LSM4 drives the progression of triple-negative breast cancer through alternative splicing

LSM4 drives the progression of triple-negative breast cancer through alternative splicing | 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 Article LSM4 drives the progression of triple-negative breast cancer through alternative splicing Xin Hu, Javaria Nasir, Yunxiao Ling, Bo-Yue Han, Hong Ling This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9275393/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 Triple-negative breast cancer (TNBC) remains a therapeutic challenge due to its aggressive nature and limited treatment options. Through integrated genomic and proteomic profiling, we identified the RNA-binding protein LSM4 as a key driver of TNBC progression. LSM4 was significantly upregulated in TNBC tissues and showed progressively increasing expression from normal breast epithelial cells to highly aggressive cancer models. Functional genomics using CRISPR screening nominated LSM4 as a functionally essential spliceosomal gene among nine critical candidates. Genetic suppression of LSM4 in TNBC cell lines (MDA-MB-231 and CA1a) markedly inhibited malignant phenotypes including proliferation, migration, invasion, and colony formation. RNA-sequencing analysis demonstrated that LSM4 knockdown alters 1,593 alternative splicing events, predominantly through exon skipping, and disrupts genes involved in key cancer pathways including RNA degradation. We established an LSM4-associated gene signature that correlated with an immunosuppressive microenvironment characterized by altered expression of immune markers and checkpoint molecules. Clinical validation using the FUSCC cohort revealed that this signature strongly predicted poor overall survival (p = 0.00067), recurrence-free survival (p = 0.05), and distant metastasis-free survival (p = 0.0092). Our findings establish LSM4 as an important splicing regulator in TNBC, linking spliceosomal dysfunction to tumor progression and immune evasion, and nominate LSM4 as a promising prognostic biomarker and therapeutic target. Biological sciences/Cancer/Breast cancer Health sciences/Biomarkers/Prognostic markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer remains a significant global health burden, accounting for a substantial proportion of cancer diagnoses and mortality in women worldwide [ 1 ]. Within this landscape, triple-negative breast cancer (TNBC) represents a particularly aggressive and clinically challenging subtype, characterized by the absence of estrogen receptor, progesterone receptor, and HER2 amplification. This molecular profile renders TNBC patients ineligible for targeted hormonal and anti-HER2 therapies, leaving conventional chemotherapy as the primary treatment option. Consequently, TNBC is associated with a higher relapse rate, a greater propensity for visceral metastasis, and a significantly poorer prognosis, especially in advanced stages where the 5-year survival rate plummets [ 2 ]. The limited therapeutic arsenal and heterogeneous nature of TNBC underscore the urgent need to elucidate its distinct molecular drivers and identify novel, effective therapeutic targets. Alternative splicing is a fundamental post-transcriptional mechanism that dramatically expands the coding capacity of the human genome, allowing a single gene to generate multiple mRNA and protein isoforms with diverse or even opposing functions [ 3 ]. It is estimated that over 95% of human multi-exon genes undergo alternative splicing, highlighting its critical role in regulating cellular processes such as differentiation, proliferation, and apoptosis [ 4 ]. In cancer, the splicing landscape is frequently and profoundly dysregulated. Cancer cells exploit this mechanism to produce specific protein variants that enhance their survival, promote uncontrolled proliferation, drive invasion and metastasis, and confer resistance to therapy [ 5 , 6 ]. These aberrant splicing events affect key oncogenic pathways and are increasingly recognized as a hallmark of cancer, making the spliceosome and its regulators a promising new frontier for therapeutic intervention [ 7 – 9 ]. Among the complex machinery governing RNA metabolism, the Like Sm (LSM) family of proteins plays an evolutionarily conserved and crucial role. These proteins form ring-shaped complexes that are integral to various aspects of RNA biology, including splicing, decay, and modification [ 10 ]. LSM4 has emerged