MIR4435-2HG: A Novel Biomarker for Triple-Negative Breast Cancer Diagnosis and Prognosis, Driving Tumor Progression through EMT by JNK/c-Jun and p38 MAPK Signaling Pathway Activation

MIR4435-2HG: A Novel Biomarker for Triple-Negative Breast Cancer Diagnosis and Prognosis, Driving Tumor Progression through EMT by JNK/c-Jun and p38 MAPK Signaling Pathway Activation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MIR4435-2HG: A Novel Biomarker for Triple-Negative Breast Cancer Diagnosis and Prognosis, Driving Tumor Progression through EMT by JNK/c-Jun and p38 MAPK Signaling Pathway Activation Peng Gu, Wentao Ding, Wenting Zhu, Ling Shen, Bin Yan, Lei Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3832143/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer has the highest incidence rate and causes the most fatalities among all female cancers worldwide. Triple-negative breast cancer (TNBC) is known for its strong invasiveness and higher rates of recurrence. In this research, we aimed to identify MIR4435-2HG as a promising long non-coding RNA (lncRNA) biomarker and therapeutic target for TNBC. Methods Utilizing clinicopathological information and transcriptome data from The Cancer Genome Atlas (TCGA) database, we assessed the clinical relevance of MIR4435-2HG in breast cancer through univariate and multivariate COX regression, receiver operating characteristic (ROC) analysis, as well as Kaplan-Meier survival analysis. To investigate the biological role of MIR4435-2HG in TNBC, we conducted gene set enrichment analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, we constructed and validated a nomogram to predict disease-free survival (DFS). Both the R package “pRRophetic” and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were employed to forecast the sensitivity to different therapeutics between the high- and low-MIR4435-2HG groups. We employed single-cell RNA sequencing analysis and tumor microenvironment infiltration analysis to investigate the potential involvement of MIR4435-2HG in the TNBC tumor microenvironment. Cellular biological behaviors were assessed utilizing CCK-8, transwell assays, and wound-healing assays. Furthermore, we performed RNA-seq, qRT-PCR, and western blotting analyses to elucidate and confirm the specific mechanisms underlying MIR4435-2HG-mediated TNBC progression. Results In our study, we have identified MIR4435-2HG as a significant diagnostic and prognostic factor for TNBC. We observed that MIR4435-2HG is widely expressed and might have a significant impact on the reshaping of the TNBC tumor microenvironment. Patients with breast cancer in the high-MIR4435-2HG group may show reduced sensitivity to cisplatin, doxorubicin, and gemcitabine and have an increased propensity for immune escape. Notably, MIR4435-2HG predominantly enhances the migratory and invasive capabilities of TNBC cells through the epithelial-mesenchymal transition (EMT) process. Mechanistically, we validated that MIR4435-2HG activates the JNK/c-Jun and p38 MAPK signaling pathway in TNBC. Conclusions Our findings highlight the significant potential of MIR4435-2HG as a highly promising biomarker for TNBC. Targeting MIR4435-2HG could represent an appealing therapeutic approach to suppress TNBC metastasis. MIR4435-2HG Triple-negative breast cancer Biomarker MAPK signaling pathway Tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Background Breast cancer represents the most frequently diagnosed cancer for women around the world. It is a heterogeneous and complex disease, encompassing various subtypes that exhibit distinct molecular characteristics and clinical outcomes[1]. One of the aggressive subtypes is TNBC, comprising 10–20% of breast cancer cases. It is distinguished by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression[2]. The lack of targeted therapies for TNBC poses a significant challenge to its management. Hence, there is a pressing demand to explore novel therapeutic targets and develop more efficacious treatment strategies for TNBC. In recent years, lncRNAs have emerged as significant contributors to cancer biology and have gained considerable attention in breast cancer research. LncRNAs, which are non-coding RNA molecules exceeding 200 nucleotides in length, exert regulatory control over gene expression in multiple different ways, including modulating chromatin remodeling as well as regulating the transcription and translation process[3–5]. Accumulating evidence supports the notion that aberrant expression or altered function of lncRNAs is associated with the development and progression of breast cancer, including TNBC. Furthermore, an increasing number of lncRNAs have been proposed as potential biomarkers for TNBC, offering new opportunities for personalized medicine approaches[6]. In our previously published article, we have constructed a prognostic model comprising 12 lncRNAs associated with hypoxia[7]. However, the functional characterization of most of these lncRNAs remains unexplored in the breast cancer context, necessitating further experimental investigations for exploration and validation. The Mitogen-Activated Protein Kinase (MAPK) signaling pathway plays a vital role in numerous cellular processes, such as cell differentiation, proliferation, apoptosis, angiogenesis, and invasion[8]. The involvement of lncRNAs in regulating the MAPK signaling pathway has become apparent, working alongside growth factors and cytokines during the progression of breast cancer[9]. For instance, the repression of ERK1/2 and p38 phosphorylation by PTENP1 could inhibit the growth and migration of breast cancer cells[10]. Hence, a combination of inhibitors targeting lncRNA and MAPK pathways could hold promise as an alternative therapeutic approach in cancer treatment. In our study, we aimed to ascertain the potential of MIR4435-2HG as both a biomarker and therapeutic target for TNBC. 2 Materials and Methods 2.1 Acquisition of data We obtained the clinicopathological information and transcriptome data of patients with breast cancer from the TCGA database. Several gene sets used for GSEA were acquired from the Molecular Signatures Database (version 7.5.1). Additionally, TNMplot[11], XianTao platform( https://www.xiantaozi.com/ ), and GEPIA2.0 were utilized to assist our analysis. 2.2 Differential gene expression analysis Based on the median expression of MIR4435-2HG, TNBC cases were categorized into high- and low-MIR4435-2HG groups. The R package "limma" or “DEseq2” was utilized for the identification of differentially expressed genes for each data type (TPM or counts). R packages “pheatmap” and “EnhancedVolcano” were utilized to generate the heatmaps and volcano plots. The STRING tool (V12.0) was used to generate the protein–protein interaction (PPI) network. We screened protein interaction pairs greater than 400 and then visualized the network plot via Cytoscape 3.8.2 with the CytoHubba app to identify the hub genes. 2.3 Co-expressed genes and gene enrichment analysis The TNBC sample data were examined using Spearman correlation analysis to identify positively correlated genes with MIR4435-2HG with the criteria of |Correlation Coefficient| >0.3 and P < 0.05. We performed KEGG and GO analysis by utilizing the “clusterProfiler” R package. The plots were visualized via the R package “ggplot2”. To identify differential biological functions and pathways, we conducted GSEA (version 4.1.0). The statistical significance level was determined as follows: |NES| >1, FDR.qval < 0.25, and NOM.pval < 0.05. 2.4 Establishment and assessment of a nomogram To predict 1-, 3-, 5-, and 10-year DFS in Basal-like breast cancer patients, we utilized the R package "rms" to develop a nomogram that assigns points to prognostic factors. In addition, to assess the predictive accuracy of the nomogram, we computed the concordance index (C-index) and generated calibration curves. 2.5 Drug Sensitivity Analysis To predict variations in drug sensitivity to distinct chemotherapeutics between the high- and low-MIR4435-2HG groups, we employed the “pRRophetic” R package based on tumor expression profiles. The drug sensitivity was determined by the half-maximal inhibitory concentration (IC50). Additionally, we employed the TIDE algorithm to forecast the potential for tumor immune evasion and response to immune checkpoint blockade therapy in both groups. 2.6 Tumor microenvironment analysis Multiple algorithms, including CIBERSORT, TIMER, xCELL, CIBERSORT-ABS, quanTIseq, EPIC, MCPcounter as well as TIDE were used to analyze the disparities in the levels of immune and stromal cell infiltration between groups with high- and low-MIR4435-2HG expression. Additionally, Spearman's rank correlation was calculated to assess the relationship between immune or stromal infiltration score and MIR4435-2HG mRNA expression levels in samples of TNBC. 2.7 Single-cell RNA sequencing analysis We analyzed MIR4435-2HG expression in one TNBC tissue using the GEO dataset (GSE188600)[12] with the R package “Seurat”. Cells expressing ≤ 200 genes were excluded from the analysis. Additionally, cells that contained ≤ 1,000 unique molecular identifiers (UMIs), ≥ 3% hemoglobin counts as well as mitochondrial reads counts ≥ 20% were also removed. We normalized the data using a global-scaling normalization method (LogNormalize) with the scaling factor value set to 10,000. We employed the FindVariableGenes module to identify 2,000 highly variable genes. We utilized Uniform manifold approximation and projection (UMAP) to perform data dimensionality reduction. We then annotated the cell types according to the expression of marker genes. 2.8 Cell culture and transient transfection experiments The cell lines utilized in this study were acquired from the Stem Cell Bank, Chinese Academy of Sciences (Shanghai, China). MCF-10A cells were cultured in a medium composed of DMEM/F12 supplemented with 5% HS, 20ng/mL EGF, 10µg/mL Insulin, 0.5µg/mL Hydrocortisone, 1% NEAA, 1% Penicillin and Streptomycin (P/S); We cultured MDA-MB-231 cells in DMEM/F12 supplemented with 10% fetal bovine serum (FBS; BI, USA) and 1% P/S; MDA-MB-468, MCF-7, and SK-BR3 cells were cultured in DMEM supplemented with 10% FBS and 1% P/S; BT-474 cells were cultured in 1640 supplemented with 10% FBS and 1% P/S. These cells were incubated in a humidified environment containing at 37°C with 5% CO2. Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) was utilized for transient cell transfection experiments. All the experiments were implemented 24 hours after transfection. The sequences for the siRNAs were as follows (siMIR1, forward, 5’-CAACCUUAAUGAACUGUAUTT-3’, and reverse, 5’-AUACAGUUCAUUAAGGUUGTT-3’; siMIR2, forward, 5’-CCCAGAUUUAAGGGCUAUUTT-3’, and reverse, 5’-AAUAGCCCUUAAAUCUGGGTT-3’) and siRNA negative control (si-NC, forward, 5’-UUCUCCGAACGUGUCACGUdTdT-3’, and reverse, 5’-ACGUGACACGUUCGGAGAAdTdT-3’). 2.9 RNA isolation and quantificational real-time polymerase chain reaction (qRT-PCR) We performed standard TRIzol-based RNA isolation in the cell lines mentioned above. The synthesis of cDNA was accomplished using the Reverse Transcription Kit (XinBei, catalog no.R202-02). The SYBR qPCR Mix (Q204-01) was used for the qRT-PCR in Quantstudio 7 flex (ABI, USA). The following primers were used: ACTB forward, 5’- CACCATTGGCAATGAGCGGTTC-3’, and reverse, 5’-AGGTCTTTGCGGATGTCCACGT-3’; MIR4435-2HG forward, 5’- TGACATTCCAGACAAGCGGTG-3’, and reverse, 5’- GGAAAAGATGCTGGTGACTGC-3’; CDH1 forward, 5’- CCCAATACATCTCCCTTCACAG-3’, and reverse, 5’- CCACCTCTAAGGCCATCTTTG-3’; CDH2 forward, 5’- CCTCCAGAGTTTACTGCCATGAC-3’, and reverse, 5’- GTAGGATCTCCGCCACTGATTC-3’. 2.10 Western blotting We extracted the cell lysates by utilizing RIPA buffer (Epizyme, Shanghai, China). The lysates were loaded onto 4–12% Bis-Tris Super PAGE precast gels (Epizyme, catalog no.LK308) and then transferred to PVDF membranes (0.45 µm, Millipore, MA), which were subsequently blocked by NcmBlot Blocking Buffer (NCM biotech, Suzhou, China). Then the membranes were incubated overnight with specific primary antibodies at 4°C. Subsequently, a one-hour incubation at room temperature with proper secondary antibody was performed. Finally, the membranes were subjected to imaging. The antibodies utilized in the current study are as follows: anti-p-JNK(Thr183/Tyr185)(#4668), anti-p-Erk1/2 (Thr202/Tyr204)(#4370), anti-p-p38 (Thr180/Tyr182)(#9211), anti-E-Cadherin (#3195), and anti-p-AKT (Ser473)(#4060) from Cell Signaling Technology; anti-p-JUN (Ser73), anti-Alpha Tubulin (66031-1-Ig), and anti-GAPDH (60004-1-Ig) from Proteintech (Wuhan, China); anti-p-GSK3β (Ser9)(#AF2016) from Affinity (Cincinnati, OH, USA). 