analysis demonstrated significant differences in biomarkers across clinicopathological features. Biomarkers were expressed in both the cytoplasm and nucleus. In addition, four immune cells were associated with it. Lastly, signaling pathways related to macrophage polarization such as the Jak-STAT signaling pathway were enriched. Conclusion CTC-537E7.2 was selected as a candidate prognostic biomarker for HER2+ breast cancer based on bioinformatic analysis. HER2+ breast cancer Long non-coding RNA Immune-related predictors TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Background Breast cancer is one of the most frequent malignancies in women. Its incidence continues to increase, and it has the second highest number of deaths among female malignancies[1] . Breast cancer is a highly heterogeneous disease, categorized into four primary clinically relevant molecular subtypes based on the expression of estrogen receptor (ER), progesterone receptor(PR), and human epidermal growth factor receptor 2 (HER2):luminal A, luminal B, HER2 +, and triple negative breast cancer[2].Overexpression of human epidermal growth factor receptor 2 (HER2) occurs in approximately 15-20% of breast cancer cases[3].HER2+ breast cancer is a subtype defined as HER2 positive and ER and PR negative[2]. Compared with other subtypes, HER2+ breast cancer is fast-growing, aggressive, prone to metastasis and recurrence, and has a poor prognosis[4]. Therefore, it is important to identify specific biomarkers for early diagnosis and prognosis assessment of HER2+ breast cancer. Long non-coding RNAs (lncRNAs) are classified as transcripts that are more than 200 nucleotides and do not encode proteins[5]. It has been shown that lncRNAs have a wide range of functional activities including the regulation of gene expression, RNA splicing, regulation of miRNAs, protein folding, and other related biological functions[6]. Furthermore, recent studies have shown that lncRNAs have important functions in different stages of cancer immunity, such as antigen presentation, immune activation, and immune cell infiltration[7, 8]. Therefore, immune-related lncRNAs have received a great deal of attention. The prognostic merit of immune-related lncRNA markers in predicting overall survival (OS) in breast cancer has been reported[9], however, nothing is known about the diagnostic and prognostic role of immune-related lncRNAs in HER2 + breast cancer. In this study, we downloaded breast cancer patient data from the TCGA database and screened for differential lncRNAs associated with HER2+ breast cancer. Combined with immune-related genes from the Immport database, we searched for immune-related lncRNAs associated with prognosis to provide novel diagnostic and prognostic targets for HER2+ breast cancer. Materials and Methods Data source and preprocessing We downloaded the data of TCGA-BRCA from the the UCSC XENA platform in July 2021[10].RNA sequencing data, survival information, and clinical information of breast cancer samples and normal samples were downloaded. Based on the three indicators of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2（HER2）, the cancer samples were classified into four subtypes: luminal A, luminal B, HER2 +, and triple negative breast cancer[2]. According to the human gene annotation file (Release 22) provided by GENCODE, genes with annotation information of "protein_coding" are reserved as mRNA, and genes with annotation information of "antisense", "sense_intronic", "lincRNA", "ncRNA", "sense_overlapping" or "processed_transcript" are reserved as lncRNA.The matrix files for mRNA and lncRNA were collated separately. Immune gene data was then downloaded from The Immunology Database and Analysis Portal and organized into immune gene matrix files[11]. Analysis of differential immune-related genes and lncRNAs In the immune-related gene matrix file, the differentially expressed immune-related genes between HER2+ and normal, luminalA, luminal B, and triple-negative breast cancer samples were analyzed using the "limma" package for R[12]. P.Value0.5 were used as screening criteria to obtain the differential genes. And the common differential immune genes were obtained by Venn analysis. Used the same method to obtain differential lncRNAs(the screening criteria were P.Value0.5) and common differential lncRNAs for subsequent analysis. Identification of immune related lncRNAs Pearson correlation analysis was performed on the shared differential immune genes and shared differential lncRNAs obtained from the above analysis. The correlation pairs were screened by r>|0.3｜ and P.value<0.05. The screening results were visualized using the "ggplot2" package for R. Screening for diagnostic markers ROC curves were constructed for immune-related differential lncRNAs between normal control samples and HER2+ samples using the "pROC" package for R[13]. The area under the ROC curve (AUC) was calculated to assess the diagnostic efficacy of immune-related differential lncRNAs. The lncRNAs with AUC>0.7 were selected as diagnostic markers. Screening biomarkers The optimal threshold value of each diagnostic marker was used to divide the HER2+ samples into two groups of high and low expression. The correlation between diagnostic lncRNAs and overall survival (OS) of HER2+ breast cancer patients was analyzed by plotting K-M survival curves using the R package "survival". The lncRNAs with P < 0.05 were considered biomarkers for this study. Correlation analysis of biomarkers and clinicopathological features Box plots of biomarker expression values in different clinicopathological features were performed using the R package "ggplot2" to investigate the correlation between biomarkers and clinicopathological features. Subcellular localization analysis of biomarkers Subcellular localization of prognostic biomarkers by LncLocator database（http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/index.html#) and iLoc-lncRNA database（http://lin-group.cn/server/iLoc-LncRNA/predictor.php）, respectively Correlation analysis of biomarkers and immune cells Immune cell infiltration analysis of HER2 + breast cancer patients was performed by the CIBERSORT algorithm[14]. Then the Spearman correlation coefficient between biomarkers and 22 infiltrating immune cells was calculated using the R package "ggstatsplot" to analyze the biomarker-associated immune cells in the microenvironment. Immune cells with r >|0.3| and p.value <0.05 were considered as biomarker-associated immune cells. Construction of lncRNA-mRNA and ceRNA network Pearson correlations were calculated for lncRNAs in the nucleus and lncRNAs in the cytoplasm with the above immune-related genes based on the subcellular localization results of the biomarkers. The lncRNA-mRNA correlation pairs with r >0.3 and p.value0.7 were selected. In addition, lncRNA-miRNA prediction was performed using the lncbaseV2 database (http://carolina.imis.athena-innovation.gr), and those with score >0.7 were selected as lncRNA-miRNA relationship pairs.Meanwhile, mRNA-miRNA prediction was performed for mRNAs associated with lncRNAs in the cytoplasm using the miRwalk2.0 database. Finally, the lncRNA-miRNA-mRNA network was formed by combining the co-expression relationship of lncRNA and mRNA in the cytoplasm. Then Cytoscape software was used to construct lncRNA-mRNA co-expression networks for lncRNAs in the nucleus and ceRNA networks for lncRNAs in the cytoplasm. Finally, the mRNAs in the lncRNA-mRNA network and ceRNA network were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the DAVID database, respectively. Results Data source and processing A total of 803 samples were downloaded from the TCGA database. Of these, 430 samples were luminalA samples, 124 luminalB samples, 37 HER2 + samples, 113 triple-negative breast cancer samples, and 99 normal samples. The number of samples with both survival and clinical information was 689.Immune-related genes downloaded from the ImmPort database were de-duplicated to obtain 1811 genes. Analysis of differential immune-related genes and lncRNAs The number of differential immune genes among each sample group was obtained by differential gene expression analysis as shown in Table 1 . In order to understand the distribution of differential genes as a whole, the distribution of differential immune genes among each sample group was shown separately by volcano plot as Fig. 1 . And the 4 groups of differential immune genes were intersected using the online Veen mapping software ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) as in Fig. 2 , and 23 shared differential immune genes were obtained.The number of differential lncRNAs among each sample group was obtained using the same method as above in Table 2 , and the distribution of differential lncRNAs among each sample group was shown by volcano plot in Fig. 3 . 