as a protein of particular interest due to its unique dual functionality. It is a core component of two distinct complexes: the nuclear U6 snRNP, where it is essential for pre-mRNA splicing, and the cytoplasmic LSM1-7 complex, which is involved in mRNA decapping and degradation [ 11 ]. This positions LSM4 at the nexus of RNA fate determination. Notably, genomic studies have revealed that LSM4 exhibits a significant alteration frequency (approximately 9%) in breast cancer, with a pattern of upregulation and amplification, suggesting a selective advantage for cancer cells [ 10 , 11 ]. Its central role in RNA processing and cancer-associated dysregulation nominates LSM4 as a potential master regulator of post-transcriptional gene expression in tumorigenesis. In this study, we establish LSM4 as a pivotal driver of TNBC progression and metastasis. Mechanistically, LSM4 governs a global alternative splicing program, predominantly through exon skipping, which reshapes the transcriptome to activate pro-tumorigenic pathways. Functionally, silencing LSM4 potently suppresses TNBC cell proliferation, migration, invasion, and clonogenicity. Clinically, high LSM4 expression correlates with an immunosuppressive microenvironment and predicts poor patient survival. Our findings unveil LSM4 as a functionally important splicing regulator in TNBC and nominate it as a promising therapeutic target for this aggressive disease. Materials and Methods Cell Lines MCF10 series cell lines (MCF10A, MCF10AT, MCF10DCIS, and MCF10CA1a) and MDA-MB-231 cells were purchased from the Cell Bank of Chinese Academy of Sciences. HEK-293T cells were obtained from ATCC (American Type Culture Collection). All cell lines were authenticated by STR profiling to verify their identity. MCF10 series cells were cultured in DMEM/F12 medium (Gibco) supplemented with 5% horse serum, 10 µg/mL insulin, 0.5 µg/mL hydrocortisone (Sigma), 100 ng/mL cholera toxin (Sigma), and 20 ng/mL epidermal growth factor (PeproTech). MDA-MB-231 cells were maintained in high-glucose DMEM (Basal Media) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin-streptomycin. All cells were cultured at 37°C in a humidified atmosphere with 5% CO₂. RNA Extraction and Reverse Transcription Protocol RNA Extraction and Reverse Transcription Protocol Total RNA was extracted from cultured cells using TRIzol reagent (Invitrogen). 3 × 10⁶ cells were harvested at 90% confluence, washed with PBS, and lysed with 500 µL lysis buffer. Following manufacturer's instructions, 500 µL ethanol was added and mixed thoroughly. The lysate was transferred to RNA binding columns and centrifuged at 12,000 × g for 2 minutes. RNA was washed with 500 µL wash buffer, centrifuged at 12,000 × g for 1 minute, and eluted with 20–50 µL elution buffer by centrifugation at 12,000 × g for 1 minute. RNA concentration and purity were determined by spectrophotometry (NanoDrop/specify instrument). High-quality RNA samples exhibited OD₂₆₀/₂₈₀ ratios between 1.9–2.2. RNA was stored at -80°C or kept on ice for immediate use. For reverse transcription, 1–2 µg total RNA was treated with 4 µL 4× gDNA Wiper Mix (specify manufacturer) in a total volume of 16 µL with nuclease-free water. After incubation at 42°C for 2 minutes, 4 µL HiScript III RT SuperMix (specify manufacturer) was added. Reverse transcription was performed using the following program: 37°C for 15 minutes, followed by 85°C for 5 minutes. The resulting cDNA was diluted 1:10 with nuclease-free water for subsequent quantitative PCR analysis. Quantification PCR Protocol For quantitative PCR analysis, reaction mixtures were prepared containing 0.4 µL each of forward and reverse primers (10 µM), 10 µL of SYBR Green Master Mix, 2 µL of diluted cDNA template, and nuclease-free water to achieve a final volume of 20 µL. The threshold cycle (Ct) values were recorded for each sample, and relative gene expression was analyzed using the ΔΔCt method. Breast Cancer Tissue Microarray Cohort Acquisition Breast cancer tissue microarray cohort data was obtained from patients who had undergone surgery at our center since 2000, randomly sampling a total of 360 patients from Luminal A, Luminal B, HER2-overexpressing, and TNBC/Basal subtypes. Basal and TNBC subtypes were classified as one type, whereas Luminal A, Luminal B, and HER 2-overexpressing were classified as ‘Others’. Exclusion criteria were: (1) non-invasive cancer; (2) not primary surgery; (3) recurrence or metastasis