2.11 Cell proliferation assays We seeded the cells (with 3000 cells for MDA-MB-231 and 6000 cells for MDA-MB-468) in a 96-well plate (100 µl/well). Each sample was set up with five replicate wells and incubated in a CO2 incubator for 24 hours at 37°C with 5% CO2. The culture medium was aspirated from each well, and a solution was prepared by combining the CCK-8 reagent with serum-free culture medium (1:10). Then, a volume of 110 µl of the prepared solution was added to each well and then we incubated the plate incubated at 37°C for 3–4 hours. Finally, we measured the absorbance at a wavelength of 450 nm using the microplate reader, with 600 nm as the reference wavelength. 2.12 Transwell assays After transfection for 24 hours, cells (3*10^4 for MDA-MB-231 migration; 6*10^4 for MDA-MB-231 invasion; 10*10^4 for MDA-MB-468 migration; 6*10^4 for MDA-MB-468 invasion) with serum-free medium were added to the transwell (8µm pore size, Corning) upper chamber. For invasion assays, the upper surfaces were coated with 50 µl Matrigel, which was diluted at the ratio of 1:8 with serum-free medium. The coated chambers were then allowed to solidify at 37°C for 3–4 hours. The inserts were placed into wells containing serum medium (20% FBS). 48 hours later, cells in the upper compartment were scraped off and those migrated to the lower surface were fixed with methanol and subsequently stained with 0.1% crystal violet for 15 minutes each step. 2.13 Wound healing assays MDA-MB-231 and MDA-MB-468 cells were seeded in 12-well plates. 24 hours after plating, cells reached more than 95% confluence. Then the scratches were created using 200 µl pipette tips, and the cells were subsequently washed three times with PBS to eliminate any floating cells. The medium was then replaced by the fresh serum-free medium. We captured the images after 0, 24, and 48 hours. 2.14 RNA Sequencing Total RNA was extracted from MDA-MB-231 cells using TRIzol (Thermo Fisher, 15596018) after 36-hour-transfection with either siNC or siMIR2. The total RNA was quality controlled using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), and the integrity of the RNA was examined by Bioanalyzer 2100 (Agilent, CA, USA). For downstream cDNA library construction, a RIN value greater than 7.0, a concentration of greater than 50 ng/µL, and a minimum amount of 1µg of total RNA were deemed sufficient. High-throughput sequencing was performed using Illumina NovaseqTM 6000 according to the standard operation with the sequencing mode as PE150. 2.15 Statistical analysis We analyzed the data using R software for Mac (version 2022.07.1) and Prism 8 (version 8.3.1). The Wilcoxon test was utilized to compare the gene expression levels and the immune or stromal cell infiltration scores between two independent groups. The survival analysis was performed via the Kaplan–Meier method and log-rank tests. Additionally, the chi-square test was employed to examine the correlation between MIR4435-2HG expression level and patients’ clinicopathological factors. Either Spearman’s rank correlation or Pearson correlation was used for correlation analyses. Statistical significance in this study was defined as follows: * P < 0.05, ** P < 0.01, and *** P < 0.001. 3 Results 3.1 Identification of MIR4435-2HG as a diagnostic and prognostic factor for breast cancer We employed the Xiantao platform to examine the mRNA expression levels of MIR4435-2HG of tumor tissues compared to corresponding normal tissues within the TCGA database. As shown in Fig. 1 A, the mRNA expression of MIR4435-2HG was significantly increased in various cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Furthermore, MIR4435-2HG showed elevated expression in three PAM50 subtypes of breast cancer, particularly for the Basal-like subtype (Fig. 1 B). Utilizing the TNMplot tool, we observed a gradual increase in the expression of MIR4435-2HG in normal breast tissues, breast cancer tissues, and metastatic breast cancer tissues (Fig. 1 C). Subsequently, we performed survival analysis using patients’ information from the TCGA database and found that high expression of MIR4435-2HG was associated with poorer overall survival (OS) and DFS in breast cancer patients as a whole (Fig. 1 D, E). Although in the Basal-like subgroup, high expression of MIR4435-2HG did not exhibit a significant association with OS, it was significantly linked to shorter DFS (Fig. 1 F, G). Additionally, we collected and integrated clinicopathological information of breast cancer patients and conducted chi-square tests and Wilcoxon tests according to the expression level of MIR4435-2HG. These analyses revealed significant differences in clinicopathological factors, including T stage, N stage, age, and PAM50 subtypes between high- and low-MIR4435-2HG groups (Table 1 ). Then we performed univariate Cox regression analysis using DFS as the outcome event and included factors with p-values less than 0.1 in further multivariate Cox regression analysis. The results in Table 2 showed that the univariate hazard ratio (HR) for high expression of MIR4435-2HG in the overall breast cancer patients was 1.97 (95% CI 1.0-3.7, p = 0.037), and the multivariate HR was 2.07 (95% CI 1.1–3.9, p = 0.026). For Basal-like breast cancer patients, the univariate and multivariate HRs for high expression of MIR4435-2HG were 4.65 (95% CI 1.0-21.5, p = 0.049) and 9.24 (95% CI 1.1–78.6, p = 0.042). The ROC analysis demonstrated that the expression of MIR4435-2HG had good diagnostic accuracy for different subtypes of breast cancer. The area under the curve (AUC) for Luminal, HER2+, and Basal-like breast cancer patients were up to 0.855, 0.933, and 0.862, respectively (Fig. 1 H, I, J). Table 1 Baseline patients characteristics(n = 1097). Characteristic Low expression of MIR4435-2HG High expression of MIR4435-2HG p statistic method n 541 542 T stage, n (%) 0.005 12.82 Chisq.test 1 124 (11.5%) 153 (14.2%) 2 323 (29.9%) 306 (28.3%) 3 82 (7.6%) 57 (5.3%) 4 11 (1%) 24 (2.2%) N stage, n (%) 0.016 10.29 Chisq.test 0 266 (25%) 248 (23.3%) 1 178 (16.7%) 180 (16.9%) 2 43 (4%) 73 (6.9%) 3 44 (4.1%) 32 (3%) M stage, n (%) 1.000 0 Chisq.test 0 457 (49.6%) 445 (48.3%) 1 10 (1.1%) 10 (1.1%) Pathologic stage, n (%) 0.201 4.63 Chisq.test I 82 (7.7%) 99 (9.3%) II 327 (30.8%) 292 (27.5%) III 113 (10.7%) 129 (12.2%) IV 9 (0.8%) 9 (0.8%) Age, n (%) 0.003 9.14 Chisq.test 60 266 (24.6%) 216 (19.9%) PAM50, n (%) 0.023 11.36 Chisq.test Normal 21 (1.9%) 19 (1.8%) LumA 287 (26.5%) 275 (25.4%) LumB 114 (10.5%) 90 (8.3%) Her2 29 (2.7%) 53 (4.9%) Basal 90 (8.3%) 105 (9.7%) Age, median (IQR) 60 (50, 67) 56 (47, 67) 0.002 162179.5 Wilcoxon Table 2 Univariate and multivariate Cox regression analysis among breast patients. Parameters TCGA All Samples DFS TCGA PAM50_Basal_like DFS Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis HR(95% CI) p-value HR(95% CI) p-value HR(95% CI) p-value HR(95% CI) p-value Age ≤ 65 1(ref) 1(ref) > 65 0.84 (0.5–1.5) 0.570 0.67 (0.1–5.3) 0.702 Stage I-II 1(ref) 1(ref) 1(ref) 1(ref) III-IV 3.15 (1.7–5.8) ༜0.001 2.10 (1.0-4.4) 0.051 13.37 (3.9–46.0) ༜0.001 6.51 (1.4–30.4) 0.017 T 1–2 1(ref) 1(ref) 1(ref) 1(ref) 3–4 2.70 (1.4–5.3) 0.004 1.66 (0.7–3.7) 0.216 7.37 (2.2–24.8) 0.001 4.86 (0.8–29.1) 0.083 N 0 1(ref) 1(ref) 1–3 1.21 (0.7–2.2) 0.528 2.33 (0.7–7.2) 0.144 M 0 1(ref) 1(ref) 1(ref) 1(ref) 1 9.36 (2.9–30.4) ༜0.001 6.20 (1.8–21.9) 0.005 6.80 (0.8–55.8) 0.074 2.67 (0.1–57.2) 0.529 MIR4435-2HG Low 1(ref) 1(ref) 1(ref) 1(ref) High 1.97 (1.0-3.7) 0.037 2.07 (1.1–3.9) 0.026 4.65 (1.0-21.5) 0.049 9.24 (1.1–78.6) 0.042 3.2 Functional annotation of MIR4435-2HG in Basal-like breast cancer Through Spearman correlation analysis, we identified the genes that exhibited a significant positive correlation with MIR4435-2HG in Basal-like breast cancer tissues, with the criteria of |Correlation Coefficient| >0.3 and P < 0.05. The functional annotation results based on GO and KEGG are presented in Fig. 2 A. Additionally, we performed differential gene expression analysis between high- and low-MIR4435-2HG Basal-like breast cancer patients. We identified the differentially expressed genes (|logFC|>0.5, p < 0.05) to conduct GO and KEGG analysis (Fig. 2 B). The significantly enriched pathways included “ECM − receptor interaction”, “extracellular structure organization” and other related pathways. Moreover, GSEA revealed significant associations between MIR4435-2HG and pathways such as “Epithelial Mesenchymal Transition”, “Hypoxia”, “Breast Cancer Ductal Invasive UP”, and “EMT Breast Tumor up”, which are known to be characteristic of Basal-like breast cancer (Fig. 2 C, D). 3.3 Construction and assessment of a nomogram as well as drug sensitivity analysis A nomogram was constructed to predict the prognosis of Basal-like breast cancer patients incorporating key prognostic factors, including age, stage, and expression of MIR4435-2HG. This nomogram assigns a point to each subgroup based on these indicators, allowing us to calculate the total points for each patient and predict their 1-, 3-, 5-, and 10-year DFS. For example, a 51-year-old patient diagnosed with TNBC and clinical stage II, with MIR4435-2HG log2(TPM + 1) expression of 4.01, accumulated a total score of 127 points. Based on the nomogram, this patient was estimated to have a 1-, 3-, 5-, and 10-year DFS of 96.17%, 81%, 67.4%, and 57.5%, respectively (Fig. 3 A). The nomogram's predictive performance was assessed using the C-index, which was computed to be 0.811 (95% CI 0.685–0.937). Furthermore, calibration curves were constructed to examine the calibration or accuracy of the nomogram in predicting 1-, 3-, and 5-year DFS (Fig. 3 B-D). The disparities in chemosensitivity between the high- and low-MIR4435-2HG groups were assessed by calculating the variance in IC50 values. Patients in the high-MIR4435-2HG expression level group demonstrated reduced chemosensitivity to cisplatin, doxorubicin, and gemcitabine while exhibiting enhanced chemosensitivity to paclitaxel (Fig. 3 E-H). What’s more, patients exhibiting high expression levels of MIR4435-2HG may have a greater potential for tumor immune evasion, which suggests that they might be less likely to benefit from the immune checkpoint inhibitors, such as anti-PD1/CTLA-4 therapies ( p = 0.114)(Fig. 3 I, J). 3.4 Correlation between MIR4435-2HG expression and immune or stromal cell infiltration Tumor microenvironment infiltration scoring results calculated by the CIBERSORT algorithm revealed a significantly lower proportion of activated CD4 + memory T cells and CD8 + T Cells in high-MIR4435-2HG Basal-like breast cancer patients (Fig. 4 A). Additionally, we employed multiple algorithms to calculate the correlation coefficient between MIR4435-2HG expression and the abundance of other tumor microenvironment cells. The results demonstrated a negative correlation between the MIR4435-2HG expression level and infiltration levels of certain immune cells, including CD8 + T cells and CD4 + T cells, while a significant positive correlation with stromal cells, i.e. cancer-associated fibroblasts (CAFs)(Fig. 4 B, Supplementary Fig. 1 ). The immune scores exhibited no significant disparity between the high and low expression groups of MIR4435-2HG (Fig. 4 C), whereas the stromal scores demonstrated a noticeably elevated level higher in the high MIR4435-2HG expression group in comparison to the low MIR4435-2HG expression group (Fig. 4 D). With the TIDE algorithm, the average expression levels of CD8A and CD8B were found to be decreased in the high-MIR4435-2HG group (Fig. 4 E), and MIR4435-2HG expression demonstrated strong relevance to FAP + CAF signature (Fig. 4 F). Moreover, the MIR4435-2HG expression was associated with the expression of CAFs’ biomarkers ( ACTA2 , PDGFRB , FAP , VIM ) in Basal-like breast cancer tissues (Fig. 4 G-J). These findings suggest that MIR4435-2HG may potentially influence breast cancer by reshaping the tumor microenvironment. 