22 common differential lncRNAs were obtained by taking the intersection of the 4 groups of differential lncRNAs in Fig. 4 . Table 1 The number of differential immune genes among each sample group. Group Up Down Total HER2 + VS Normal 185 300 485 HER2 + VS LuminalA 76 117 193 HER2 + VS LuminalB 56 79 135 HER2 + VS TNBC 43 109 152 Table 2 The number of differential lncRNAs among each sample group. Group Up Down Total HER2 + VS Normal 158 504 662 HER2 + VS LuminalA 55 178 233 HER2 + VS LuminalB 36 102 138 HER2 + VS TNBC 68 116 184 Identification of immune related lncRNAs Pearson correlation analysis was performed between 22 differentially expressed lncRNAs and 23 differentially expressed immune-related genes. The 74 correlation pairs were screened using the criteria of r >ཛྷ0.3ཛྷ and P.value < 0.05, which contained 20 differential immune genes and 19 differential lncRNAs.That is, 19 immune-related differential lncRNAs were obtained.The results were visualized using the R package "ggplot2", as shown in Fig. 5 . Screening for diagnostic markers To obtain the diagnostic lncRNAs, ROC curves were constructed for 19 immune-related differential lncRNAs between normal control and HER2 + samples. The results showed that 13 immune-related differential lncRNAs had AUC༞0.7, as shown in Fig. 6 , indicating a promising predictive value for breast cancer survival. Therefore, we included all 13 immune-related differential lncRNAs into diagnostic lncRNAs, namely: AC008268.1, CTA-384D8.35, CTC-537E7.2, HOTAIR, LA16c-380H5.4, LINC00993, RP11-287D1.4, RP11-510J16.5, RP11-612B6.2, RP11-783K16.5, RP11-95M15.1, ST8SIA6-AS1, TMEM92-AS1. Screening biomarkers To obtain prognostic biomarkers for HER2 + breast cancer patients,the optimal threshold value of each diagnostic lncRNA was used to divide the HER2 + samples into two groups of high and low expression, and the K-M survival curve was plotted using the R package "survival", as shown in Fig. 7 .lncRNA with P-value < 0.05 was CTC-537E7.2, which was considered as a prognostic biomarker for HER2 + patients. And the survival of patients in the high-expression group was significantly longer than that of patients in the low-expression group. Correlation analysis of biomarkers and clinicopathological features In order to further investigate the relationship between prognostic biomarkers and clinicopathological factors, we performed a correlation analysis. We used the R package "ggplot2" to perform box plots of biomarker expression values in different clinicopathological factors, as shown in Fig. 8 . The results showed that the expression of biomarkers in six clinicopathological factors, ER, PR, HER2, age, and race, were significantly different among different sample groups. Subcellular localization analysis of biomarkers The prediction of subcellular localization of prognostic biomarkers by the database showed that the highest score in the predicted result of the LncLocator database was Cytoplasm (0.598853), as shown in Fig. 9 , while the highest score in the predicted result of the iLoc-lncRNA database was Nucleus (0.905448). It indicates that prognostic biomarkers are expressed in both the cytoplasm and nucleus. Correlation analysis of biomarkers and immune cells To investigate immune cells that may be associated with biomarkers in HER2 + breast cancer samples, we first used the CIBERSORT algorithm to calculate the proportion of immune cells in HER2 + breast cancer samples. The CIBERSORT algorithm is a method proposed by a team of researchers at Stanford University School of Medicine in 2015 for inferring the proportion of immune infiltrating cells[14]. The results were published in Nature Methods. This study used the CIBERSORT algorithm and LM22 gene markers for analysis. The linear support vector regression method of CIBERSORT was used to deconvolute the expression matrix of the diseased tissue and analyze the content of each cell type in the tissue. Results were obtained for the proportion of 22 immune cell types in each of the 37 HER2 + breast cancer samples.Immune cell infiltration analysis of HER2 + breast cancer patients was performed by the CIBERSORT algorithm.Next, correlation analysis was performed for the expression of biomarkers and each type of immune cells. Spearman's correlation coefficients between biomarkers and 22 infiltrating immune cells were calculated using the R package "ggstatsplot". Immune cells with r>|0.3| and p.value < 0.05 were considered as biomarker-related immune cells. A total of four immune cells associated with biomarkers were obtained:M0 Macrophages, Monocytes, Neutrophils, and M2 Macrophages, and the results are shown in Table 3 .Neutrophils and macrophages play a dual role in promoting and suppressing cancer[15, 16]. Macrophages are thought to be transformed from monocytes and can be broadly classified into two subtypes:the classically activated (M1) macrophage phenotype and the alternatively activated (M2) macrophage phenotype[17, 18]. These two types are polarized cells, and there is another subtype, called M0 cells, which are nonpolarized macrophages. Unactivated M0 macrophages can be activated into polarized macrophages[19]. Among them, M2 macrophages account for the majority of tumor-associated macrophages and have a clear role in promoting tumor progression and metastasis[15].And biomarkers are positively correlated with M2 macrophages. In addition, M2 macrophages can be activated by cytokines (IL-4, IL-13, IL-10), TGF-b, etc[17].Therefore, to further confirm the correlation between biomarkers and macrophages, we analyzed the correlation between biomarker (CTC-537E7.2) and M2 macrophage phenotypic factors (IL-10,TGF-β,IL-4 and IL-13) as shown in Fig. 10 .The correlation plot between biomarkers and M2 macrophage phenotypic factors shows that IL-4 was positively correlated with M2 macrophages positively and IL-10,TGF-β, and IL-13 negatively correlated with M2 macrophages. Table 3 Immune cells associated with biomarkers. immune.cells lncRNA r p.value Macrophages M0 CTC-537E7.2 -0.385825165 0.018350539 Monocytes CTC-537E7.2 0.340359924 0.039277796 Neutrophils CTC-537E7.2 -0.331519923 0.045024058 Macrophages M2 CTC-537E7.2 0.330639322 0.04563177 Construction of lncRNA-mRNA and ceRNA network According to the results of subcellular localization of lncRNAs, lncRNAs in the nucleus can activate or repress the expression of target genes by directly binding to them. In contrast, lncRNAs in the cytoplasm can interact with miRNAs as a competitive endogenous RNA to regulate target genes[20]. We used the matched samples to calculate the Pearson correlation coefficients of prognostic biomarkers with the above immune-related genes. The lncRNA-mRNA relationship pairs with r > 0.3 and p.value < 0.05 were selected. As a result, 51 lncRNA-mRNA relationship pairs were obtained. It contains 1 lncRNA and 51 mRNAs.According to the above lncRNA subcellular localization results, CTC-537E7.2 was expressed in both the cytoplasm and nucleus. Therefore, we constructed the lncRNA-mRNA co-expression network and ceRNA network of CTC-537E7.2, respectively. The lncRNA-miRNA prediction of biomarkers was performed using the lncbaseV2 database( http://carolina.imis.athena-innovation.gr).Thos e with score > 0.7 were selected as lncRNA-miRNA relationship pairs. As a result, 13 relationship pairs were obtained, which contained 1 lncRNA and 13 miRNAs. For the 51 mRNAs associated with prognostic biomarkers, we used the miRWalk2.0 to predict miRNA-mRNA relationship pairs by combining the 6 commonly used databases: miRWalk, miRanda, miRDB, PITA, RNA22, and Targetscan.If the predicted miRNA-mRNA relationship pairs appeared in the prediction results of at least 4 databases mentioned above, the miRNAs were considered to regulate the corresponding target genes.The results showed that a total of 6456 miRNA-mRNA relationship pairs were obtained, including 48 mRNAs and 1284 miRNAs.Finally, the co-expression relationship between prognostic biomarkers and mRNAs was combined to form a ceRNA regulatory network. A total of 24 lncRNA-miRNA-mRNA relationship pairs were obtained, which contained 1 lncRNA, 8 miRNAs and 11 mRNAs. lncRNA-mRNA co-expression network and ceRNA network of prognostic biomarkers were constructed and visualized by Cytoscape[21], as shown in Fig. 11 and Fig. 12 , respectively. The mRNAs in the lncRNA-mRNA network and ceRNA network were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the DAVID database, respectively.GO analysis can be classified into different gene functions, for example, biological process (BP),molecular function (MF), and cellular component (CC).KEGG enriches information about gene pathways in different species and different genes.Pvalue < 0.05 and count ≥ 2 were considered as significant enrichment results.The results showed that 40 GO BP, 8 GO CC, 7 GO MF and 18 KEGG pathways were enriched by mRNAs in the lncRNA-mRNA co-expression network.In biological processes(BP),mRNAs mainly enriched in signal transduction,cell − cell signaling,etc.In molecular function(MF),mRNAs mainly enriched in cytokine activity,growth factor activity,etc.In cell component (CC),mRNAs mainly enriched in