present; (4) neoadjuvant chemotherapy or other treatments before this surgery. Tissue microarray chips (TMA) of breast cancer patients were prepared with assistance from our center's pathology department. The use of patient specimens and follow-up data was approved by the FUSCC Ethics Committee. Patients had informed consent rights and signed consent forms. Construction of Gene Knockout Cell Lines For lentivirus packaging, the knockout system was prepared by combining 5.856 µg of target plasmid, 4.389 µg of psPAX2 packaging plasmid, and 1.755 µg of pMD2.G packaging plasmid in 600 µL of PBS, followed by thorough mixing through 30 pipetting cycles and a 5-minute incubation period. Subsequently, 36 µL of PEI was added to this mixture, mixed thoroughly by 30 pipetting cycles, and allowed to stand for 15 minutes to form transfection complexes. HEK-293T cells cultured to 80–90% confluence had their medium changed before the addition of the transfection mixture, and the medium was replaced again after 8–12 hours. The viral supernatant was collected 48 hours post-transfection and filtered through a 0.45 µm membrane to remove cellular debris prior to use for cell infection. For cell infection, target cells were cultured to approximately 50% confluence to ensure optimal infection conditions. The packaged lentivirus was mixed with fresh culture medium at a 1:1 ratio, supplemented with Polybrene to a final concentration of 10 µg/mL to enhance viral infection efficiency, and this mixture was added to the target cells. After 48 hours of infection, the cells were resuspended in medium containing puromycin at a final concentration of 1 µg/mL to select for successfully infected cells that had integrated the antibiotic resistance gene along with the target construct. CCK-8 Proliferation Assay For CCK-8 proliferation assays, cells were diluted to 1×10^4 cells/mL and plated at 100 µL per well in 96-well plates with three replicate wells established for each experimental group. The cells were cultured for 6 days, with Cell Counting Kit-8 (CCK-8) reagent added daily followed by incubation periods of 30, 60, and 90 minutes before measuring absorbance at 450 nm using a plate reader to assess cell viability and proliferation rates. Colony Formation Assay Colony formation assays were performed by plating 1×10^3 cells per well in 6-well plates with three replicate wells per group. The cells were cultured for 2 weeks with medium changes every 3–4 days to maintain optimal growth conditions. At the end of the culture period, the cells were fixed with methanol and stained for photographic documentation and quantitative analysis of colony formation capacity. Transwell Migration and Invasion Assay For Transwell migration assays, 1×10^5 MCF10 CA1a cells or 5×10^4 MDA-MB-231 cells were suspended in 200 µL of serum-free medium and added to Transwell chambers, with three replicate wells established per experiment. The lower chambers were filled with 600 µL of medium containing 20% serum to create a chemotactic gradient, and the chambers were placed in the incubator. After the incubation period, non-migrated cells remaining on the inner chamber wall were removed using cotton swabs, and the migrated cells were fixed with methanol, stained, and photographed for analysis. Transwell invasion assays followed a similar protocol but with additional preparation steps involving coating the Transwell chambers with 100 µL of Matrigel diluted 1:8 in serum-free medium, followed by incubation at 37°C for 4–6 hours to allow solidification. Subsequently, 2×10^5 MCF10 CA1a cells or 1×10^5 MDA-MB-231 cells were suspended in 200 µL of serum-free medium and added to the Matrigel-coated upper chambers, with three replicate wells per experiment. The lower chambers contained 600 µL of medium with 20% serum, and after incubation, non-invaded cells and residual Matrigel on the inner chamber wall were removed with cotton swabs. The invaded cells were then fixed with methanol for 15 minutes, stained with 0.1% crystal violet for 15 minutes, and photographed under a microscope for quantitative analysis of invasive capacity. RNA-Seq Total RNA was collected from LSM4 knockdown cells and normal control cells using MCF10 CA1a cell lines for RNA sequencing analysis. The quality of extracted RNA was assessed using the Agilent 2100 Bioanalyzer to ensure sample integrity prior to library preparation. RNA library construction and sequencing services were performed