3.5 Expression of MIR4435-2HG within the tumor microenvironment analyzed by single-cell sequencing data After quality control (see Methods) of the single-cell sequencing data from one TNBC sample (Fig. 5 A), we utilized principal component analysis (PCA) to reduce the data dimensionality (Fig. 5 B). For visualization, we further reduced the single-cell expression data to two dimensions using UMAP. A total of 11 cell clusters were identified, and finally, we annotated 8 major cell types according to gene expression patterns within these clusters (Fig. 5 C). Interestingly, MIR4435-2HG was detected not only in tumor epithelial cells but also in various other cell types within the tumor microenvironment (Fig. 5 D, E). This suggests that MIR4435-2HG may have a broader impact beyond tumor cells alone, potentially influencing various components of the tumor microenvironment in TNBC. 3.6 MIR4435-2HG enhances migration and invasion of TNBC cells via EMT in vitro Through qRT-PCR experiments, we noted a remarkable decrease in the expression level of MIR4435-2HG was significantly lower in luminal-type breast cancer cell lines (MCF-7, BT-474) in comparison to normal breast epithelial cell line (MCF-10A) but showed no significant difference in HER2-positive breast cancer cell line (SK-BR3). In contrast, the MIR4435-2HG expression was significantly elevated in TNBC cell lines (MDA-MB-231, MDA-MB-468) (Fig. 6 A). We chose these two TNBC cell lines for transient siRNA transfection and found that the knockdown efficiency was higher with the second set of siRNA (MIR4435-2HG si2, siMIR2), reaching 75% in MDA-MB-231 and 80% in MDA-MB-468 (Fig. 6 B). We performed CCK-8 assays using MDA-MB-231 and MDA-MB-468 cells transiently transfected with siNC and siMIR2. The findings revealed that MIR4435-2HG knockdown did not significantly affect the proliferation of MDA-MB-231 cells while exhibiting a notable inhibitory effect on the proliferation of MDA-MB-468 cells ( Supplementary Fig. 2 ). Thus, we focused on investigating the influence of MIR4435-2HG on the migratory capacity of TNBC cells. We performed transwell migration and invasion (Matrigel-coated) assays and found that MDA-MB-231 and MDA-MB-468 cells transfected with siMIR2 displayed significantly reduced migration and invasion abilities (Fig. 6 C). Besides, we performed wound healing assays in both cell lines. At 48 hours after creating the wounds, we observed significantly larger wound areas in the siMIR2 group as compared to the siNC group, indicating a weakened cell migration ability (Fig. 6 D). Furthermore, we examined the expression of key genes (CDH1 and CDH2) in the EMT process. Knockdown of MIR4435-2HG in both cell lines caused a reduction in the mRNA level of CDH2 (a mesenchymal marker) by PCR, and an increase in the mRNA and protein levels of CDH1 (an epithelial marker) as detected by PCR, RNA-seq, and western blot analysis (Fig. 6 E, F). Based on these findings, it can be inferred that MIR4435-2HG enhances the migration and invasion abilities of TNBC cells by orchestrating the EMT program in vitro. 3.7 MIR4435-2HG activates the JNK/c-Jun and p38 MAPK pathway in TNBC cells To explore the mechanisms through which MIR4435-2HG contributes to the development of TNBC, we performed transcriptome RNA sequencing analysis. The differentially expressed genes were visualized in a heatmap (Fig. 7 A) and a volcano plot (Fig. 7 B). As shown in Fig. 7 C, from the PPI network analysis based on these differentially expressed genes, IL6, CASP3, and MAPK8 were identified as the top three hub genes. Further analysis using KEGG and GSEA unveiled a significant enrichment of the MAPK signaling pathway (Fig. 7 D, E). The KEGG pathways to which the genes were annotated were summarized and fill-in colors were used to locate nodes in the pathway where the differential genes were located ( Supplementary Fig. 3 ). Besides, we filtered out the downregulated genes (logFC<-1, p < 0.05) in MDA-MB-231 cells with MIR4435-2HG knockdown and performed KEGG analysis (Fig. 7 F). As expected, the MAPK signaling pathway exhibited the highest level of enrichment. To validate the involvement of the MAPK signaling pathway, we performed western blot analyses. The results indicated a significant reduction in the levels of p-JNK, p-JUN, and p-p38, while no obvious change was identified in the level of p-ERK1/2 in MIR4435-2HG-knockdown TNBC cells when compared to the control group, suggesting that MIR4435-2HG might specifically activate the JNK/c-Jun and p38 MAPK pathway rather than the classical MAPK pathway (Fig. 7 G). Discussion TNBC is widely recognized as a highly aggressive and heterogeneous subtype of breast cancer. Although TNBC is known to be highly responsive to standard chemotherapy regimens, the lack of targeted adjuvant therapies has been linked to early relapse and poor OS in TNBC patients[13]. Distant metastasis is estimated to occur in approximately 46% of TNBC patients, with a 40% mortality rate within 5 years of diagnosis[14]. Hence, identification of targetable TNBC-associated molecules and elucidation of the mechanisms underlying their contribution to the high metastatic capacity of TNBC are crucial for improving treatment efficacy and patient survival. LncRNA MIR4435-2HG, which locates on human chromosome 2q13, spans a total length of 517,362 base pairs and was identified as a lncRNA associated with gastric cancer initially[15]. MIR4435-2HG was elevated in cervical cancer and MIR4435-2HG knockdown inhibited the proliferation and metastasis abilities of cervical cancer through the miR-128-3p/MSI2 axis[16]. In hepatocellular carcinoma, the upregulation of MIR4435-2HG has been found to enhance cancer cell growth and invasion, exerting a significant impact on the OS of liver cancer patients[17, 18]. Moreover, MIR4435-2HG was also reported to play oncogenic roles in oral squamous cell carcinoma[19], lung cancer[20], prostate cancer[21], ovarian cancer[22], and glioblastoma[23]. However, in colorectal cancer, MIR4435-2HG exhibits a tumor suppressor function by reprogramming neutrophils[24]. Previous studies have provided evidence that MIR4435-2HG can either promote the progression of ER-positive breast cancer through the miR-22-3p/TMEM9B pathway[25] or promote breast cancer via the Wnt/β-catenin signaling pathway[26]. However, it is worth noting that the primers used in these studies, the former covering one transcript of MIR4435-2HG and one transcript of CYTOR (a highly homologous RNA to MIR4435-2HG), and the latter covering three transcripts of MIR4435-2HG. Both studies detected high expression of MIR4435-2HG in ER-positive breast cancer cell lines (ZR-75-30 and MCF7) in comparison to MCF-10A. In our study, we used primers that covered six out of the eight transcripts of MIR4435-2HG. Through qRT-PCR analysis using RNA extracted from different batches of cells, we observed significantly lower expression of MIR4435-2HG in luminal subtype cell lines (MCF7 and BT-474) when compared to MCF-10A. Conversely, we found significantly higher expression of MIR4435-2HG in TNBC cell lines (MDA-MB-231 and MDA-MB-468). Based on the close relationship between MIR4435-2HG expression and clinicopathological information of Basal-like breast cancer patients, we specifically chose TNBC cell lines for the exploration of the role of MIR4435-2HG in TNBC. Furthermore, using the Blast tool ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ), we compared the siRNA sequence (5'-CCCAGAUUUAAGGGCUAUUTT-3') used in Chen et al.'s study mentioned above and found that it targeted the NR_015395.2 transcript, while the primers utilized in their study (Forward, 5'-AGGCCCGGAATCTTTCA-3' and reverse, 5'-GCCTCTCCCTGAATAACTGGG-3') detected the NR_024373.2 transcript. Similarly, in Ke et al.'s study, the shRNA (5'-GGTCTGGTCGGTTTCCCATTT-3') targeted five transcripts of CYTOR and one transcript of MIR4435-2HG (NR_024373.2), while the primers used (forward, 5'-CGGAGCATGGAACTCGACA-3', and reverse, 5'-CAAGTCTCACACATCCGGG-3') did not include NR_024373.2. Therefore, it is possible that the findings from these studies might not accurately reflect the expression of MIR4435-2HG in breast cancer cell lines, and the observed phenotypes may be confounded by changes in the expression levels of highly homologous RNA, CYTOR. It is important to note that although MIR4435-2HG and CYTOR are highly homologous, this does not necessarily mean that they have identical functions. Genes can undergo evolutionary changes, leading to alterations in their expression patterns and functions. The siRNA used in our study specifically targeted the six transcripts of MIR4435-2HG covered by the primers we used and did not affect the expression level of CYTOR[24]. This allowed for a more accurate validation of the functional role of MIR4435-2HG in our experimental setting. In the current study, we observed that MIR4435-2HG may promote metastasis of TNBC through the non-classical MAPK signaling pathway. This finding adds to the existing knowledge about the mechanisms through which MIR4435-2HG contributes to tumor progression, such as the TGF-β1 signaling pathway, Wnt/β‑catenin signaling pathway, and competing endogenous RNA (ceRNA) network. Furthermore, using bioinformatics analysis, we proposed that MIR4355-2HG could serve as an effective prognostic and diagnostic factor for TNBC. Elevated expression of MIR4435-2HG correlated with lower sensitivities to cisplatin, doxorubicin, and gemcitabine while showing higher sensitivity to paclitaxel. These findings have the potential to guide personalized clinical therapeutics in TNBC. Through tumor microenvironment infiltration analysis, we observed a higher level of infiltration of stromal cells in the high-MIR4435-2HG group. MIR4435-2HG expression exhibited a positive correlation with CAFs while a negative correlation with CD8 + T cells. This led us to propose a potential mechanism by which MIR4435-2HG may remodel the tumor microenvironment of TNBC by promoting CAFs-mediated CD8 + T-cell exclusion. Conclusions and limitations Our study elucidates for the first time that MIR4435-2HG serves as a promising diagnostic and prognostic biomarker of TNBC and it promotes the migration and invasion abilities of TNBC cells by driving the EMT process through the activation of JNK/c-Jun and p38 MAPK signaling pathway, suggesting that targeting MIR4435-2HG could be an appealing therapeutic strategy for the prevention of TNBC metastasis. However, it is important to note that our current experiments focused solely on TNBC cell lines. To gain a deeper understanding of the complex functions of MIR4435-2HG in TNBC and uncover new treatment methods, additional studies involving diverse experimental models and clinical samples are warranted. Abbreviations TNBC: Triple-negative breast cancer; LncRNA: Long non-coding RNA; TCGA: The Cancer Genome Atlas; ROC: Receiver operating characteristic; GSEA: Gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DFS: Disease-free survival; TIDE: Tumor Immune Dysfunction and Exclusion; EMT: Epithelial–mesenchymal transition; ER: Estrogen receptor; PR: Progesterone receptor; HER2: Human epidermal growth; MAPK: Mitogen-Activated Protein Kinase; PPI: protein–protein interaction; C-index: Concordance index; IC50: Half-maximal inhibitory concentration; NCBI: National Center for Biotechnology Information; UMIs: Unique molecular identifiers; UMAP: Uniform manifold approximation and projection; P/S: Penicillin and streptomycin; FBS: Fetal bovine serum; qRT-PCR: quantificational real-time polymerase chain reaction; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; ESCA: Esophageal carcinoma; HNSC: Head and neck squamous cell carcinoma; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; READ: Rectum adenocarcinoma; STAD Stomach adenocarcinoma; THCA: Thyroid carcinoma; UCEC: Uterine corpus endometrial carcinoma; OS: Overall survival; HR: Hazard ratio; AUC: Area under the curve; CAFs: Cancer-associated fibroblasts; ceRNA: Competing endogenous RNA. Declarations Acknowledgments We acknowledge contributors for uploading their meaningful datasets to the public platforms. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The datasets supporting the conclusions of this study are available in the TCGA repository (https://cancergenome.nih.gov/), the GEO websites (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse188600), and the Molecular Signatures Database V7.5.1 (https://www.gsea-msigdb.org/gsea/msigdb/). Conflict of interest The authors declare that they have no competing interests. Funding This work was supported by the Natural Science Foundation of China [grant number 82172907]. Authors’ contributions GP, YB, and SX conceived and designed the study. GP, DWT, and ZWT performed the experiments. GP, WWH, and WYH acquired and analyzed the data. GP prepared the figures and tables and drafted the manuscript. SL, ZL, WW, and WRT revised the manuscript. All authors have read and approved the final manuscript. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48. Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750-67. Tan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun (Lond). 2021;41(2):109-20. Yang F, Zhang H, Mei Y, Wu M. Reciprocal regulation of HIF-1α and lincRNA-p21 modulates the Warburg effect. Mol Cell. 2014;53(1):88-100. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010;464(7291):1071-6. Maldonado V, Melendez-Zajgla J. The Role of Hypoxia-Associated Long Non-Coding RNAs in Breast Cancer. Cells. 2022;11(10). Gu P, Zhang L, Wang R, Ding W, Wang W, Liu Y, et al. Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer. Front Cell Dev Biol. 2021;9:796729. Anjum J, Mitra S, Das R, Alam R, Mojumder A, Emran TB, et al. A renewed concept on the MAPK signaling pathway in cancers: Polyphenols as a choice of therapeutics. Pharmacol Res. 2022;184:106398. Maharati A, Moghbeli M. Long non-coding RNAs as the critical regulators of PI3K/AKT, TGF-β, and MAPK signaling pathways during breast tumor progression. J Transl Med. 2023;21(1):556. Chen S, Wang Y, Zhang JH, Xia QJ, Sun Q, Li ZK, et al. Long non-coding RNA PTENP1 inhibits proliferation and migration of breast cancer cells via AKT and MAPK signaling pathways. Oncol Lett. 2017;14(4):4659-62. Bartha A, Gyorffy B. TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues. Int J Mol Sci. 2021;22(5). Pullikuth AK, Routh ED, Zimmerman KD, Chifman J, Chou JW, Soike MH, et al. Bulk and Single-Cell Profiling of Breast Tumors Identifies TREM-1 as a Dominant Immune Suppressive Marker Associated With Poor Outcomes. Front Oncol. 2021;11:734959. Mirzania M. Approach to the Triple Negative Breast Cancer in New Drugs Area. Int J Hematol Oncol Stem Cell Res. 2016;10(2):115-9. Yin L, Duan JJ, Bian XW, Yu SC. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 2020;22(1):61. Ke D, Li H, Zhang Y, An Y, Fu H, Fang X, Zheng X. The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer. Oncotarget. 2017;8(13):21516-25. Wang R, Liu L, Jiao J, Gao D. Knockdown of MIR4435-2HG Suppresses the Proliferation, Migration and Invasion of Cervical Cancer Cells via Regulating the miR-128-3p/MSI2 Axis in vitro. Cancer Manag Res. 2020;12:8745-56. Bai Y, Lin H, Chen J, Wu Y, Yu S. Identification of Prognostic Glycolysis-Related lncRNA Signature in Tumor Immune Microenvironment of Hepatocellular Carcinoma. Front Mol Biosci. 2021;8:645084. Shen X, Ding Y, Lu F, Yuan H, Luan W. Long noncoding RNA MIR4435-2HG promotes hepatocellular carcinoma proliferation and metastasis through the miR-22-3p/YWHAZ axis. Am J Transl Res. 2020;12(10):6381-94. Shen H, Sun B, Yang Y, Cai X, Bi L, Deng L, Zhang L. MIR4435-2HG regulates cancer cell behaviors in oral squamous cell carcinoma cell growth by upregulating TGF-beta1. Odontology. 2020;108(4):553-9. Qian H, Chen L, Huang J, Wang X, Ma S, Cui F, et al. The lncRNA MIR4435-2HG promotes lung cancer progression by activating beta-catenin signalling. Journal of molecular medicine. 2018;96(8):753-64. Xing P, Wang Y, Zhang L, Ma C, Lu J. Knockdown of lncRNA MIR4435‑2HG and ST8SIA1 expression inhibits the proliferation, invasion and migration of prostate cancer cells in vitro and in vivo by blocking the activation of the FAK/AKT/beta‑catenin signaling pathway. Int J Mol Med. 2021;47(6). Liu S, Qiao Z, Ma Q, Liu X, Ma X. LncRNA CYTOR and MIR4435-2HG in ovarian cancer and its relationship with clinicopathological features. Panminerva Med. 2022;64(1):119-20. Xu H, Zhang B, Yang Y, Li Z, Zhao P, Wu W, et al. LncRNA MIR4435-2HG potentiates the proliferation and invasion of glioblastoma cells via modulating miR-1224-5p/TGFBR2 axis. J Cell Mol Med. 2020;24(11):6362-72. Yu H, Chen C, Han F, Tang J, Deng M, Niu Y, et al. Long Noncoding RNA MIR4435-2HG Suppresses Colorectal Cancer Initiation and Progression By Reprogramming Neutrophils. Cancer Immunol Res. 2022;10(9):1095-110. Ke J, Wang Q, Zhang W, Ni S, Mei H. LncRNA MIR4435-2HG promotes proliferation, migration, invasion and epithelial mesenchymal transition via targeting miR-22-3p/TMEM9B in breast cancer. Am J Transl Res. 2022;14(8):5441-54. Chen D, Tang P, Wang Y, Wan F, Long J, Zhou J, et al. Downregulation of long non-coding RNA MR4435-2HG suppresses breast cancer progression via the Wnt/beta-catenin signaling pathway. Oncol Lett. 2021;21(5):373. Supplementary Figures Supplementary Figures are not available with this version 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3832143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267024486,"identity":"8af84e62-3d14-4baa-9f2d-34ea8a8351ff","order_by":0,"name":"Peng Gu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDADfgaGBBDN2EBIJQ+MIdnAkNhAmhaDAxDVhLXYSx9++Jl3h12e8fkDzx/zMNjIbjjA/OwBXlv40oylec8kF5vdSEhs5mFIM95wgM3cAK8WHgYzZt425sRtNxhAWg4nbjjAwyaBXwv7N6CW+sTN/QdAWv4To4UHZAvQcAawww4QoeUMT7Hk3DPHE2cA/TJzjkGy8czDbGZ4tbD3sG/88HZHdWJ//5mED28q7GT7jjc/w6sFBJh4G8AWJgBjB0gzE1IPBIw/wVrYDxChdhSMglEwCkYiAACIfEd0tl9qOAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5641-960X","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Gu","suffix":""},{"id":267024487,"identity":"0d2de5ac-b51c-42a7-936c-7d3919d4afee","order_by":1,"name":"Wentao Ding","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Ding","suffix":""},{"id":267024488,"identity":"95adc443-a531-43eb-8431-39686ee4f50f","order_by":2,"name":"Wenting Zhu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenting","middleName":"","lastName":"Zhu","suffix":""},{"id":267024489,"identity":"910db940-b7b7-4d00-8fc8-464d43b337c6","order_by":3,"name":"Ling Shen","email":"","orcid":"","institution":"Shanghai General Hospital of Nanjing Medical University: Shanghai General Hospital Songjiang Branch","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Shen","suffix":""},{"id":267024490,"identity":"2f07f255-cf0c-474a-a4b6-dc20d7dec22a","order_by":4,"name":"Bin Yan","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yan","suffix":""},{"id":267024491,"identity":"d83ac772-9814-419f-9ae7-790759b3d11f","order_by":5,"name":"Lei Zhang","email":"","orcid":"","institution":"Shanghai General Hospital of Nanjing Medical University: Shanghai General Hospital Songjiang Branch","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":267024492,"identity":"b311e3c8-9e6f-4bc9-a28a-4ed3e6f39d65","order_by":6,"name":"Wei Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":267024493,"identity":"3460fb7e-e033-4e8d-88bd-6a4b53ebe9d0","order_by":7,"name":"Ruitao Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruitao","middleName":"","lastName":"Wang","suffix":""},{"id":267024494,"identity":"3a507624-df14-46e5-9dfc-020a12bd09c6","order_by":8,"name":"Wenhao Wang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Wang","suffix":""},{"id":267024495,"identity":"ec86dfc2-aff0-41ef-89e1-0f2c6284c9c9","order_by":9,"name":"Yanhao Wang","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Yanhao","middleName":"","lastName":"Wang","suffix":""},{"id":267024496,"identity":"21cd1502-5e65-4f30-a2b6-19393055e9e1","order_by":10,"name":"Xing Sun","email":"","orcid":"https://orcid.org/0000-0001-5256-9758","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-01-03 15:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3832143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3832143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49716308,"identity":"244aec00-d896-4e74-a5f6-471ad81ab0c8","added_by":"auto","created_at":"2024-01-16 21:50:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":721919,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of MIR4435-2HG as a diagnostic and prognostic factor for breast cancer. (A) Expression levels of MIR4435-2HG in pan-cancer tissues and normal tissues. (B) Differential expression of MIR4435-2HG in distinct subtypes of breast cancer tissues in comparison to normal tissues. (C) Expression of MIR4435-2HG in normal tissues, breast cancer tissues, and metastatic breast cancer tissues. (D, E) OS and DFS curves of overall breast cancer patients stratified by MIR4435-2HG expression. (F, G) OS and DFS curves of Basal-like breast cancer patients stratified by MIR4435-2HG expression. (H-J) Assessment of the predictive ability of high MIR4435-2HG expression for different subtypes of breast cancer by ROC analysis. *\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"renamed846b2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/b0b92d94871244891c4a203e.jpg"},{"id":49716314,"identity":"1c1b7973-ef41-440b-a356-51f6c8aa24af","added_by":"auto","created_at":"2024-01-16 21:50:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":527385,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation of MIR4435-2HG in Basal-like breast cancer. (A) GO and KEGG functional annotation for the genes positively correlated with MIR4435-2HG in Basal-like breast cancer. GO, KEGG analyses (B) as well as GSEA (C, D)for the genes differentially expressed in high- and low-MIR4435-2HG Basal-like breast cancer patients.\u003c/p\u003e","description":"","filename":"renamed8c36d.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/54fe8f40eda7055521ae558d.jpg"},{"id":49716312,"identity":"9e05c9b0-9803-43c0-8c79-c6fb043c2946","added_by":"auto","created_at":"2024-01-16 21:50:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":636551,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and evaluation of a nomogram to predict the DFS of Basal-like breast cancer patients as well as the drug sensitivity analysis. (A) A nomogram incorporating prognostic factors, including age, stage, and MIR4435-2HG expression for predicting the 1-, 3-, 5-, and 10-year DFS of Basal-like breast cancer patients. (B-D) Calibration curves of the nomogram predicting the 1-, 3-, and 5-year DFS. (E-H) Differential chemosensitivity of patients in high- and low-MIR4435-2HG groups. (I) Predicted non-responder to immune checkpoint blockade therapy in both groups, according to the combination of TIDE and IFNG signatures. (J) TIDE prediction score in high- and low-MIR4435-2HG groups. *\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"renamed085fb.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/ddc60a52b5cb41c25c2f42c2.jpg"},{"id":49716309,"identity":"cb594470-9f00-4f7b-97d3-e8cb809f50f2","added_by":"auto","created_at":"2024-01-16 21:50:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":718400,"visible":true,"origin":"","legend":"\u003cp\u003eTumor microenvironment infiltration analysis for Basal-like breast cancer patients based on MIR4435-2HG expression level. (A) Utilizing the Cibersort algorithm to compare the differential immune cell infiltration levels in Basal-like breast samples grouped by MIR4435-2HG expression. (B) Correlation of MIR4435-2HG with infiltration degree of tumor microenvironment cells. (C, D) Differential immune scores and stromal scores of between high- and low-MIR4435-2HG Basal-like breast cancer patients. (E) Average expression of CD8A and CD8B in high- and low-MIR4435-2HG groups. (F) Pearson correlation between MIR4435-2HG expression and FAP+ CAF signature. (G-J) Expression correlation analysis between MIR4435-2HG and CAFs’ biomarkers evaluated by Spearman rank correlation. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"renamedb42d0.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/394a28fe61b2cd611bedb3e1.jpg"},{"id":49717151,"identity":"465f8912-cd4a-49f9-a828-209b5d0893f6","added_by":"auto","created_at":"2024-01-16 21:58:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":611233,"visible":true,"origin":"","legend":"\u003cp\u003eScRNA-sequencing analysis. (A) Quality controls of the data. (B) Dimensionality reduction was performed using PCA. (C) UMAP dimension reduction results and cell subsets annotation. (D, E) The expression level of MIR4435-2HG within the TNBC tumor microenvironment.\u003c/p\u003e","description":"","filename":"renameda7676.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/782726d060e034b0e6d08ef8.jpg"},{"id":49716311,"identity":"4eec6cbe-66dd-4833-8a0c-a595d2ed5a98","added_by":"auto","created_at":"2024-01-16 21:50:50","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":944725,"visible":true,"origin":"","legend":"\u003cp\u003eMIR4435-2HGpromotes the migration and invasion abilities of TNBC cells through the EMT process. (A) The mRNA expression of MIR4435-2HG in normal breast epithelial cell line and breast cancer cell lines by PCR.(B) Verification of knockdown efficiency in MDA-MB-231 and MDA-MB-468 cells by PCR. (C) Transwell experiments compared the migratory and invasive capacities of MDA-MB-231 and MDA-MB-468 transfected with siNC or siMIR2. (D) Wound-healing assays compared the migration capacity of MDA-MB-231 and MDA-MB-468 after transient transfection of siNC or siMIR2. (E) mRNA expression level of CDH1 and CDH2 detected by PCR and RNA-seq. (F) The expression of E-Cadherin determined by Western blotting analysis. *\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001.\u003c/p\u003e","description":"","filename":"renamed8032d.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/61fecec036eb75d8c645ad19.jpg"},{"id":49717152,"identity":"b69ca3d4-2437-4e36-8473-93f2711cb6b8","added_by":"auto","created_at":"2024-01-16 21:58:50","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":906543,"visible":true,"origin":"","legend":"\u003cp\u003eDownregulation of MIR4435-2HG attenuates the JNK/c-Jun and p38 MAPK pathway. Heatmap (A) and volcano map (B) display the gene expression patterns in control or MIR4435-2HG knockdown MDA-MB-231 cells. (C) PPI network constructed with the differentially expressed genes. KEGG analysis (D) and GSEA (E) for the differentially expressed genes. (F) KEGG analysis for the downregulated genes (logFC\u0026lt;-1) in MIR4435-2HG-knockdown MDA-MB-231 cells. (G) The activation status of MAPK signaling pathway detected by Western blot.