extracellular space,extracellular region,etc.KEGG pathway analysis showed that these genes were mainly enriched in Jak-STAT signaling pathway,Cytokine − cytokine receptor interaction,etc.A total of 12 GO BPs, 2 GO CCs, 5 GO MFs and 5 KEGG pathways were enriched by mRNAs in the mRNAs in the ceRNA network.In biological processes(BP),mRNAs mainly enriched in signal transduction,cell − cell signaling,etc.In molecular function(MF),mRNAs mainly enriched in transmembrane receptor protein serine/threonine kinase activity,growth factor binding,etc.In cell component (CC),mRNAs mainly enriched in plasma membrane,cell surface,etc.KEGG pathway analysis showed that these genes were mainly enriched in Pathways in cancer,Cytokine − cytokine receptor interaction,Jak-STAT signaling pathway,etc. The enrichment results of mRNAs in the lncRNA-mRNA co-expression network are shown in Fig. 13 , as there are many enrichment pathways, we select the TOP10 Terms of GO and KEGG here according to the Count ranking.The enrichment results of mRNAs in ceRNA network are shown in Fig. 14 . Discussion Breast cancer is one of the most frequent malignancies in women and is a highly heterogeneous disease[1].HER2 + breast cancers tend to have low survival rates, high rates of malignancy, and susceptibility to metastasis[4]. With the development of breast cancer research, although surgery, radiotherapy, and targeted therapy have greatly improved the outcome of breast cancer treatment, the prognosis for patients remains unsatisfactory[22]. Therefore, exploring potentially effective prognostic biomarkers is crucial for breast cancer. Long non-coding RNA (lncRNA) plays an important role in tumorigenesis and progression, as well as in cancer immunity[7, 8]. In addition, several studies have confirmed the prognostic value of lncRNAs in breast cancer[9]. Based on the above, we identified a potential specific biomarker associated with the prognosis in HER2 + breast cancer: CTC-537E7.2. It is a reliable predictor of prognosis with a high AUC value (> 0.7) and expression significantly correlated with overall survival (OS).To date, this immune-related lncRNA has not been reported in studies of breast cancer or other cancers. By investigating the relationship between biomarkers and clinical characteristics of HER2 + breast cancer patients, it was found that the expression of CTC-537E7.2 differed significantly among different expression of ER, PR, and HER2. It indicates that biomarkers may be involved in regulating ER, PR, and HER2 gene expression to influence the formation of molecular subtypes. However, expression of biomarkers was not significantly correlated with TNM and tumor stage. This implies that the timing of HER2 + breast carcinoma diagnosis is not closely related to biomarkers.Correlation analysis of biomarkers with immune infiltrating cells revealed negative associations with M0 macrophages and neutrophils, and positive associations with monocytes and M2 macrophages.Studies have shown that M2 macrophages promote tumor growth and metastasis[15]. Therefore, we investigated the relationship between biomarkers and cytokines that promote polarization of M2 macrophages. Correlation analysis revealed a correlation between biomarkers and cytokines that promote macrophage polarization to M2. These results suggest that biomarkers may further influence tumor status by affecting these cytokines to regulate infiltration of M2 macrophages.However, this mechanism requires further in-depth study. Finally, enrichment analysis of mRNAs in the lncRNA-mRNA network and ceRNA network was performed. The results showed that the Jak-STAT signaling pathway was enriched. JAK-activated STAT protein family members are key transcription factors regulating the polarization of macrophages[23]. Among them, STAT6 is a key transcription factor for M2 macrophage polarization, and STAT6 enhances the transcription of genes related to macrophage M2 polarization[24]. Our results suggest that the biomarker target genes may promote macrophage polarization to the M2 through the Jak-STAT signaling pathway. The advantage of this study is firstly that the study data are based on population-based database and high-throughput sequencing data. Secondly, CTC-537E7.2 has not been found to be investigated and reported in the field of cancer and its function remains to be elucidated. This gene may serve as a newly discovered prognostic biomarker for HER2 + breast cancer and be studied in depth. Finally, tumor immunology has become the fastest growing field in cancer research, and this study may provide valuable insights into the clinical application of antitumor immunotherapy. However, there were some limitations in this study. Firstly as the data analyzed were obtained from online databases, there are no in vitro or in vivo experimental data to confirm our findings. In addition, we did not explore the potential mechanisms of the lncRNAs investigated. Therefore we should experimentally validate the differential expression of biomarkers in different subtypes of breast cancer as well as between normal tissue. More functional studies of lncRNAs should be conducted to further uncover potential immune-related mechanisms. Conclusions A total of 19 immune-related differential lncRNAs were screened by differential analysis and correlation analysis of HER2 + breast cancer with other subtypes and normal samples. 13 of these lncRNAs showed good diagnostic potential, including AC008268.1, CTA-384D8.35, CTC-537E7.2, HOTAIR, LA16c -380H5.4, LINC00993, RP11-287D1.4, RP11-510J16.5, RP11-612B6.2, RP11-783K16.5, RP11-95M15.1, ST8SIA6-AS1, TMEM92-AS1. Further analysis showed that CTC-537E7.2 had a good predictive value and was correlated with overall survival time, providing a theoretical study for early diagnosis and prognosis assessment of HER2 + breast cancer. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Public data used in this work can be acquired from the UCSC XENA database (https://xenabrowser.net) and The Immunology Database and Analysis Portal (ImmPort, https://immport.niaid.nih.gov).The raw experimental data and analysis codes supporting the conclusions of this article will be made available by the corresponding author. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the National Natural Science Foundation of China (Grant No. 82003149), the Guangzhou Science and Technology Program (Grant No. 202102020265). Authors' contributions Conception and design: BG, YM, XL . Financial support: BG. Administrative support: BG and YM. Collection and assembly of data: YM and XL. Data analysis and interpretation:YM and XL. Manuscript writing: XL; final approval of manuscript: all the authors; accountable for all aspects of the work: all the authors. Acknowledgements We would like to gratefully acknowledge the authors of the UCSC XENA database,The Cancer Genome Atlas and ImmPort for providing their platforms and contributors for uploading their meaningful datasets. Author details 1 Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China. 2 Department of clinical laboratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China References Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA A Cancer J Clinicians 72:7–33 Carey LA, Perou CM, Livasy CA, et al (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295:2492–2502 Cronin KA, Harlan LC, Dodd KW, Abrams JS, Ballard-Barbash R (2010) Population-based estimate of the prevalence of HER-2 positive breast cancer tumors for early stage patients in the US. Cancer Invest 28:963–968 Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL (1987) Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235:177–182 Sun M, Kraus WL (2015) From discovery to function: the expanding roles of long noncoding RNAs in physiology and disease. Endocr Rev 36:25–64 Chen YG, Satpathy AT, Chang HY (2017) Gene regulation in the immune system by long noncoding RNAs. Nat Immunol 18:962–972 Denaro N, Merlano MC, Lo Nigro C (2019) Long noncoding RNAs as regulators of cancer immunity. Mol Oncol 13:61–73 Zhang L, Xu X, Su X (2020) Noncoding RNAs in cancer immunity: functions, regulatory mechanisms, and clinical application. Mol Cancer 19:48 Shen Y, Peng X, Shen C (2020) Identification and validation of immune-related lncRNA prognostic signature for breast cancer. Genomics 112:2640–2646 UCSC Xena. http://xena.ucsc.edu/. Accessed 19 Jul 2021 ImmPort Private Data. https://immport.niaid.nih.gov/home. Accessed 19 Jul 2021 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47 Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36 Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol 1711:243–259 Hu W, Li X, Zhang C, Yang Y, Jiang J, Wu C (2016) Tumor-associated macrophages in cancers. Clin Transl Oncol 18:251–258 Xiong S, Dong L, Cheng L (2021) Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol 14:173 Biswas SK, Mantovani A (2010) Macrophage plasticity and interaction with lymphocyte subsets: cancer as a paradigm. Nat Immunol 11:889–896 Terry RL, Miller