by Novogene company following standard protocols for high-throughput sequencing. Alternative splicing analysis was conducted using MISO (Mixture of Isoforms) software to identify differential splicing events between experimental conditions. Differential expression gene analysis was performed using DESeq2 (version 1.6.3) within the Bioconductor software package to identify significantly up- and down-regulated genes. Gene Ontology (GO) functional enrichment analysis was carried out using the DAVID (Database for Annotation, Visualization and Integrated Discovery) database to determine the biological processes, molecular functions, and cellular components associated with differentially expressed genes. Clinical Samples Breast cancer specimens and adjacent normal tissues were acquired from breast cancer patients who underwent surgery at FUSCC. All experiments involving humans were conducted on the basis of the Declaration of Helsinki. Before the patients were enrolled in the experiment, informed consent was provided, and the study was permitted by the Independent Ethical Committee/Institutional Review Board of Fudan University Shanghai Cancer Center. Statistical Analysis Methods Follow-up time is defined as the date when a patient was first diagnosed with breast cancer at our center until breast cancer recurrence, death, or last follow-up. Overall survival (OS) is defined as the time from the date when a patient was first diagnosed with breast cancer at our center until patient death from any cause. Recurrence-free survival (RFS) is defined as the time from the date when a patient was first diagnosed with breast cancer at our center until breast cancer recurrence. This paper used Graphpad Prism 10.0 software, R studio, and IBM SPSS Statistics Version 29.0.1.0 software for data processing and plotting. The T-test was used for continuous variables analysis, and the chi-square test was used for categorical variables analysis. Results LSM4 is Identified as a Upregulated Spliceosomal Gene in TNBC Through Integrated Omics Analysis Our comprehensive molecular profiling of TNBC revealed distinct transcriptomic and proteomic characteristics compared to normal breast tissue. Principal Component Analysis (PCA) demonstrated a clear and robust separation between TNBC and normal samples, with the first principal component (PC1) accounting for 51.27% of the total variance, indicating substantial molecular differences (Fig. 1 a). Differential gene expression analysis further identified LSM4 as one of the most significantly upregulated genes in TNBC. Differential expression analysis identified LSM4 as significantly upregulated in TNBC, exhibiting high statistical significance (-log10(p-value) > 75) with a Log2FC of approximately 1.5 (Fig. 1 b). Subsequent pathway enrichment analysis of the differentially expressed genes highlighted a significant overrepresentation of splicing-related pathways, including the spliceosome and Sm/LSm core pathways, suggesting a central role for RNA processing dysregulation in TNBC pathogenesis (Fig. 1 c). A heatmap visualization of spliceosome-associated genes confirmed this widespread dysregulation, demonstrating a consistent pattern of upregulation across multiple LSM family members and SNRP genes in TNBC samples compared to their normal counterparts (Fig. 1 d). Parallel analysis at the protein level confirmed distinct expression patterns of LSM and SNRP family members in TNBC versus normal samples (Fig. 1 e). Box plot analysis specifically demonstrated markedly elevated LSM4 RNA levels in TNBC compared to normal tissue (p < 2e-16) (Fig. 1 f). Notably, LSM4 expression was significantly elevated in basal-type breast cancer compared to other subtypes (p < 0.0056), underscoring its potential subtype-specific relevance (Fig. 1 g). Critically, this transcriptional upregulation was strongly validated at the protein level, where LSM4 exhibited the most pronounced and statistically significant increase among all LSM family members analyzed (p = 0.0000028) (Fig. 1 h). Collectively, these multi-omics data established LSM4 as a prominently and consistently dysregulated component of the spliceosomal network in TNBC. Functional Genomics and Clinical Analysis Validate LSM4 as a Critical Player in TNBC Progression We next employed a functional genomics approach to pinpoint spliceosomal genes essential for breast cancer malignancy. A genome-wide CRISPR screen was conducted in the aggressive MDA-MB-231 