\u003c/p\u003e","description":"","filename":"renamed19bb5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/f30eae0c28bf235b3fc4c685.jpg"},{"id":53695813,"identity":"7b44cff4-f843-4f11-a4a4-7e66a9e63f6f","added_by":"auto","created_at":"2024-03-29 03:33:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1672357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3832143/v1/33e81866-77a8-45a7-bd50-3fce5047fc68.pdf"}],"financialInterests":"","formattedTitle":"MIR4435-2HG: A Novel Biomarker for Triple-Negative Breast Cancer Diagnosis and Prognosis, Driving Tumor Progression through EMT by JNK/c-Jun and p38 MAPK Signaling Pathway Activation","fulltext":[{"header":"1 Background","content":"\u003cp\u003eBreast cancer represents the most frequently diagnosed cancer for women around the world. It is a heterogeneous and complex disease, encompassing various subtypes that exhibit distinct molecular characteristics and clinical outcomes[1]. One of the aggressive subtypes is TNBC, comprising 10\u0026ndash;20% of breast cancer cases. It is distinguished by the lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression[2]. The lack of targeted therapies for TNBC poses a significant challenge to its management. Hence, there is a pressing demand to explore novel therapeutic targets and develop more efficacious treatment strategies for TNBC.\u003c/p\u003e \u003cp\u003eIn recent years, lncRNAs have emerged as significant contributors to cancer biology and have gained considerable attention in breast cancer research. LncRNAs, which are non-coding RNA molecules exceeding 200 nucleotides in length, exert regulatory control over gene expression in multiple different ways, including modulating chromatin remodeling as well as regulating the transcription and translation process[3\u0026ndash;5]. Accumulating evidence supports the notion that aberrant expression or altered function of lncRNAs is associated with the development and progression of breast cancer, including TNBC. Furthermore, an increasing number of lncRNAs have been proposed as potential biomarkers for TNBC, offering new opportunities for personalized medicine approaches[6]. In our previously published article, we have constructed a prognostic model comprising 12 lncRNAs associated with hypoxia[7]. However, the functional characterization of most of these lncRNAs remains unexplored in the breast cancer context, necessitating further experimental investigations for exploration and validation.\u003c/p\u003e \u003cp\u003eThe Mitogen-Activated Protein Kinase (MAPK) signaling pathway plays a vital role in numerous cellular processes, such as cell differentiation, proliferation, apoptosis, angiogenesis, and invasion[8]. The involvement of lncRNAs in regulating the MAPK signaling pathway has become apparent, working alongside growth factors and cytokines during the progression of breast cancer[9]. For instance, the repression of ERK1/2 and p38 phosphorylation by PTENP1 could inhibit the growth and migration of breast cancer cells[10]. Hence, a combination of inhibitors targeting lncRNA and MAPK pathways could hold promise as an alternative therapeutic approach in cancer treatment.\u003c/p\u003e \u003cp\u003eIn our study, we aimed to ascertain the potential of MIR4435-2HG as both a biomarker and therapeutic target for TNBC.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Acquisition of data\u003c/h2\u003e \u003cp\u003eWe obtained the clinicopathological information and transcriptome data of patients with breast cancer from the TCGA database. Several gene sets used for GSEA were acquired from the Molecular Signatures Database (version 7.5.1). Additionally, TNMplot[11], XianTao platform(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com/\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and GEPIA2.0 were utilized to assist our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential gene expression analysis\u003c/h2\u003e \u003cp\u003eBased on the median expression of MIR4435-2HG, TNBC cases were categorized into high- and low-MIR4435-2HG groups. The R package \"limma\" or \u0026ldquo;DEseq2\u0026rdquo; was utilized for the identification of differentially expressed genes for each data type (TPM or counts). R packages \u0026ldquo;pheatmap\u0026rdquo; and \u0026ldquo;EnhancedVolcano\u0026rdquo; were utilized to generate the heatmaps and volcano plots. The STRING tool (V12.0) was used to generate the protein\u0026ndash;protein interaction (PPI) network. We screened protein interaction pairs greater than 400 and then visualized the network plot via Cytoscape 3.8.2 with the CytoHubba app to identify the hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Co-expressed genes and gene enrichment analysis\u003c/h2\u003e \u003cp\u003eThe TNBC sample data were examined using Spearman correlation analysis to identify positively correlated genes with MIR4435-2HG with the criteria of |Correlation Coefficient| \u0026gt;0.3 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We performed KEGG and GO analysis by utilizing the \u0026ldquo;clusterProfiler\u0026rdquo; R package. The plots were visualized via the R package \u0026ldquo;ggplot2\u0026rdquo;. To identify differential biological functions and pathways, we conducted GSEA (version 4.1.0). The statistical significance level was determined as follows: |NES| \u0026gt;1, FDR.qval\u0026thinsp;\u0026lt;\u0026thinsp;0.25, and NOM.pval\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Establishment and assessment of a nomogram\u003c/h2\u003e \u003cp\u003eTo predict 1-, 3-, 5-, and 10-year DFS in Basal-like breast cancer patients, we utilized the R package \"rms\" to develop a nomogram that assigns points to prognostic factors. In addition, to assess the predictive accuracy of the nomogram, we computed the concordance index (C-index) and generated calibration curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eTo predict variations in drug sensitivity to distinct chemotherapeutics between the high- and low-MIR4435-2HG groups, we employed the \u0026ldquo;pRRophetic\u0026rdquo; R package based on tumor expression profiles. The drug sensitivity was determined by the half-maximal inhibitory concentration (IC50). Additionally, we employed the TIDE algorithm to forecast the potential for tumor immune evasion and response to immune checkpoint blockade therapy in both groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Tumor microenvironment analysis\u003c/h2\u003e \u003cp\u003eMultiple algorithms, including CIBERSORT, TIMER, xCELL, CIBERSORT-ABS, quanTIseq, EPIC, MCPcounter as well as TIDE were used to analyze the disparities in the levels of immune and stromal cell infiltration between groups with high- and low-MIR4435-2HG expression. Additionally, Spearman's rank correlation was calculated to assess the relationship between immune or stromal infiltration score and MIR4435-2HG mRNA expression levels in samples of TNBC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Single-cell RNA sequencing analysis\u003c/h2\u003e \u003cp\u003eWe analyzed MIR4435-2HG expression in one TNBC tissue using the GEO dataset (GSE188600)[12] with the R package \u0026ldquo;Seurat\u0026rdquo;. Cells expressing\u0026thinsp;\u0026le;\u0026thinsp;200 genes were excluded from the analysis. Additionally, cells that contained\u0026thinsp;\u0026le;\u0026thinsp;1,000 unique molecular identifiers (UMIs), \u0026ge; 3% hemoglobin counts as well as mitochondrial reads counts\u0026thinsp;\u0026ge;\u0026thinsp;20% were also removed. We normalized the data using a global-scaling normalization method (LogNormalize) with the scaling factor value set to 10,000. We employed the FindVariableGenes module to identify 2,000 highly variable genes. We utilized Uniform manifold approximation and projection (UMAP) to perform data dimensionality reduction. We then annotated the cell types according to the expression of marker genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Cell culture and transient transfection experiments\u003c/h2\u003e \u003cp\u003eThe cell lines utilized in this study were acquired from the Stem Cell Bank, Chinese Academy of Sciences (Shanghai, China). MCF-10A cells were cultured in a medium composed of DMEM/F12 supplemented with 5% HS, 20ng/mL EGF, 10\u0026micro;g/mL Insulin, 0.5\u0026micro;g/mL Hydrocortisone, 1% NEAA, 1% Penicillin and Streptomycin (P/S); We cultured MDA-MB-231 cells in DMEM/F12 supplemented with 10% fetal bovine serum (FBS; BI, USA) and 1% P/S; MDA-MB-468, MCF-7, and SK-BR3 cells were cultured in DMEM supplemented with 10% FBS and 1% P/S; BT-474 cells were cultured in 1640 supplemented with 10% FBS and 1% P/S. These cells were incubated in a humidified environment containing at 37\u0026deg;C with 5% CO2. Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) was utilized for transient cell transfection experiments. All the experiments were implemented 24 hours after transfection. The sequences for the siRNAs were as follows (siMIR1, forward, 5\u0026rsquo;-CAACCUUAAUGAACUGUAUTT-3\u0026rsquo;, and reverse, 5\u0026rsquo;-AUACAGUUCAUUAAGGUUGTT-3\u0026rsquo;; siMIR2, forward, 5\u0026rsquo;-CCCAGAUUUAAGGGCUAUUTT-3\u0026rsquo;, and reverse, 5\u0026rsquo;-AAUAGCCCUUAAAUCUGGGTT-3\u0026rsquo;) and siRNA negative control (si-NC, forward, 5\u0026rsquo;-UUCUCCGAACGUGUCACGUdTdT-3\u0026rsquo;, and reverse, 5\u0026rsquo;-ACGUGACACGUUCGGAGAAdTdT-3\u0026rsquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 RNA isolation and quantificational real-time polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eWe performed standard TRIzol-based RNA isolation in the cell lines mentioned above. The synthesis of cDNA was accomplished using the Reverse Transcription Kit (XinBei, catalog no.R202-02). The SYBR qPCR Mix (Q204-01) was used for the qRT-PCR in Quantstudio 7 flex (ABI, USA). The following primers were used: ACTB forward, 5\u0026rsquo;- CACCATTGGCAATGAGCGGTTC-3\u0026rsquo;, and reverse, 5\u0026rsquo;-AGGTCTTTGCGGATGTCCACGT-3\u0026rsquo;; MIR4435-2HG forward, 5\u0026rsquo;- TGACATTCCAGACAAGCGGTG-3\u0026rsquo;, and reverse, 5\u0026rsquo;- GGAAAAGATGCTGGTGACTGC-3\u0026rsquo;; CDH1 forward, 5\u0026rsquo;- CCCAATACATCTCCCTTCACAG-3\u0026rsquo;, and reverse, 5\u0026rsquo;- CCACCTCTAAGGCCATCTTTG-3\u0026rsquo;; CDH2 forward, 5\u0026rsquo;- CCTCCAGAGTTTACTGCCATGAC-3\u0026rsquo;, and reverse, 5\u0026rsquo;- GTAGGATCTCCGCCACTGATTC-3\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Western blotting\u003c/h2\u003e \u003cp\u003eWe extracted the cell lysates by utilizing RIPA buffer (Epizyme, Shanghai, China). The lysates were loaded onto 4\u0026ndash;12% Bis-Tris Super PAGE precast gels (Epizyme, catalog no.LK308) and then transferred to PVDF membranes (0.45 \u0026micro;m, Millipore, MA), which were subsequently blocked by NcmBlot Blocking Buffer (NCM biotech, Suzhou, China). Then the membranes were incubated overnight with specific primary antibodies at 4\u0026deg;C. Subsequently, a one-hour incubation at room temperature with proper secondary antibody was performed. Finally, the membranes were subjected to imaging. The antibodies utilized in the current study are as follows: anti-p-JNK(Thr183/Tyr185)(#4668), anti-p-Erk1/2 (Thr202/Tyr204)(#4370), anti-p-p38 (Thr180/Tyr182)(#9211), anti-E-Cadherin (#3195), and anti-p-AKT (Ser473)(#4060) from Cell Signaling Technology; anti-p-JUN (Ser73), anti-Alpha Tubulin (66031-1-Ig), and anti-GAPDH (60004-1-Ig) from Proteintech (Wuhan, China); anti-p-GSK3β (Ser9)(#AF2016) from Affinity (Cincinnati, OH, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Cell proliferation assays\u003c/h2\u003e \u003cp\u003eWe seeded the cells (with 3000 cells for MDA-MB-231 and 6000 cells for MDA-MB-468) in a 96-well plate (100 \u0026micro;l/well). Each sample was set up with five replicate wells and incubated in a CO2 incubator for 24 hours at 37\u0026deg;C with 5% CO2. The culture medium was aspirated from each well, and a solution was prepared by combining the CCK-8 reagent with serum-free culture medium (1:10). Then, a volume of 110 \u0026micro;l of the prepared solution was added to each well and then we incubated the plate incubated at 37\u0026deg;C for 3\u0026ndash;4 hours. Finally, we measured the absorbance at a wavelength of 450 nm using the microplate reader, with 600 nm as the reference wavelength.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Transwell assays\u003c/h2\u003e \u003cp\u003eAfter transfection for 24 hours, cells (3*10^4 for MDA-MB-231 migration; 6*10^4 for MDA-MB-231 invasion; 10*10^4 for MDA-MB-468 migration; 6*10^4 for MDA-MB-468 invasion) with serum-free medium were added to the transwell (8\u0026micro;m pore size, Corning) upper chamber. For invasion assays, the upper surfaces were coated with 50 \u0026micro;l Matrigel, which was diluted at the ratio of 1:8 with serum-free medium. The coated chambers were then allowed to solidify at 37\u0026deg;C for 3\u0026ndash;4 hours. The inserts were placed into wells containing serum medium (20% FBS). 