SD (2014) Molecular control of monocyte development. Cell Immunol 291:16–21 Miao X, Leng X, Zhang Q (2017) The Current State of Nanoparticle-Induced Macrophage Polarization and Reprogramming Research. Int J Mol Sci 18:336 Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? Cell 146:353–358 Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504 Maughan KL, Lutterbie MA, Ham PS (2010) Treatment of breast cancer. Am Fam Physician 81:1339–1346 Li H, Jiang T, Li M-Q, Zheng X-L, Zhao G-J (2018) Transcriptional Regulation of Macrophages Polarization by MicroRNAs. Front Immunol 9:1175 Yu T, Gan S, Zhu Q, et al (2019) Modulation of M2 macrophage polarization by the crosstalk between Stat6 and Trim24. Nat Commun 10:4353 Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3188760","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":220442039,"identity":"26e30d6a-d09f-4b45-a809-72fd7bffcac0","order_by":0,"name":"Xinwei Li","email":"","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinwei","middleName":"","lastName":"Li","suffix":""},{"id":220442040,"identity":"04229219-2e62-43ba-8111-f3b79528ad5f","order_by":1,"name":"Yue Meng","email":"","orcid":"","institution":"Department of clinical laboratory, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Meng","suffix":""},{"id":220442041,"identity":"dba0315e-6972-48c9-a2a3-02abe40963d3","order_by":2,"name":"Bing Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3RIQvCQBTA8TesN1fP4r7Crcv2NYwnA9MmfgCVJS1qvuFXMbxxoEVmHWzBZBTFImJwE7PnmuD9w4MXfrzwAHS6n4wAAnQAkNQj/ZqkTNYgdjdM5PC+d5f5AuGyHoO1ij4TJxtwGS9zPy5SbojjFmiBCiICJs157rMsYA2CG2CUf0XSGsSmJSE3dN9kpCaMnLg0I5+3ih1LBCKhmerKLJRX8nC9Zj53DmectC2huoLlMKbQiyhUP5Xq79hRNR/gAX3tE6XQ6XS6/+sJfRhNLp83/UwAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2023-07-20 13:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3188760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3188760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40549562,"identity":"563435fb-4fb2-44f7-8e34-035c2f6a6dbb","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208383,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differential expression of immune genes between sample groups.(A)Differential expression of immune genes in HER2+ samples versus normal samples.(B)Differential expression of immune genes in HER2+ samples versus Luminal A samples.(C)Differential expression of immune genes in HER2+ samples versus Luminal B samples.(D)Differential expression of immune genes in HER2+ samples versus TNBC samples.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/6829bce825ace3272d5e4f29.png"},{"id":40550300,"identity":"108512c8-f440-4253-bed6-b898d125fc1b","added_by":"auto","created_at":"2023-07-25 15:27:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":915516,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of immune genes among different samples.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/0f993058337352d14c4496e9.png"},{"id":40549564,"identity":"1fa7af65-30fe-4c4a-8adb-98649c4d2afc","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251052,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differential expression of lncRNA between sample groups.(A)Differential expression of lncRNA in HER2+ samples versus normal samples.(B)Differential expression of lncRNA in HER2+ samples versus Luminal A samples.(C)Differential expression of lncRNA in HER2+ samples versus Luminal B samples.(D)Differential expression of lncRNA in HER2+ samples versus TNBC samples.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/79f09e0d9557af3fdf0d245d.png"},{"id":40549571,"identity":"a65d9c2f-36e3-46c6-923a-27e03d29cd80","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":934224,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of lncRNA among different samples.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/9df4890f984fe196bd127117.png"},{"id":40549563,"identity":"d77dc319-2ced-41df-bc7d-5c5ffbf2bf0c","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2047248,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation diagram between differential lncRNA and differential immune genes.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/e07557819b62f52ece0a4e0e.png"},{"id":40549560,"identity":"2cf806fd-8d57-4c77-850c-e340d518bbb2","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1303015,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for 13 diagnostic lncRNAs.(A)The ROC curve of AC008268.1.(B)The ROC curve of CTA-384D8.35.(C)The ROC curve of CTC-537E7.2.(D)The ROC curve of HOTAIR.(E)The ROC curve of LA16c-380H5.4.\u003c/p\u003e\n\u003cp\u003e(F)The ROC curve of LINC00993.(G)The ROC curve of RP11-287D1.4.(H)The ROC curve of RP11-510J16.5.(I)The ROC curve of RP11-612B6.2.(J)The ROC curve of RP11-783K16.5.(K)The ROC curve of RP11-95M15.1.(L)The ROC curve of ST8SIA6-AS1.(M)The ROC curve of TMEM92-AS1.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/610b3faa9b12a1c60b83fbb8.png"},{"id":40552145,"identity":"479531d4-780a-470f-9e1b-e58d044ef94f","added_by":"auto","created_at":"2023-07-25 15:43:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":504285,"visible":true,"origin":"","legend":"\u003cp\u003eK-M survival curve of prognostic biomarkers.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/2ae73ffb77b73ea7ddd4bd82.png"},{"id":40549565,"identity":"4dcb8e33-5bd0-419b-88fa-22abc3ee78b1","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":987371,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of biomarker expression values in different clinicopathological factors.(A)Box plots of biomarker expression values in different age groups.(B)Box plots of biomarker expression values in different expressions of ER.(C)Box plots of biomarker expression values in different group.(D)Box plots of biomarker expression values in different expressions of HER2.(E)Box plots of biomarker expression values in different expressions of PR.(F)Box plots of biomarker expression values in different race.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/7d5a9ed8696e6693c22c5484.png"},{"id":40549569,"identity":"b4a83468-9e02-4512-93ab-9d3753e36a80","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":167921,"visible":true,"origin":"","legend":"\u003cp\u003eThe predicted result of subcellular localization of prognostic biomarkers from the LncLocator database.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/d16b0aab98bb9f258ca5dcec.png"},{"id":40551185,"identity":"044dfd43-4688-4c61-bd0a-6c87860c6f6b","added_by":"auto","created_at":"2023-07-25 15:35:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":408231,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of correlation between biomarker (CTC-537E7.2) and macrophage phenotypic factors.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/63f98b189796045f8c8b8b3e.png"},{"id":40550303,"identity":"2954282d-c1bb-4d2d-b646-e5afa9404248","added_by":"auto","created_at":"2023-07-25 15:27:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1732897,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression network of prognostic biomarkers and mRNAs. Blue diamonds indicate lncRNA, yellow triangles indicate mRNA.\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/eaf0fefd94a39eec1b4f9975.png"},{"id":40549566,"identity":"29e448a9-4997-4643-ba80-5fc7a2141ad8","added_by":"auto","created_at":"2023-07-25 15:19:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1502739,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network of prognostic biomarkers.Blue diamond indicates lncRNA, red circle indicates miRNA, yellow triangle indicates mRNA.\u003c/p\u003e","description":"","filename":"Fig.12.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/f3f1731f42d72f8ebaceb8c2.png"},{"id":40550301,"identity":"f3587dfd-bcf4-4464-a803-4c2c487282de","added_by":"auto","created_at":"2023-07-25 15:27:20","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":188942,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of mRNAs in lncRNA-mRNA co-expression networks.\u003c/p\u003e","description":"","filename":"Fig.13.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/afa686f7d6a7787b278d65bc.png"},{"id":40551186,"identity":"b40ce19b-94cf-4025-81f2-b4dc8a822e36","added_by":"auto","created_at":"2023-07-25 15:35:20","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1229324,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of mRNAs in ceRNA networks.\u003c/p\u003e","description":"","filename":"Fig.14.png","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/59296b5a4b959fcf7054467a.png"},{"id":43263158,"identity":"5c1c0837-8fda-4528-873c-d0c0584eee15","added_by":"auto","created_at":"2023-09-17 22:37:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3880358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3188760/v1/93cffef8-bc4a-436a-bcbd-5b97b5dc2227.