and CA1a cell lines to identify genes whose knockout negatively impacted cell fitness (Fig. 2 a). This screen identified 177 spliceosome-associated genes and 91 CA1a negative score genes, with a critical overlap of 9 high-confidence candidates (Fig. 2 b). LSM4 was prioritized among these overlapping hits for further investigation. Validation by quantitative PCR (qPCR) confirmed a progressive increase in LSM4 expression that correlated directly with cellular aggressiveness. Expression was lowest in normal breast epithelial cells (MCF10A), significantly elevated in the MDA-MB-231 cell line (2.5-fold increase, p = 0.0417), and highest in the highly aggressive CA1a cells (approximately 3-fold increase, p = 0.0021) (Fig. 2 c). To assess the clinical relevance of these findings, we analyzed data from the FUSCC cohort. Survival analysis revealed a notable trend towards reduced relapse-free survival (RFS) in patients with high LSM4 expression (p = 0.096) (Fig. 2 d). Cumulative hazard analysis further showed a dramatic increase in risk accumulation within the first 40 months of follow-up specifically for the high LSM4 expression group (Fig. 2 e). Multivariate Cox regression analysis confirmed that established clinicopathological parameters were strong independent prognostic factors, with advanced lymph node status (N3 vs. N0, HR = 7.78, 95% CI: 3.118–17.82) being the most powerful predictor (Fig. 2 f). Together, these results establish LSM4 as a critical driver of breast cancer progression, with its prognostic impact being significant alongside other clinical factors. LSM4 Knockdown Suppresses Malignant Phenotypes in TNBC Cells We established efficient LSM4 knockdown models using both siRNA and shRNA approaches in MDA-MB-231 and CA1a cell lines to investigate its functional role. Molecular analysis confirmed successful suppression across all constructs, with statistically significant reduction in LSM4 expression (p < 0.001). The shRNA construct sh-11 demonstrated particularly potent knockdown efficiency, achieving approximately 95% reduction in LSM4 expression compared to controls (p = 0.0004) in both cell lines (Fig. 3 a-d). Having validated the knockdown efficiency, we examined the impact on cancer cell proliferation using CCK-8 assays. The results revealed that LSM4 suppression significantly inhibited cell growth in a time-dependent manner, with substantial differences emerging after day 3 of treatment. This inhibitory effect was most pronounced at day 5, particularly in CA1a cells where wild-type cells reached an OD 450 of 1.0 while si-11-treated cells showed only 0.2 (Figs. 3 e and f), indicating a crucial role for LSM4 in sustaining TNBC cell proliferation. Beyond proliferation, we investigated the effects of LSM4 knockdown on metastatic capabilities using transwell assays. Migration assays demonstrated that LSM4 suppression profoundly impaired cell motility, reducing migration by up to 80% compared to controls (Fig. 3 g-i). A similar trend was observed in invasion assays, which showed an approximate 50% reduction in the number of invaded cells following siRNA-mediated LSM4 knockdown (Fig. 3 j-l). Most notably, plate clone formation assays revealed that LSM4 depletion substantially compromised long-term survival and clonogenic capacity. The sh-11 construct reduced colony formation by approximately 75% compared to controls, with both MDA-MB-231 (sh-NC = 129.3 vs sh-11 = 34, p = 0.0003) and CA1a (sh-NC = 260 vs sh-11 = 68.3, p < 0.0001) cells showing significant sensitivity to LSM4 knockdown despite their differential baseline colony formation capacities (Fig. 3 m and n). The consistency of these inhibitory effects across multiple functional assays, including proliferation, migration, invasion, and colony formation, firmly establishes LSM4 as a critical regulator of numerous malignant phenotypes in TNBC cells. LSM4 Regulates Global Alternative Splicing and Gene Expression in TNBC To elucidate the mechanistic basis of LSM4's functional impact, we performed comprehensive RNA-seq analysis following LSM4 knockdown. Characterization of alternative splicing events identified a total of 1,593 significant splicing alterations, with exon skipping (SE) being the predominant type (1,060 events, 66.5%), followed by mutually exclusive exons (160, 10.0%), alternative 3' splice sites (A3'SS, 158, 9.9%), intron retention (119, 7.5%), and alternative 5' splice sites (A5'SS, 96, 6.0%) (Fig. 4 a and b). Detailed