48 hours later, cells in the upper compartment were scraped off and those migrated to the lower surface were fixed with methanol and subsequently stained with 0.1% crystal violet for 15 minutes each step.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Wound healing assays\u003c/h2\u003e \u003cp\u003eMDA-MB-231 and MDA-MB-468 cells were seeded in 12-well plates. 24 hours after plating, cells reached more than 95% confluence. Then the scratches were created using 200 \u0026micro;l pipette tips, and the cells were subsequently washed three times with PBS to eliminate any floating cells. The medium was then replaced by the fresh serum-free medium. We captured the images after 0, 24, and 48 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 RNA Sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from MDA-MB-231 cells using TRIzol (Thermo Fisher, 15596018) after 36-hour-transfection with either siNC or siMIR2. The total RNA was quality controlled using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), and the integrity of the RNA was examined by Bioanalyzer 2100 (Agilent, CA, USA). For downstream cDNA library construction, a RIN value greater than 7.0, a concentration of greater than 50 ng/\u0026micro;L, and a minimum amount of 1\u0026micro;g of total RNA were deemed sufficient. High-throughput sequencing was performed using Illumina NovaseqTM 6000 according to the standard operation with the sequencing mode as PE150.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe analyzed the data using R software for Mac (version 2022.07.1) and Prism 8 (version 8.3.1). The Wilcoxon test was utilized to compare the gene expression levels and the immune or stromal cell infiltration scores between two independent groups. The survival analysis was performed via the Kaplan\u0026ndash;Meier method and log-rank tests. Additionally, the chi-square test was employed to examine the correlation between MIR4435-2HG expression level and patients\u0026rsquo; clinicopathological factors. Either Spearman\u0026rsquo;s rank correlation or Pearson correlation was used for correlation analyses. Statistical significance in this study was defined as follows: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e3.1 Identification of MIR4435-2HG as a diagnostic and prognostic factor for breast cancer\u003c/h2\u003e\n \u003cp\u003eWe employed the Xiantao platform to examine the mRNA expression levels of MIR4435-2HG of tumor tissues compared to corresponding normal tissues within the TCGA database. As shown in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eA, the mRNA expression of MIR4435-2HG was significantly increased in various cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). Furthermore, MIR4435-2HG showed elevated expression in three PAM50 subtypes of breast cancer, particularly for the Basal-like subtype (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eB). Utilizing the TNMplot tool, we observed a gradual increase in the expression of MIR4435-2HG in normal breast tissues, breast cancer tissues, and metastatic breast cancer tissues (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eC). Subsequently, we performed survival analysis using patients\u0026rsquo; information from the TCGA database and found that high expression of MIR4435-2HG was associated with poorer overall survival (OS) and DFS in breast cancer patients as a whole (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eD, E). Although in the Basal-like subgroup, high expression of MIR4435-2HG did not exhibit a significant association with OS, it was significantly linked to shorter DFS (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eF, G). Additionally, we collected and integrated clinicopathological information of breast cancer patients and conducted chi-square tests and Wilcoxon tests according to the expression level of MIR4435-2HG. These analyses revealed significant differences in clinicopathological factors, including T stage, N stage, age, and PAM50 subtypes between high- and low-MIR4435-2HG groups (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Then we performed univariate Cox regression analysis using DFS as the outcome event and included factors with p-values less than 0.1 in further multivariate Cox regression analysis. The results in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e showed that the univariate hazard ratio (HR) for high expression of MIR4435-2HG in the overall breast cancer patients was 1.97 (95% CI 1.0-3.7, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), and the multivariate HR was 2.07 (95% CI 1.1\u0026ndash;3.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). For Basal-like breast cancer patients, the univariate and multivariate HRs for high expression of MIR4435-2HG were 4.65 (95% CI 1.0-21.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) and 9.24 (95% CI 1.1\u0026ndash;78.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). The ROC analysis demonstrated that the expression of MIR4435-2HG had good diagnostic accuracy for different subtypes of breast cancer. The area under the curve (AUC) for Luminal, HER2+, and Basal-like breast cancer patients were up to 0.855, 0.933, and 0.862, respectively (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003eH, I, J).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline patients characteristics(n\u0026thinsp;=\u0026thinsp;1097).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow expression of MIR4435-2HG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh expression of MIR4435-2HG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT stage, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN stage, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (6.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM stage, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologic stage, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (30.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;=60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAM50, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChisq.test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLumA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e287 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLumB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHer2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (50, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (47, 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162179.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWilcoxon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate and multivariate Cox regression analysis among breast patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTCGA All Samples\u003c/p\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTCGA PAM50_Basal_like\u003c/p\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u0026thinsp;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;\u0026thinsp;65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003cp\u003e(0.5\u0026ndash;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.1\u0026ndash;5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI-II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII-IV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003cp\u003e(1.7\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e༜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003cp\u003e(1.0-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.37 (3.9\u0026ndash;46.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e༜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003cp\u003e(1.4\u0026ndash;30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026ndash;2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026ndash;4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003cp\u003e(1.4\u0026ndash;5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003cp\u003e(0.7\u0026ndash;3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003cp\u003e(2.2\u0026ndash;24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003cp\u003e(0.8\u0026ndash;29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026ndash;3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(0.7\u0026ndash;2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003cp\u003e(0.7\u0026ndash;7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36\u003c/p\u003e\n \u003cp\u003e(2.9\u0026ndash;30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e༜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003cp\u003e(1.8\u0026ndash;21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80\u003c/p\u003e\n \u003cp\u003e(0.8\u0026ndash;55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003cp\u003e(0.1\u0026ndash;57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIR4435-2HG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003cp\u003e(1.0-3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003cp\u003e(1.1\u0026ndash;3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.65\u003c/p\u003e\n \u003cp\u003e(1.0-21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.24\u003c/p\u003e\n \u003cp\u003e(1.1\u0026ndash;78.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e3.2 Functional annotation of MIR4435-2HG in Basal-like breast cancer\u003c/h2\u003e\n \u003cp\u003eThrough Spearman correlation analysis, we identified the genes that exhibited a significant positive correlation with MIR4435-2HG in Basal-like breast cancer tissues, with the criteria of |Correlation Coefficient| \u0026gt;0.3 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The functional annotation results based on GO and KEGG are presented in Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eA. Additionally, we performed differential gene expression analysis between high- and low-MIR4435-2HG Basal-like breast cancer patients. We identified the differentially expressed genes (|logFC|\u0026gt;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to conduct GO and KEGG analysis (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eB). The significantly enriched pathways included \u0026ldquo;ECM\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction\u0026rdquo;, \u0026ldquo;extracellular structure organization\u0026rdquo; and other related pathways. Moreover, GSEA revealed significant associations between MIR4435-2HG and pathways such as \u0026ldquo;Epithelial Mesenchymal Transition\u0026rdquo;, \u0026ldquo;Hypoxia\u0026rdquo;, \u0026ldquo;Breast Cancer Ductal Invasive UP\u0026rdquo;, and \u0026ldquo;EMT Breast Tumor up\u0026rdquo;, which are known to be characteristic of Basal-like breast cancer (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003eC, D).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\"\u003e\n \u003ch2\u003e3.3 Construction and assessment of a nomogram as well as drug sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eA nomogram was constructed to predict the prognosis of Basal-like breast cancer patients incorporating key prognostic factors, including age, stage, and expression of MIR4435-2HG. This nomogram assigns a point to each subgroup based on these indicators, allowing us to calculate the total points for each patient and predict their 1-, 3-, 5-, and 10-year DFS. For example, a 51-year-old patient diagnosed with TNBC and clinical stage II, with MIR4435-2HG log2(TPM\u0026thinsp;+\u0026thinsp;1) expression of 4.01, accumulated a total score of 127 points. Based on the nomogram, this patient was estimated to have a 1-, 3-, 5-, and 10-year DFS of 96.17%, 81%, 67.4%, and 57.5%, respectively (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eA). The nomogram\u0026apos;s predictive performance was assessed using the C-index, which was computed to be 0.811 (95% CI 0.685\u0026ndash;0.937). Furthermore, calibration curves were constructed to examine the calibration or accuracy of the nomogram in predicting 1-, 3-, and 5-year DFS (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eB-D). The disparities in chemosensitivity between the high- and low-MIR4435-2HG groups were assessed by calculating the variance in IC50 values. Patients in the high-MIR4435-2HG expression level group demonstrated reduced chemosensitivity to cisplatin, doxorubicin, and gemcitabine while exhibiting enhanced chemosensitivity to paclitaxel (Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eE-H). What\u0026rsquo;s more, patients exhibiting high expression levels of MIR4435-2HG may have a greater potential for tumor immune evasion, which suggests that they might be less likely to benefit from the immune checkpoint inhibitors, such as anti-PD1/CTLA-4 therapies (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.114)(Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003eI, J).