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA novel immune-related lncRNA as a predictor of survival in HER2+ breast cancer\u003c/strong\u003e \u003cstrong\u003escreened via bioinformatics analyses\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is one of the most frequent malignancies in women. Its incidence continues to increase, and it has the second highest number of deaths among female malignancies[1]\u0026nbsp;. Breast cancer is a highly heterogeneous disease, categorized into four primary clinically relevant molecular subtypes based on the expression of estrogen receptor (ER), progesterone receptor(PR), and human epidermal growth factor receptor 2 (HER2):luminal A, luminal B, HER2 +, and triple negative breast cancer[2].Overexpression of human epidermal growth factor receptor 2 (HER2) occurs in approximately 15-20% of breast cancer cases[3].HER2+ breast cancer is a subtype defined as HER2 positive and ER and PR negative[2]. Compared with other subtypes, HER2+ breast cancer is fast-growing, aggressive, prone to metastasis and recurrence, and has a poor prognosis[4]. Therefore, it is important to identify specific biomarkers for early diagnosis and prognosis assessment of HER2+ breast cancer.\u003c/p\u003e\n\u003cp\u003eLong non-coding RNAs (lncRNAs) are classified as transcripts that are more than 200 nucleotides and do not encode proteins[5]. It\u0026nbsp;has\u0026nbsp;been shown\u0026nbsp;that\u0026nbsp;lncRNAs have a wide range of functional activities including the regulation of gene expression, RNA splicing, regulation\u0026nbsp;of\u0026nbsp;miRNAs, protein folding, and other related biological functions[6]. Furthermore, recent studies have shown that lncRNAs have important functions in different stages of cancer immunity, such as antigen presentation, immune activation, and immune cell infiltration[7, 8]. Therefore, immune-related lncRNAs have received\u0026nbsp;a\u0026nbsp;great\u0026nbsp;deal\u0026nbsp;of\u0026nbsp;attention. The prognostic merit of immune-related lncRNA markers in predicting overall survival (OS) in breast cancer has been reported[9], however, nothing is known about the diagnostic and prognostic role of immune-related lncRNAs in HER2 + breast cancer.\u003c/p\u003e\n\u003cp\u003eIn this study, we downloaded breast cancer patient data from the TCGA database and screened for differential lncRNAs associated with HER2+ breast cancer. Combined with immune-related genes from the Immport database, we searched for immune-related lncRNAs associated with prognosis to provide novel diagnostic and prognostic targets for HER2+ breast cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData source and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded the data of \u0026nbsp;TCGA-BRCA from the the UCSC XENA platform in July 2021[10].RNA sequencing data, survival information, and clinical information of breast cancer samples and normal samples were downloaded. Based on the three indicators of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2（HER2）, the cancer samples were classified into four subtypes: luminal A, luminal B, HER2 +, and triple negative breast cancer[2]. According to the human gene annotation file (Release 22) provided by GENCODE, genes with annotation information of \u0026quot;protein_coding\u0026quot; are reserved as mRNA, and genes with annotation information of \u0026quot;antisense\u0026quot;, \u0026quot;sense_intronic\u0026quot;, \u0026quot;lincRNA\u0026quot;, \u0026quot;ncRNA\u0026quot;, \u0026quot;sense_overlapping\u0026quot; or \u0026quot;processed_transcript\u0026quot; are reserved as lncRNA.The matrix files for mRNA and lncRNA were collated separately. Immune gene data was then downloaded from The Immunology Database and Analysis Portal and organized into immune gene matrix files[11].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differential immune-related genes and lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the immune-related gene matrix file, the differentially expressed immune-related genes between HER2+ and normal, luminalA, luminal B, and triple-negative breast cancer samples were analyzed using the \u0026quot;limma\u0026quot; package for R[12]. P.Value\u0026lt;0.05\u0026amp;|log2FC|\u0026gt;0.5 were used as screening criteria to obtain the differential genes. And the common differential immune genes were obtained by Venn analysis. Used the same method to obtain differential lncRNAs(the screening criteria were P.Value\u0026lt;0.05\u0026amp;|log2FC|\u0026gt;0.5) and common differential lncRNAs for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of immune related lncRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was performed on the shared differential immune genes and shared differential lncRNAs obtained from the above analysis. The correlation pairs were screened by r\u0026gt;|0.3｜\u0026nbsp;and P.value\u0026lt;0.05. The screening results were visualized using the \u0026quot;ggplot2\u0026quot; package for R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening for diagnostic markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves were constructed for immune-related differential lncRNAs between normal control samples and HER2+ samples using the \u0026quot;pROC\u0026quot; package for R[13]. The area under the ROC curve (AUC) was calculated to assess the diagnostic efficacy of immune-related differential lncRNAs. The lncRNAs with AUC\u0026gt;0.7 were selected as diagnostic markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimal threshold value of each diagnostic marker was used to divide the HER2+ samples into two groups of high and low expression. The correlation between diagnostic lncRNAs and overall survival (OS) of HER2+ breast cancer patients was analyzed by plotting K-M survival curves using the R package \u0026quot;survival\u0026quot;. The lncRNAs with P \u0026lt; 0.05 were considered biomarkers for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of biomarkers and clinicopathological features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox plots of biomarker expression values in different clinicopathological features were performed using the R package \u0026quot;ggplot2\u0026quot; to investigate the correlation between biomarkers and clinicopathological features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubcellular localization analysis of biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubcellular localization of prognostic biomarkers by LncLocator database（http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/index.html#) and iLoc-lncRNA database（http://lin-group.cn/server/iLoc-LncRNA/predictor.php）, respectively\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of biomarkers and immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration analysis of HER2 + breast cancer patients was performed by the CIBERSORT algorithm[14]. Then the Spearman correlation coefficient between biomarkers and 22 infiltrating immune cells was calculated using the R package \u0026quot;ggstatsplot\u0026quot; to analyze the biomarker-associated immune cells in the microenvironment. Immune cells with r \u0026gt;|0.3| and p.value \u0026lt;0.05 were considered as biomarker-associated immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of lncRNA-mRNA and ceRNA network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlations were calculated for lncRNAs in the nucleus and lncRNAs in the cytoplasm with the above immune-related genes based on the subcellular localization results of the biomarkers. The lncRNA-mRNA correlation pairs with r \u0026gt;0.3 and p.value0.7 were selected. In addition, lncRNA-miRNA prediction was performed using the lncbaseV2 database (http://carolina.imis.athena-innovation.gr), and those with score \u0026gt;0.7 were selected as lncRNA-miRNA relationship pairs.Meanwhile, mRNA-miRNA prediction was performed for mRNAs associated with lncRNAs in the cytoplasm using the miRwalk2.0 database. Finally, the lncRNA-miRNA-mRNA network was formed by combining the co-expression relationship of lncRNA and mRNA in the cytoplasm. Then Cytoscape software was used to construct lncRNA-mRNA co-expression networks for lncRNAs in the nucleus and ceRNA networks for lncRNAs in the cytoplasm. Finally, the mRNAs in the lncRNA-mRNA network and ceRNA network were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the DAVID database, respectively.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData source and processing\u003c/h2\u003e \u003cp\u003eA total of 803 samples were downloaded from the TCGA database. Of these, 430 samples were luminalA samples, 124 luminalB samples, 37 HER2\u0026thinsp;+\u0026thinsp;samples, 113 triple-negative breast cancer samples, and 99 normal samples. The number of samples with both survival and clinical information was 689.Immune-related genes downloaded from the ImmPort database were de-duplicated to obtain 1811 genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of differential immune-related genes and lncRNAs\u003c/h2\u003e \u003cp\u003eThe number of differential immune genes among each sample group was obtained by differential gene expression analysis as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In order to understand the distribution of differential genes as a whole, the distribution of differential immune genes among each sample group was shown separately by volcano plot as Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. And the 4 groups of differential immune genes were intersected using the online Veen mapping software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and 23 shared differential immune genes were obtained.The number of differential lncRNAs among each sample group was obtained using the same method as above in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the distribution of differential lncRNAs among each sample group was shown by volcano plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. 