analysis of specific splicing types revealed widespread differential splicing patterns, with volcano plots for both A3'SS and SE events showing numerous statistically significant alterations (-log10 p-value > 10) across inclusive difference ranges from − 1.5 to 1.5 (Fig. 4 c and d). Parallel examination of transcriptional changes identified 295 differentially expressed genes (124 downregulated and 171 upregulated) following LSM4 suppression (Fig. 4 e). Heatmap visualization highlighted consistent expression patterns across biological replicates, showing marked downregulation of LSM4, EDN2, RASSF6 and TATDN3, alongside upregulation of MAST1, CSAD, and ARHGEF4 (Fig. 4 f). Functional enrichment analysis provided insight into the biological consequences of these changes. Gene Ontology (GO) analysis revealed significant enrichment in crucial cellular components including the presynaptic active zone, nuclear exosome, and CUL3-RING ubiquitin ligase complex, while biological processes showed enrichment in macrophage migration, regulation of peptide secretion, and face development (Fig. 4 g). Molecular function analysis highlighted exoribonuclease activity and G-protein coupled receptor binding. KEGG pathway analysis further identified significant alterations in Toll-like receptor signaling and inflammatory TRP channel regulation (Fig. 4 h). Integrated Analysis Reveals Overlap Between Splicing and Expression Alterations We next integrated the splicing and gene expression data to identify coordinated regulatory mechanisms. Venn diagram analysis revealed complex interactions between different regulatory layers, identifying 10 genes overlapping between differentially expressed genes (DEGs) and exon skipping events, 23 genes between SE and A3'SS events, and 12 genes common to all three categories (DEG, SE, and A3'SS) (Fig. 5 a). GO enrichment analysis of these overlapping gene sets demonstrated significant enrichment in DNA repair and telomere maintenance pathways across biological process, cellular component, and molecular function categories (Fig. 5 b-d). Finally, we validated the RNA-seq findings using quantitative PCR, which confirmed the differential expression of seven key genes: four downregulated (EDN2, NAP1L1, TATDN3, and RASSF6) and three upregulated (MAST1, CSAD, and ARHGEF4) (Fig. 5 e and f). These integrated analyses establish that LSM4 knockdown induces extensive reprogramming of both alternative splicing and gene expression, with coordinated effects on pathways critical for TNBC progression. LSM4-Associated Immune Signature Predicts Clinical Outcomes in TNBC We further investigated the clinical implications of LSM4-associated molecular alterations by analyzing the FUSCC cohort. Classification of patients into Signature (SG) High and Signature (SG) Low expression groups revealed distinct distribution patterns across molecular subtypes using both FUSCC (Basal-Like Immune-Suppressed, Immunomodulatory, Luminal Androgen Receptor, Mesenchymal) and Lehmann classification systems (p < 0.001) (Fig. 6 a). Comparative analysis demonstrated significant differences in clinicopathological features between these groups. The SG High group comprised significantly younger patients (peak age around 50 years) compared to the SG Low group (peak age around 63 years) (p < 0.01), and exhibited markedly higher Ki67 expression levels (peak 80–120 vs 70 in SG Low) (p < 0.001) (Fig. 6 b). Analysis of the tumor immune microenvironment through heatmap visualization revealed distinct expression patterns of immune-related genes, including key markers such as CD8A, CXCL9, CXCL10, GZMB, PRF1, and checkpoint molecules (CD274, PDCD1, PDCD1LG2) (Fig. 6 c). Survival analyses over 50 months demonstrated the significant prognostic power of the LSM4-associated signature. Most strikingly, overall survival (OS) analysis revealed consistently lower survival probability in the SG High group (approximately 0.80) compared to the SG Low group (approximately 0.90) (p = 0.00067) (Fig. 6 d). Similarly, recurrence-free survival (RFS) analysis showed clinically relevant differences between the groups (p = 0.05), though slightly less pronounced than OS (Fig. 6 e). Analysis of distant metastasis-free survival (DMFS) further confirmed significant stratification, with the SG High group maintaining lower survival rates throughout the follow-up period (p = 0.0092) (Fig. 6 f). Notably, all survival curves showed early separation at approximately 10 