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\"\u003e\n \u003ch2\u003e3.4 Correlation between MIR4435-2HG expression and immune or stromal cell infiltration\u003c/h2\u003e\n \u003cp\u003eTumor microenvironment infiltration scoring results calculated by the CIBERSORT algorithm revealed a significantly lower proportion of activated CD4\u0026thinsp;+\u0026thinsp;memory T cells and CD8\u0026thinsp;+\u0026thinsp;T Cells in high-MIR4435-2HG Basal-like breast cancer patients (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eA). Additionally, we employed multiple algorithms to calculate the correlation coefficient between MIR4435-2HG expression and the abundance of other tumor microenvironment cells. The results demonstrated a negative correlation between the MIR4435-2HG expression level and infiltration levels of certain immune cells, including CD8\u0026thinsp;+\u0026thinsp;T cells and CD4\u0026thinsp;+\u0026thinsp;T cells, while a significant positive correlation with stromal cells, i.e. cancer-associated fibroblasts (CAFs)(Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eB, \u003cstrong\u003eSupplementary Fig.\u0026nbsp;1\u003c/strong\u003e). The immune scores exhibited no significant disparity between the high and low expression groups of MIR4435-2HG (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eC), whereas the stromal scores demonstrated a noticeably elevated level higher in the high MIR4435-2HG expression group in comparison to the low MIR4435-2HG expression group (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eD). With the TIDE algorithm, the average expression levels of CD8A and CD8B were found to be decreased in the high-MIR4435-2HG group (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eE), and MIR4435-2HG expression demonstrated strong relevance to FAP\u0026thinsp;+\u0026thinsp;CAF signature (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eF). Moreover, the MIR4435-2HG expression was associated with the expression of CAFs\u0026rsquo; biomarkers (\u003cem\u003eACTA2\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, \u003cem\u003eFAP\u003c/em\u003e, \u003cem\u003eVIM\u003c/em\u003e) in Basal-like breast cancer tissues (Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003eG-J). These findings suggest that MIR4435-2HG may potentially influence breast cancer by reshaping the tumor microenvironment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e3.5 Expression of MIR4435-2HG within the tumor microenvironment analyzed by single-cell sequencing data\u003c/h2\u003e\n \u003cp\u003eAfter quality control (see Methods) of the single-cell sequencing data from one TNBC sample (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eA), we utilized principal component analysis (PCA) to reduce the data dimensionality (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eB). For visualization, we further reduced the single-cell expression data to two dimensions using UMAP. A total of 11 cell clusters were identified, and finally, we annotated 8 major cell types according to gene expression patterns within these clusters (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eC). Interestingly, MIR4435-2HG was detected not only in tumor epithelial cells but also in various other cell types within the tumor microenvironment (Fig.\u0026nbsp;\u003cspan\u003e5\u003c/span\u003eD, E). This suggests that MIR4435-2HG may have a broader impact beyond tumor cells alone, potentially influencing various components of the tumor microenvironment in TNBC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e3.6 MIR4435-2HG enhances migration and invasion of TNBC cells via EMT in vitro\u003c/h2\u003e\n \u003cp\u003eThrough qRT-PCR experiments, we noted a remarkable decrease in the expression level of MIR4435-2HG was significantly lower in luminal-type breast cancer cell lines (MCF-7, BT-474) in comparison to normal breast epithelial cell line (MCF-10A) but showed no significant difference in HER2-positive breast cancer cell line (SK-BR3). In contrast, the MIR4435-2HG expression was significantly elevated in TNBC cell lines (MDA-MB-231, MDA-MB-468) (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eA). We chose these two TNBC cell lines for transient siRNA transfection and found that the knockdown efficiency was higher with the second set of siRNA (MIR4435-2HG si2, siMIR2), reaching 75% in MDA-MB-231 and 80% in MDA-MB-468 (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eB). We performed CCK-8 assays using MDA-MB-231 and MDA-MB-468 cells transiently transfected with siNC and siMIR2. The findings revealed that MIR4435-2HG knockdown did not significantly affect the proliferation of MDA-MB-231 cells while exhibiting a notable inhibitory effect on the proliferation of MDA-MB-468 cells (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eThus, we focused on investigating the influence of MIR4435-2HG on the migratory capacity of TNBC cells. We performed transwell migration and invasion (Matrigel-coated) assays and found that MDA-MB-231 and MDA-MB-468 cells transfected with siMIR2 displayed significantly reduced migration and invasion abilities (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eC). Besides, we performed wound healing assays in both cell lines. At 48 hours after creating the wounds, we observed significantly larger wound areas in the siMIR2 group as compared to the siNC group, indicating a weakened cell migration ability (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eD). Furthermore, we examined the expression of key genes (CDH1 and CDH2) in the EMT process. Knockdown of MIR4435-2HG in both cell lines caused a reduction in the mRNA level of CDH2 (a mesenchymal marker) by PCR, and an increase in the mRNA and protein levels of CDH1 (an epithelial marker) as detected by PCR, RNA-seq, and western blot analysis (Fig.\u0026nbsp;\u003cspan\u003e6\u003c/span\u003eE, F).\u003c/p\u003e\n \u003cp\u003eBased on these findings, it can be inferred that MIR4435-2HG enhances the migration and invasion abilities of TNBC cells by orchestrating the EMT program in vitro.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e3.7 MIR4435-2HG activates the JNK/c-Jun and p38 MAPK pathway in TNBC cells\u003c/h2\u003e\n \u003cp\u003eTo explore the mechanisms through which MIR4435-2HG contributes to the development of TNBC, we performed transcriptome RNA sequencing analysis. The differentially expressed genes were visualized in a heatmap (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eA) and a volcano plot (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eB). As shown in Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eC, from the PPI network analysis based on these differentially expressed genes, IL6, CASP3, and MAPK8 were identified as the top three hub genes. Further analysis using KEGG and GSEA unveiled a significant enrichment of the MAPK signaling pathway (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eD, E). The KEGG pathways to which the genes were annotated were summarized and fill-in colors were used to locate nodes in the pathway where the differential genes were located (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;3\u003c/strong\u003e). Besides, we filtered out the downregulated genes (logFC\u0026lt;-1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in MDA-MB-231 cells with MIR4435-2HG knockdown and performed KEGG analysis (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eF). As expected, the MAPK signaling pathway exhibited the highest level of enrichment. To validate the involvement of the MAPK signaling pathway, we performed western blot analyses. The results indicated a significant reduction in the levels of p-JNK, p-JUN, and p-p38, while no obvious change was identified in the level of p-ERK1/2 in MIR4435-2HG-knockdown TNBC cells when compared to the control group, suggesting that MIR4435-2HG might specifically activate the JNK/c-Jun and p38 MAPK pathway rather than the classical MAPK pathway (Fig.\u0026nbsp;\u003cspan\u003e7\u003c/span\u003eG).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTNBC is widely recognized as a highly aggressive and heterogeneous subtype of breast cancer. Although TNBC is known to be highly responsive to standard chemotherapy regimens, the lack of targeted adjuvant therapies has been linked to early relapse and poor OS in TNBC patients[13]. Distant metastasis is estimated to occur in approximately 46% of TNBC patients, with a 40% mortality rate within 5 years of diagnosis[14]. Hence, identification of targetable TNBC-associated molecules and elucidation of the mechanisms underlying their contribution to the high metastatic capacity of TNBC are crucial for improving treatment efficacy and patient survival.\u003c/p\u003e \u003cp\u003eLncRNA MIR4435-2HG, which locates on human chromosome 2q13, spans a total length of 517,362 base pairs and was identified as a lncRNA associated with gastric cancer initially[15]. MIR4435-2HG was elevated in cervical cancer and MIR4435-2HG knockdown inhibited the proliferation and metastasis abilities of cervical cancer through the miR-128-3p/MSI2 axis[16]. In hepatocellular carcinoma, the upregulation of MIR4435-2HG has been found to enhance cancer cell growth and invasion, exerting a significant impact on the OS of liver cancer patients[17, 18]. Moreover, MIR4435-2HG was also reported to play oncogenic roles in oral squamous cell carcinoma[19], lung cancer[20], prostate cancer[21], ovarian cancer[22], and glioblastoma[23]. However, in colorectal cancer, MIR4435-2HG exhibits a tumor suppressor function by reprogramming neutrophils[24].\u003c/p\u003e \u003cp\u003ePrevious studies have provided evidence that MIR4435-2HG can either promote the progression of ER-positive breast cancer through the miR-22-3p/TMEM9B pathway[25] or promote breast cancer via the Wnt/β-catenin signaling pathway[26]. However, it is worth noting that the primers used in these studies, the former covering one transcript of MIR4435-2HG and one transcript of CYTOR (a highly homologous RNA to MIR4435-2HG), and the latter covering three transcripts of MIR4435-2HG. Both studies detected high expression of MIR4435-2HG in ER-positive breast cancer cell lines (ZR-75-30 and MCF7) in comparison to MCF-10A. In our study, we used primers that covered six out of the eight transcripts of MIR4435-2HG. Through qRT-PCR analysis using RNA extracted from different batches of cells, we observed significantly lower expression of MIR4435-2HG in luminal subtype cell lines (MCF7 and BT-474) when compared to MCF-10A. Conversely, we found significantly higher expression of MIR4435-2HG in TNBC cell lines (MDA-MB-231 and MDA-MB-468). Based on the close relationship between MIR4435-2HG expression and clinicopathological information of Basal-like breast cancer patients, we specifically chose TNBC cell lines for the exploration of the role of MIR4435-2HG in TNBC. Furthermore, using the Blast tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we compared the siRNA sequence (5'-CCCAGAUUUAAGGGCUAUUTT-3') used in Chen et al.'s study mentioned above and found that it targeted the NR_015395.2 transcript, while the primers utilized in their study (Forward, 5'-AGGCCCGGAATCTTTCA-3' and reverse, 5'-GCCTCTCCCTGAATAACTGGG-3') detected the NR_024373.2 transcript. Similarly, in Ke et al.'s study, the shRNA (5'-GGTCTGGTCGGTTTCCCATTT-3') targeted five transcripts of CYTOR and one transcript of MIR4435-2HG (NR_024373.2), while the primers used (forward, 5'-CGGAGCATGGAACTCGACA-3', and reverse, 5'-CAAGTCTCACACATCCGGG-3') did not include NR_024373.2. Therefore, it is possible that the findings from these studies might not accurately reflect the expression of MIR4435-2HG in breast cancer cell lines, and the observed phenotypes may be confounded by changes in the expression levels of highly homologous RNA, CYTOR. It is important to note that although MIR4435-2HG and CYTOR are highly homologous, this does not necessarily mean that they have identical functions. Genes can undergo evolutionary changes, leading to alterations in their expression patterns and functions. The siRNA used in our study specifically targeted the six transcripts of MIR4435-2HG covered by the primers we used and did not affect the expression level of CYTOR[24]. This allowed for a more accurate validation of the functional role of MIR4435-2HG in our experimental setting.