22 common differential lncRNAs were obtained by taking the intersection of the 4 groups of differential lncRNAs in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe number of differential immune genes among each sample group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS LuminalA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS LuminalB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS TNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152\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\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\u003eThe number of differential lncRNAs among each sample group.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS Normal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS LuminalA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS LuminalB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2\u0026thinsp;+\u0026thinsp;VS TNBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e184\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of immune related lncRNAs\u003c/h2\u003e \u003cp\u003ePearson correlation analysis was performed between 22 differentially expressed lncRNAs and 23 differentially expressed immune-related genes. The 74 correlation pairs were screened using the criteria of r \u0026gt;ཛྷ0.3ཛྷ and P.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which contained 20 differential immune genes and 19 differential lncRNAs.That is, 19 immune-related differential lncRNAs were obtained.The results were visualized using the R package \"ggplot2\", as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening for diagnostic markers\u003c/h2\u003e \u003cp\u003eTo obtain the diagnostic lncRNAs, ROC curves were constructed for 19 immune-related differential lncRNAs between normal control and HER2\u0026thinsp;+\u0026thinsp;samples. The results showed that 13 immune-related differential lncRNAs had AUC༞0.7, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, indicating a promising predictive value for breast cancer survival. Therefore, we included all 13 immune-related differential lncRNAs into diagnostic lncRNAs, namely: AC008268.1, CTA-384D8.35, CTC-537E7.2, HOTAIR, LA16c-380H5.4, LINC00993, RP11-287D1.4, RP11-510J16.5, RP11-612B6.2, RP11-783K16.5, RP11-95M15.1, ST8SIA6-AS1, TMEM92-AS1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eScreening biomarkers\u003c/h2\u003e \u003cp\u003eTo obtain prognostic biomarkers for HER2\u0026thinsp;+\u0026thinsp;breast cancer patients,the optimal threshold value of each diagnostic lncRNA was used to divide the HER2\u0026thinsp;+\u0026thinsp;samples into two groups of high and low expression, and the K-M survival curve was plotted using the R package \"survival\", as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.lncRNA with P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was CTC-537E7.2, which was considered as a prognostic biomarker for HER2\u0026thinsp;+\u0026thinsp;patients. And the survival of patients in the high-expression group was significantly longer than that of patients in the low-expression group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of biomarkers and clinicopathological features\u003c/h2\u003e \u003cp\u003eIn order to further investigate the relationship between prognostic biomarkers and clinicopathological factors, we performed a correlation analysis. We used the R package \"ggplot2\" to perform box plots of biomarker expression values in different clinicopathological factors, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The results showed that the expression of biomarkers in six clinicopathological factors, ER, PR, HER2, age, and race, were significantly different among different sample groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSubcellular localization analysis of biomarkers\u003c/h2\u003e \u003cp\u003eThe prediction of subcellular localization of prognostic biomarkers by the database showed that the highest score in the predicted result of the LncLocator database was Cytoplasm (0.598853), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, while the highest score in the predicted result of the iLoc-lncRNA database was Nucleus (0.905448). It indicates that prognostic biomarkers are expressed in both the cytoplasm and nucleus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis of biomarkers and immune cells\u003c/h2\u003e \u003cp\u003eTo investigate immune cells that may be associated with biomarkers in HER2\u0026thinsp;+\u0026thinsp;breast cancer samples, we first used the CIBERSORT algorithm to calculate the proportion of immune cells in HER2\u0026thinsp;+\u0026thinsp;breast cancer samples. The CIBERSORT algorithm is a method proposed by a team of researchers at Stanford University School of Medicine in 2015 for inferring the proportion of immune infiltrating cells[14]. The results were published in Nature Methods. This study used the CIBERSORT algorithm and LM22 gene markers for analysis. The linear support vector regression method of CIBERSORT was used to deconvolute the expression matrix of the diseased tissue and analyze the content of each cell type in the tissue. Results were obtained for the proportion of 22 immune cell types in each of the 37 HER2\u0026thinsp;+\u0026thinsp;breast cancer samples.Immune cell infiltration analysis of HER2\u0026thinsp;+\u0026thinsp;breast cancer patients was performed by the CIBERSORT algorithm.Next, correlation analysis was performed for the expression of biomarkers and each type of immune cells. Spearman's correlation coefficients between biomarkers and 22 infiltrating immune cells were calculated using the R package \"ggstatsplot\". Immune cells with r\u0026gt;|0.3| and p.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as biomarker-related immune cells. A total of four immune cells associated with biomarkers were obtained:M0 Macrophages, Monocytes, Neutrophils, and M2 Macrophages, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.Neutrophils and macrophages play a dual role in promoting and suppressing cancer[15, 16]. Macrophages are thought to be transformed from monocytes and can be broadly classified into two subtypes:the classically activated (M1) macrophage phenotype and the alternatively activated (M2) macrophage phenotype[17, 18]. These two types are polarized cells, and there is another subtype, called M0 cells, which are nonpolarized macrophages. Unactivated M0 macrophages can be activated into polarized macrophages[19]. Among them, M2 macrophages account for the majority of tumor-associated macrophages and have a clear role in promoting tumor progression and metastasis[15].And biomarkers are positively correlated with M2 macrophages. In addition, M2 macrophages can be activated by cytokines (IL-4, IL-13, IL-10), TGF-b, etc[17].Therefore, to further confirm the correlation between biomarkers and macrophages, we analyzed the correlation between biomarker (CTC-537E7.2) and M2 macrophage phenotypic factors (IL-10,TGF-β,IL-4 and IL-13) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.The correlation plot between biomarkers and M2 macrophage phenotypic factors shows that IL-4 was positively correlated with M2 macrophages positively and IL-10,TGF-β, and IL-13 negatively correlated with M2 macrophages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImmune cells associated with biomarkers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimmune.cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elncRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eMacrophages M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC-537E7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.385825165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018350539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC-537E7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.340359924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039277796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC-537E7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.331519923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045024058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrophages M2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC-537E7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.330639322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04563177\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=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of lncRNA-mRNA and ceRNA network\u003c/h2\u003e \u003cp\u003eAccording to the results of subcellular localization of lncRNAs, lncRNAs in the nucleus can activate or repress the expression of target genes by directly binding to them. In contrast, lncRNAs in the cytoplasm can interact with miRNAs as a competitive endogenous RNA to regulate target genes[20]. We used the matched samples to calculate the Pearson correlation coefficients of prognostic biomarkers with the above immune-related genes. The lncRNA-mRNA relationship pairs with r\u0026thinsp;\u0026gt;\u0026thinsp;0.3 and p.value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. As a result, 51 lncRNA-mRNA relationship pairs were obtained. It contains 1 lncRNA and 51 mRNAs.According to the above lncRNA subcellular localization results, CTC-537E7.2 was expressed in both the cytoplasm and nucleus. Therefore, we constructed the lncRNA-mRNA co-expression network and ceRNA network of CTC-537E7.2, respectively.