months and maintained this differential throughout the entire follow-up period, supporting the robust and consistent prognostic value of the LSM4-associated immune signature in predicting patient outcomes across multiple survival endpoints. Discussion Previous investigations into LSM4 have primarily focused on its fundamental roles in RNA processing, particularly as a component of the U6 snRNP complex in nuclear mRNA splicing and the LSM1-7 complex in cytoplasmic mRNA decay [ 11 ]. While genomic studies have noted a distinctive alteration pattern of LSM4 in breast cancer, with approximately 9% frequency [ 10 ], its specific functional significance and mechanistic contributions in triple-negative breast cancer remained largely unexplored. This study provides compelling evidence that establishes LSM4 as a critical oncogenic driver in TNBC through its regulatory role in alternative splicing, with particular implications for tumor immune regulation. Our findings demonstrate that LSM4 expression is significantly elevated in TNBC tissues and progressively increases with cellular aggressiveness. Through comprehensive splicing analysis, we identified that LSM4 regulates 1,593 alternative splicing events, predominantly through exon skipping mechanisms, thereby reshaping the transcriptomic landscape to activate pro-tumorigenic pathways. These results position LSM4 among a select group of splicing factors that serve as critical dependencies in cancer cells [ 12 , 13 ], extending previous observations of spliceosomal dysregulation in TNBC [ 14 ]. The connection between LSM4 and tumor immune regulation represents a particularly significant aspect of our findings. This is particularly relevant given the growing recognition that alternative splicing serves as a key mechanism linking RNA processing to antitumor immunity [ 15 , 16 ]. Our analysis revealed that LSM4-associated splicing events significantly impact immune-related pathways, including Toll-like receptor signaling and inflammatory TRP channel regulation. More importantly, the LSM4-defined gene signature identifies a distinct TNBC subgroup characterized by specific alterations in key immune markers. This signature shows markedly different expression patterns of critical immune-related genes including CD8A, CXCL9, CXCL10, GZMB, PRF1, and various checkpoint molecules (CD274, PDCD1, PDCD1LG2). The coordinated downregulation of T-cell recruiting chemokines (CXCL9, CXCL10) and cytolytic effectors (GZMB, PRF1) in SG High tumors suggests that LSM4 may contribute to establishing an immune-suppressive microenvironment, potentially facilitating immune evasion. This finding aligns with emerging evidence linking alternative splicing to immune regulation in cancer [ 17 ] and provides a mechanistic basis for the aggressive clinical behavior of LSM4-high tumors. The molecular mechanisms underlying LSM4's oncogenic function involve extensive reprogramming of both splicing patterns and gene expression networks. Our integrated analysis revealed that LSM4-dependent splicing alterations converge on pathways crucial for cancer progression, including RNA degradation and DNA repair. This multifaceted impact aligns with LSM4's established dual functionality in RNA processing [ 18 – 21 ] and provides mechanistic insight into how splicing regulators can coordinate complex oncogenic programs. The significant overlap between differentially expressed genes and splicing events further suggests that LSM4 operates within an interconnected regulatory network that simultaneously controls both splicing patterns and expression levels of target genes. The robust prognostic value of the LSM4-associated immune signature across multiple survival endpoints (OS: p = 0.00067; DMFS: p = 0.0092) underscores its clinical relevance for patient stratification. The early separation of survival curves at approximately 10 months and maintained differential throughout follow-up suggests that LSM4-mediated immune alterations have lasting impact on disease progression. This finding gains additional significance given the growing importance of immunotherapy in cancer treatment and the urgent need for reliable biomarkers to guide therapeutic decisions in TNBC. In the context of TNBC treatment challenges, where current therapeutic options remain limited, our functional studies demonstrate that LSM4 suppression profoundly impairs multiple malignant phenotypes. These findings not only provide a mechanistic