\u003c/p\u003e \u003cp\u003eIn the current study, we observed that MIR4435-2HG may promote metastasis of TNBC through the non-classical MAPK signaling pathway. This finding adds to the existing knowledge about the mechanisms through which MIR4435-2HG contributes to tumor progression, such as the TGF-β1 signaling pathway, Wnt/β‑catenin signaling pathway, and competing endogenous RNA (ceRNA) network. Furthermore, using bioinformatics analysis, we proposed that MIR4355-2HG could serve as an effective prognostic and diagnostic factor for TNBC. Elevated expression of MIR4435-2HG correlated with lower sensitivities to cisplatin, doxorubicin, and gemcitabine while showing higher sensitivity to paclitaxel. These findings have the potential to guide personalized clinical therapeutics in TNBC. Through tumor microenvironment infiltration analysis, we observed a higher level of infiltration of stromal cells in the high-MIR4435-2HG group. MIR4435-2HG expression exhibited a positive correlation with CAFs while a negative correlation with CD8\u0026thinsp;+\u0026thinsp;T cells. This led us to propose a potential mechanism by which MIR4435-2HG may remodel the tumor microenvironment of TNBC by promoting CAFs-mediated CD8\u0026thinsp;+\u0026thinsp;T-cell exclusion.\u003c/p\u003e"},{"header":"Conclusions and limitations","content":"\u003cp\u003eOur study elucidates for the first time that MIR4435-2HG serves as a promising diagnostic and prognostic biomarker of TNBC and it promotes the migration and invasion abilities of TNBC cells by driving the EMT process through the activation of JNK/c-Jun and p38 MAPK signaling pathway, suggesting that targeting MIR4435-2HG could be an appealing therapeutic strategy for the prevention of TNBC metastasis.\u003c/p\u003e \u003cp\u003eHowever, it is important to note that our current experiments focused solely on TNBC cell lines. To gain a deeper understanding of the complex functions of MIR4435-2HG in TNBC and uncover new treatment methods, additional studies involving diverse experimental models and clinical samples are warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTNBC: Triple-negative breast cancer; LncRNA: Long non-coding RNA; TCGA: The Cancer Genome Atlas; ROC: Receiver operating characteristic; GSEA: Gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DFS: Disease-free survival; TIDE: Tumor Immune Dysfunction and Exclusion; EMT: Epithelial\u0026ndash;mesenchymal transition; ER: Estrogen receptor; PR: Progesterone receptor; HER2: Human epidermal growth; MAPK: Mitogen-Activated Protein Kinase; \u0026nbsp;PPI: protein\u0026ndash;protein interaction; C-index: Concordance index; IC50: Half-maximal inhibitory concentration; NCBI: National Center for Biotechnology Information; UMIs: Unique molecular identifiers; UMAP: Uniform manifold approximation and projection; P/S: Penicillin and streptomycin; FBS: Fetal bovine serum; qRT-PCR: quantificational real-time polymerase chain reaction; BLCA: Bladder urothelial carcinoma; BRCA: Breast invasive carcinoma; CHOL: Cholangiocarcinoma; COAD: Colon adenocarcinoma; ESCA: Esophageal carcinoma; HNSC: Head and neck squamous cell carcinoma; KIRC: Kidney renal clear cell carcinoma; KIRP: Kidney renal papillary cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma; READ: Rectum adenocarcinoma; STAD Stomach adenocarcinoma; THCA: Thyroid carcinoma; UCEC: Uterine corpus endometrial carcinoma; OS: Overall survival; HR: Hazard ratio; AUC: Area under the curve; CAFs: Cancer-associated fibroblasts; ceRNA: Competing endogenous RNA.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe acknowledge contributors for uploading their meaningful datasets to the public platforms.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The datasets supporting the conclusions of this study are available in the TCGA repository (https://cancergenome.nih.gov/), the GEO websites (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse188600), and the Molecular Signatures Database V7.5.1 (https://www.gsea-msigdb.org/gsea/msigdb/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of China [grant number 82172907].\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eGP, YB, and SX conceived and designed the study. GP, DWT, and ZWT performed the experiments. GP, WWH, and WYH acquired and analyzed the data. GP prepared the figures and tables and drafted the manuscript. SL, ZL, WW, and WRT revised the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17-48.\u003c/li\u003e\n\u003cli\u003eLehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750-67.\u003c/li\u003e\n\u003cli\u003eTan YT, Lin JF, Li T, Li JJ, Xu RH, Ju HQ. LncRNA-mediated posttranslational modifications and reprogramming of energy metabolism in cancer. Cancer Commun (Lond). 2021;41(2):109-20.\u003c/li\u003e\n\u003cli\u003eYang F, Zhang H, Mei Y, Wu M. Reciprocal regulation of HIF-1\u0026alpha; and lincRNA-p21 modulates the Warburg effect. Mol Cell. 2014;53(1):88-100.\u003c/li\u003e\n\u003cli\u003eGupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010;464(7291):1071-6.\u003c/li\u003e\n\u003cli\u003eMaldonado V, Melendez-Zajgla J. The Role of Hypoxia-Associated Long Non-Coding RNAs in Breast Cancer. Cells. 2022;11(10).\u003c/li\u003e\n\u003cli\u003eGu P, Zhang L, Wang R, Ding W, Wang W, Liu Y, et al. Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer. Front Cell Dev Biol. 2021;9:796729.\u003c/li\u003e\n\u003cli\u003eAnjum J, Mitra S, Das R, Alam R, Mojumder A, Emran TB, et al. A renewed concept on the MAPK signaling pathway in cancers: Polyphenols as a choice of therapeutics. Pharmacol Res. 2022;184:106398.\u003c/li\u003e\n\u003cli\u003eMaharati A, Moghbeli M. Long non-coding RNAs as the critical regulators of PI3K/AKT, TGF-\u0026beta;, and MAPK signaling pathways during breast tumor progression. J Transl Med. 2023;21(1):556.\u003c/li\u003e\n\u003cli\u003eChen S, Wang Y, Zhang JH, Xia QJ, Sun Q, Li ZK, et al. Long non-coding RNA PTENP1 inhibits proliferation and migration of breast cancer cells via AKT and MAPK signaling pathways. Oncol Lett. 2017;14(4):4659-62.\u003c/li\u003e\n\u003cli\u003eBartha A, Gyorffy B. TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues. Int J Mol Sci. 2021;22(5).\u003c/li\u003e\n\u003cli\u003ePullikuth AK, Routh ED, Zimmerman KD, Chifman J, Chou JW, Soike MH, et al. Bulk and Single-Cell Profiling of Breast Tumors Identifies TREM-1 as a Dominant Immune Suppressive Marker Associated With Poor Outcomes. Front Oncol. 2021;11:734959.\u003c/li\u003e\n\u003cli\u003eMirzania M. Approach to the Triple Negative Breast Cancer in New Drugs Area. Int J Hematol Oncol Stem Cell Res. 2016;10(2):115-9.\u003c/li\u003e\n\u003cli\u003eYin L, Duan JJ, Bian XW, Yu SC. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 2020;22(1):61.\u003c/li\u003e\n\u003cli\u003eKe D, Li H, Zhang Y, An Y, Fu H, Fang X, Zheng X. The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer. Oncotarget. 2017;8(13):21516-25.\u003c/li\u003e\n\u003cli\u003eWang R, Liu L, Jiao J, Gao D. Knockdown of MIR4435-2HG Suppresses the Proliferation, Migration and Invasion of Cervical Cancer Cells via Regulating the miR-128-3p/MSI2 Axis in vitro. Cancer Manag Res. 2020;12:8745-56.\u003c/li\u003e\n\u003cli\u003eBai Y, Lin H, Chen J, Wu Y, Yu S. Identification of Prognostic Glycolysis-Related lncRNA Signature in Tumor Immune Microenvironment of Hepatocellular Carcinoma. Front Mol Biosci. 2021;8:645084.\u003c/li\u003e\n\u003cli\u003eShen X, Ding Y, Lu F, Yuan H, Luan W. Long noncoding RNA MIR4435-2HG promotes hepatocellular carcinoma proliferation and metastasis through the miR-22-3p/YWHAZ axis. Am J Transl Res. 2020;12(10):6381-94.\u003c/li\u003e\n\u003cli\u003eShen H, Sun B, Yang Y, Cai X, Bi L, Deng L, Zhang L. MIR4435-2HG regulates cancer cell behaviors in oral squamous cell carcinoma cell growth by upregulating TGF-beta1. Odontology. 2020;108(4):553-9.\u003c/li\u003e\n\u003cli\u003eQian H, Chen L, Huang J, Wang X, Ma S, Cui F, et al. The lncRNA MIR4435-2HG promotes lung cancer progression by activating beta-catenin signalling. Journal of molecular medicine. 2018;96(8):753-64.\u003c/li\u003e\n\u003cli\u003eXing P, Wang Y, Zhang L, Ma C, Lu J. Knockdown of lncRNA MIR4435‑2HG and ST8SIA1 expression inhibits the proliferation, invasion and migration of prostate cancer cells in vitro and in vivo by blocking the activation of the FAK/AKT/beta‑catenin signaling pathway. Int J Mol Med. 2021;47(6).\u003c/li\u003e\n\u003cli\u003eLiu S, Qiao Z, Ma Q, Liu X, Ma X. LncRNA CYTOR and MIR4435-2HG in ovarian cancer and its relationship with clinicopathological features. Panminerva Med. 2022;64(1):119-20.\u003c/li\u003e\n\u003cli\u003eXu H, Zhang B, Yang Y, Li Z, Zhao P, Wu W, et al. LncRNA MIR4435-2HG potentiates the proliferation and invasion of glioblastoma cells via modulating miR-1224-5p/TGFBR2 axis. J Cell Mol Med. 2020;24(11):6362-72.\u003c/li\u003e\n\u003cli\u003eYu H, Chen C, Han F, Tang J, Deng M, Niu Y, et al. Long Noncoding RNA MIR4435-2HG Suppresses Colorectal Cancer Initiation and Progression By Reprogramming Neutrophils. Cancer Immunol Res. 2022;10(9):1095-110.\u003c/li\u003e\n\u003cli\u003eKe J, Wang Q, Zhang W, Ni S, Mei H. LncRNA MIR4435-2HG promotes proliferation, migration, invasion and epithelial mesenchymal transition via targeting miR-22-3p/TMEM9B in breast cancer. Am J Transl Res. 2022;14(8):5441-54.\u003c/li\u003e\n\u003cli\u003eChen D, Tang P, Wang Y, Wan F, Long J, Zhou J, et al. Downregulation of long non-coding RNA MR4435-2HG suppresses breast cancer progression via the Wnt/beta-catenin signaling pathway. Oncol Lett. 2021;21(5):373.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figures","content":"\u003cp\u003eSupplementary Figures are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"MIR4435-2HG, Triple-negative breast cancer, Biomarker, MAPK signaling pathway, Tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-3832143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3832143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer has the highest incidence rate and causes the most fatalities among all female cancers worldwide. Triple-negative breast cancer (TNBC) is known for its strong invasiveness and higher rates of recurrence. In this research, we aimed to identify MIR4435-2HG as a promising long non-coding RNA (lncRNA) biomarker and therapeutic target for TNBC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing clinicopathological information and transcriptome data from The Cancer Genome Atlas (TCGA) database, we assessed the clinical relevance of MIR4435-2HG in breast cancer through univariate and multivariate COX regression, receiver operating characteristic (ROC) analysis, as well as Kaplan-Meier survival analysis. To investigate the biological role of MIR4435-2HG in TNBC, we conducted gene set enrichment analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Additionally, we constructed and validated a nomogram to predict disease-free survival (DFS). Both the R package \u0026ldquo;pRRophetic\u0026rdquo; and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were employed to forecast the sensitivity to different therapeutics between the high- and low-MIR4435-2HG groups. We employed single-cell RNA sequencing analysis and tumor microenvironment infiltration analysis to investigate the potential involvement of MIR4435-2HG in the TNBC tumor microenvironment. Cellular biological behaviors were assessed utilizing CCK-8, transwell assays, and wound-healing assays. Furthermore, we performed RNA-seq, qRT-PCR, and western blotting analyses to elucidate and confirm the specific mechanisms underlying MIR4435-2HG-mediated TNBC progression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn our study, we have identified MIR4435-2HG as a significant diagnostic and prognostic factor for TNBC. We observed that MIR4435-2HG is widely expressed and might have a significant impact on the reshaping of the TNBC tumor microenvironment. Patients with breast cancer in the high-MIR4435-2HG group may show reduced sensitivity to cisplatin, doxorubicin, and gemcitabine and have an increased propensity for immune escape. Notably, MIR4435-2HG predominantly enhances the migratory and invasive capabilities of TNBC cells through the epithelial-mesenchymal transition (EMT) process. Mechanistically, we validated that MIR4435-2HG activates the JNK/c-Jun and p38 MAPK signaling pathway in TNBC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the significant potential of MIR4435-2HG as a highly promising biomarker for TNBC. Targeting MIR4435-2HG could represent an appealing therapeutic approach to suppress TNBC metastasis.\u003c/p\u003e","manuscriptTitle":"MIR4435-2HG: A Novel Biomarker for Triple-Negative Breast Cancer Diagnosis and Prognosis, Driving Tumor Progression through EMT by JNK/c-Jun and p38 MAPK Signaling Pathway Activation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-16 21:50:45","doi":"10.21203/rs.3.rs-3832143/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":"df9f7dc8-f99b-4bdf-84e1-ad91e42cad26","owner":[],"postedDate":"January 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T03:25:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-16 21:50:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3832143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3832143","identity":"rs-3832143","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}