\u003c/p\u003e \u003cp\u003eThe lncRNA-miRNA prediction of biomarkers was performed using the lncbaseV2 database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://carolina.imis.athena-innovation.gr).Thos\u003c/span\u003e\u003cspan address=\"http://carolina.imis.athena-innovation.gr).Thos\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee with score\u0026thinsp;\u0026gt;\u0026thinsp;0.7 were selected as lncRNA-miRNA relationship pairs. As a result, 13 relationship pairs were obtained, which contained 1 lncRNA and 13 miRNAs.\u003c/p\u003e \u003cp\u003eFor the 51 mRNAs associated with prognostic biomarkers, we used the miRWalk2.0 to predict miRNA-mRNA relationship pairs by combining the 6 commonly used databases: miRWalk, miRanda, miRDB, PITA, RNA22, and Targetscan.If the predicted miRNA-mRNA relationship pairs appeared in the prediction results of at least 4 databases mentioned above, the miRNAs were considered to regulate the corresponding target genes.The results showed that a total of 6456 miRNA-mRNA relationship pairs were obtained, including 48 mRNAs and 1284 miRNAs.Finally, the co-expression relationship between prognostic biomarkers and mRNAs was combined to form a ceRNA regulatory network. A total of 24 lncRNA-miRNA-mRNA relationship pairs were obtained, which contained 1 lncRNA, 8 miRNAs and 11 mRNAs. lncRNA-mRNA co-expression network and ceRNA network of prognostic biomarkers were constructed and visualized by Cytoscape[21], as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe mRNAs in the lncRNA-mRNA network and ceRNA network were analyzed by GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) using the DAVID database, respectively.GO analysis can be classified into different gene functions, for example, biological process (BP),molecular function (MF), and cellular component (CC).KEGG enriches information about gene pathways in different species and different genes.Pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and count\u0026thinsp;\u0026ge;\u0026thinsp;2 were considered as significant enrichment results.The results showed that 40 GO BP, 8 GO CC, 7 GO MF and 18 KEGG pathways were enriched by mRNAs in the lncRNA-mRNA co-expression network.In biological processes(BP),mRNAs mainly enriched in signal transduction,cell\u0026thinsp;\u0026minus;\u0026thinsp;cell signaling,etc.In molecular function(MF),mRNAs mainly enriched in cytokine activity,growth factor activity,etc.In cell component (CC),mRNAs mainly enriched in extracellular space,extracellular region,etc.KEGG pathway analysis showed that these genes were mainly enriched in Jak-STAT signaling pathway,Cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction,etc.A total of 12 GO BPs, 2 GO CCs, 5 GO MFs and 5 KEGG pathways were enriched by mRNAs in the mRNAs in the ceRNA network.In biological processes(BP),mRNAs mainly enriched in signal transduction,cell\u0026thinsp;\u0026minus;\u0026thinsp;cell signaling,etc.In molecular function(MF),mRNAs mainly enriched in transmembrane receptor protein serine/threonine kinase activity,growth factor binding,etc.In cell component (CC),mRNAs mainly enriched in plasma membrane,cell surface,etc.KEGG pathway analysis showed that these genes were mainly enriched in Pathways in cancer,Cytokine\u0026thinsp;\u0026minus;\u0026thinsp;cytokine receptor interaction,Jak-STAT signaling pathway,etc.\u003c/p\u003e \u003cp\u003eThe enrichment results of mRNAs in the lncRNA-mRNA co-expression network are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, as there are many enrichment pathways, we select the TOP10 Terms of GO and KEGG here according to the Count ranking.The enrichment results of mRNAs in ceRNA network are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreast cancer is one of the most frequent malignancies in women and is a highly heterogeneous disease[1].HER2\u0026thinsp;+\u0026thinsp;breast cancers tend to have low survival rates, high rates of malignancy, and susceptibility to metastasis[4]. With the development of breast cancer research, although surgery, radiotherapy, and targeted therapy have greatly improved the outcome of breast cancer treatment, the prognosis for patients remains unsatisfactory[22]. Therefore, exploring potentially effective prognostic biomarkers is crucial for breast cancer. Long non-coding RNA (lncRNA) plays an important role in tumorigenesis and progression, as well as in cancer immunity[7, 8]. In addition, several studies have confirmed the prognostic value of lncRNAs in breast cancer[9].\u003c/p\u003e \u003cp\u003eBased on the above, we identified a potential specific biomarker associated with the prognosis in HER2\u0026thinsp;+\u0026thinsp;breast cancer: CTC-537E7.2. It is a reliable predictor of prognosis with a high AUC value (\u0026gt;\u0026thinsp;0.7) and expression significantly correlated with overall survival (OS).To date, this immune-related lncRNA has not been reported in studies of breast cancer or other cancers. By investigating the relationship between biomarkers and clinical characteristics of HER2\u0026thinsp;+\u0026thinsp;breast cancer patients, it was found that the expression of CTC-537E7.2 differed significantly among different expression of ER, PR, and HER2. It indicates that biomarkers may be involved in regulating ER, PR, and HER2 gene expression to influence the formation of molecular subtypes. However, expression of biomarkers was not significantly correlated with TNM and tumor stage. This implies that the timing of HER2\u0026thinsp;+\u0026thinsp;breast carcinoma diagnosis is not closely related to biomarkers.Correlation analysis of biomarkers with immune infiltrating cells revealed negative associations with M0 macrophages and neutrophils, and positive associations with monocytes and M2 macrophages.Studies have shown that M2 macrophages promote tumor growth and metastasis[15]. Therefore, we investigated the relationship between biomarkers and cytokines that promote polarization of M2 macrophages. Correlation analysis revealed a correlation between biomarkers and cytokines that promote macrophage polarization to M2. These results suggest that biomarkers may further influence tumor status by affecting these cytokines to regulate infiltration of M2 macrophages.However, this mechanism requires further in-depth study. Finally, enrichment analysis of mRNAs in the lncRNA-mRNA network and ceRNA network was performed. The results showed that the Jak-STAT signaling pathway was enriched. JAK-activated STAT protein family members are key transcription factors regulating the polarization of macrophages[23]. Among them, STAT6 is a key transcription factor for M2 macrophage polarization, and STAT6 enhances the transcription of genes related to macrophage M2 polarization[24]. Our results suggest that the biomarker target genes may promote macrophage polarization to the M2 through the Jak-STAT signaling pathway.\u003c/p\u003e \u003cp\u003eThe advantage of this study is firstly that the study data are based on population-based database and high-throughput sequencing data. Secondly, CTC-537E7.2 has not been found to be investigated and reported in the field of cancer and its function remains to be elucidated. This gene may serve as a newly discovered prognostic biomarker for HER2\u0026thinsp;+\u0026thinsp;breast cancer and be studied in depth. Finally, tumor immunology has become the fastest growing field in cancer research, and this study may provide valuable insights into the clinical application of antitumor immunotherapy. However, there were some limitations in this study. Firstly as the data analyzed were obtained from online databases, there are no in vitro or in vivo experimental data to confirm our findings. In addition, we did not explore the potential mechanisms of the lncRNAs investigated. Therefore we should experimentally validate the differential expression of biomarkers in different subtypes of breast cancer as well as between normal tissue. More functional studies of lncRNAs should be conducted to further uncover potential immune-related mechanisms.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA total of 19 immune-related differential lncRNAs were screened by differential analysis and correlation analysis of HER2\u0026thinsp;+\u0026thinsp;breast cancer with other subtypes and normal samples. 13 of these lncRNAs showed good diagnostic potential, including AC008268.1, CTA-384D8.35, CTC-537E7.2, HOTAIR, LA16c -380H5.4, LINC00993, RP11-287D1.4, RP11-510J16.5, RP11-612B6.2, RP11-783K16.5, RP11-95M15.1, ST8SIA6-AS1, TMEM92-AS1. Further analysis showed that CTC-537E7.2 had a good predictive value and was correlated with overall survival time, providing a theoretical study for early diagnosis and prognosis assessment of HER2\u0026thinsp;+\u0026thinsp;breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic data used in this work can be acquired from the UCSC XENA database (https://xenabrowser.net) and The Immunology Database and Analysis Portal (ImmPort, https://immport.niaid.nih.gov).The raw experimental data and analysis codes supporting the conclusions of this article will be made available by the corresponding author.\u0026nbsp;\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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China (Grant No. 82003149), the Guangzhou Science and Technology Program (Grant No. 202102020265).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: BG, YM, XL . Financial support: BG. Administrative support: BG and YM. Collection and assembly of data: YM and XL. Data analysis and interpretation:YM and XL. Manuscript writing: XL; final approval of manuscript: all the authors; accountable for all aspects of the work: all the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to gratefully acknowledge the authors of the UCSC XENA database,The Cancer Genome Atlas and ImmPort for providing their platforms and contributors for uploading their meaningful datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eGuangdong Cardiovascular Institute, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of clinical laboratory, Guangdong Provincial People\u0026rsquo;s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA A Cancer J Clinicians 72:7\u0026ndash;33\u003c/li\u003e\n\u003cli\u003eCarey LA, Perou CM, Livasy CA, et al (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295:2492\u0026ndash;2502\u003c/li\u003e\n\u003cli\u003eCronin KA, Harlan LC, Dodd KW, Abrams JS, Ballard-Barbash R (2010) Population-based estimate of the prevalence of HER-2 positive breast cancer tumors for early stage patients in the US. Cancer Invest 28:963\u0026ndash;968\u003c/li\u003e\n\u003cli\u003eSlamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire WL (1987) Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 235:177\u0026ndash;182\u003c/li\u003e\n\u003cli\u003eSun M, Kraus WL (2015) From discovery to function: the expanding roles of long noncoding RNAs in physiology and disease. Endocr Rev 36:25\u0026ndash;64\u003c/li\u003e\n\u003cli\u003eChen YG, Satpathy AT, Chang HY (2017) Gene regulation in the immune system by long noncoding RNAs. Nat Immunol 18:962\u0026ndash;972\u003c/li\u003e\n\u003cli\u003eDenaro N, Merlano MC, Lo Nigro C (2019) Long noncoding RNAs as regulators of cancer immunity. Mol Oncol 13:61\u0026ndash;73\u003c/li\u003e\n\u003cli\u003eZhang L, Xu X, Su X (2020) Noncoding RNAs in cancer immunity: functions, regulatory mechanisms, and clinical application. Mol Cancer 19:48\u003c/li\u003e\n\u003cli\u003eShen Y, Peng X, Shen C (2020) Identification and validation of immune-related lncRNA prognostic signature for breast cancer. Genomics 112:2640\u0026ndash;2646\u003c/li\u003e\n\u003cli\u003eUCSC Xena. http://xena.ucsc.edu/. Accessed 19 Jul 2021\u003c/li\u003e\n\u003cli\u003eImmPort Private Data. https://immport.niaid.nih.gov/home. Accessed 19 Jul 2021\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47\u003c/li\u003e\n\u003cli\u003eHanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29\u0026ndash;36\u003c/li\u003e\n\u003cli\u003eChen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol 1711:243\u0026ndash;259\u003c/li\u003e\n\u003cli\u003eHu W, Li X, Zhang C, Yang Y, Jiang J, Wu C (2016) Tumor-associated macrophages in cancers. Clin Transl Oncol 18:251\u0026ndash;258\u003c/li\u003e\n\u003cli\u003eXiong S, Dong L, Cheng L (2021) Neutrophils in cancer carcinogenesis and metastasis. J Hematol Oncol 14:173\u003c/li\u003e\n\u003cli\u003eBiswas SK, Mantovani A (2010) Macrophage plasticity and interaction with lymphocyte subsets: cancer as a paradigm. Nat Immunol 11:889\u0026ndash;896\u003c/li\u003e\n\u003cli\u003eTerry RL, Miller SD (2014) Molecular control of monocyte development. Cell Immunol 291:16\u0026ndash;21\u003c/li\u003e\n\u003cli\u003eMiao X, Leng X, Zhang Q (2017) The Current State of Nanoparticle-Induced Macrophage Polarization and Reprogramming Research. Int J Mol Sci 18:336\u003c/li\u003e\n\u003cli\u003eSalmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP (2011) A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? Cell 146:353\u0026ndash;358\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498\u0026ndash;2504\u003c/li\u003e\n\u003cli\u003eMaughan KL, Lutterbie MA, Ham PS (2010) Treatment of breast cancer. Am Fam Physician 81:1339\u0026ndash;1346\u003c/li\u003e\n\u003cli\u003eLi H, Jiang T, Li M-Q, Zheng X-L, Zhao G-J (2018) Transcriptional Regulation of Macrophages Polarization by MicroRNAs. Front Immunol 9:1175\u003c/li\u003e\n\u003cli\u003eYu T, Gan S, Zhu Q, et al (2019) Modulation of M2 macrophage polarization by the crosstalk between Stat6 and Trim24. Nat Commun 10:4353\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HER2+ breast cancer, Long non-coding RNA, Immune-related predictors, TCGA ","lastPublishedDoi":"10.21203/rs.3.rs-3188760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3188760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHER2+ breast cancer is highly malignant with a poor prognosis. Long non-coding RNAs have been shown to play an significant role in the progression and prognosis of breast cancer, especially in tumor associated immune processes. Therefore, this study aimed to obtain differentially expressed immune-related lncRNAs by bioinformatics analysis to provide novel diagnostic and prognostic targets for HER2+ breast cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe downloaded breast cancer patient samples and corresponding clinical data from the TCGA database and downloaded Immune gene data from the Immport database. We performed a separate screen for differentially expressed lncRNAs and differentially expressed immune related genes. The immune-related differential lncRNAs were screened by Pearson correlation analysis.ROC curves were constructed for the immune-related differential lncRNAs, and diagnostic lncRNAs were obtained based on AUC values. The prognostic biomarker was subjected to correlation analysis with clinicopathological features, analysis of subcellular localization, and analysis of immune infiltration. We also constructed a lncRNA-mRNA co-expression network and a ceRNA network for the biomarker and performed a functional enrichment analysis for the associated mRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 22 total differential lncRNAs and 23 total differential immune genes, of which 19 differential lncRNAs were associated with immune genes. 13 of the 19 immune-related lncRNAs were found to have good diagnostic value, and one lncRNA (CTC-537E7.2) was found to be significantly associated with overall survival time as a prognostic biomarker. Subsequent analysis demonstrated significant differences in biomarkers across clinicopathological features. Biomarkers were expressed in both the cytoplasm and nucleus. In addition, four immune cells were associated with it. Lastly, signaling pathways related to macrophage polarization such as the Jak-STAT signaling pathway were enriched.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCTC-537E7.2 was selected as a candidate prognostic biomarker for HER2+ breast cancer based on bioinformatic analysis.\u003c/p\u003e","manuscriptTitle":"A novel immune-related lncRNA as a predictor of survival in HER2+ breast cancer screened via bioinformatics analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-25 15:19:15","doi":"10.21203/rs.3.rs-3188760/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":"6d1c81ef-b8d0-447d-9288-a8259629328d","owner":[],"postedDate":"July 25th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-09-17T22:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-25 15:19:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3188760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3188760","identity":"rs-3188760","version":["v1"]},"buildId":"oE6Zbj460LM0Up2FdVbMZ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}