explanation for the poor prognosis associated with high LSM4 expression but also highlight the therapeutic potential of targeting this splicing regulator. Given the limited targeted therapy options for TNBC, developing strategies to modulate LSM4 activity or its downstream splicing network may offer new therapeutic avenues, particularly in combination with immunotherapeutic approaches. In summary, our study elucidates the oncogenic role of LSM4 in TNBC progression through its function as an important splicing regulator with significant impact on tumor immune microenvironment. These findings not only advance our understanding of splicing dysregulation in TNBC pathogenesis but also identify LSM4 as a potential prognostic biomarker and therapeutic target. Future investigations exploring LSM4-targeted strategies and its specific roles in modulating anti-tumor immunity may yield significant insights for improving TNBC management. Declarations Competing Interests Statement The authors declare no competing financial interests Supplementary Materials All sequences used in this study are provided in Table S1 (S1.1–S1.4). Author Contributions JN, YXL and HL conceived the study design and methodology. JN and YXL planned the experimental design and analyzed the data; JN and BH conducted the experiments and collected most of the data; XH and HL provided materials and clinical specimens; JN and YXL analyzed the final results and wrote the manuscript. All authors read and approved the final manuscript. JN and YXL contributed equally to this work. Acknowledgements We would like to express our sincere gratitude to Professor Zhimin Shao for providing the laboratory facilities and resources that made this research possible. We are also deeply thankful to Wenxiao Yang and Luo Hong for their invaluable support and expertise throughout the course of this study. Their contributions to experimental work and data analysis were instrumental in the successful completion of this research. Data Availability Statement All data is available from the corresponding author upon reasonable request. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024; 74:12–49. 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The LSM1-7 complex differentially regulates arabidopsis tolerance to abiotic stress conditions by promoting selective mRNA decapping. Plant Cell. 2016; 28:505–520. Tao Y, Zhang Q, Wang H, Yang X, Mu H. Alternative splicing and related RNA binding proteins in human health and disease. Signal Transduct Target Ther. 2024; 9:26. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementaryTableS1.xlsx Table S1 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-9275393","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":618851493,"identity":"ff2d62aa-43d6-4b40-8dea-17f6c4526252","order_by":0,"name":"Xin Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACAyA+wGDDLMfG3nyAFC1pzMZ8PMcSiNfCANSSOE8iR4E4LeYSOYaHCxKs09sYchgYflRsI6zFckZawuEZCem5bQxnDzD2nLlNhMNuJB84zPvjcG4bY18CM2MbUVoSGw7zJBxOZ2PmMSBWC9AWoJYENjaitZx5lgDUkm7YxsOWcJA4vxzPMf7Mk2AtLz//8cEHPyqI0IICDpCofhSMglEwCkYBLgAA8Zc78CM2XgsAAAAASUVORK5CYII=","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Hu","suffix":""},{"id":618851494,"identity":"6e658282-3b53-4eba-a410-4011c0dc601c","order_by":1,"name":"Javaria Nasir","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Javaria","middleName":"","lastName":"Nasir","suffix":""},{"id":618851495,"identity":"1ef73e3c-e2f1-476c-b4e9-e99d98416b00","order_by":2,"name":"Yunxiao Ling","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunxiao","middleName":"","lastName":"Ling","suffix":""},{"id":618851496,"identity":"3c1140aa-628b-4b54-9942-c044ae4ce518","order_by":3,"name":"Bo-Yue Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Bo-Yue","middleName":"","lastName":"Han","suffix":""},{"id":618851497,"identity":"89e61d89-81c9-43ce-9174-5ff4ea6d34d1","order_by":4,"name":"Hong Ling","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ling","suffix":""}],"badges":[],"createdAt":"2026-03-31 06:30:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9275393/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9275393/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106407611,"identity":"6838e08c-226a-4dc0-8236-4b816bd7d8d4","added_by":"auto","created_at":"2026-04-08 09:38:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":586716,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrative Analysis of LSM4 and Spliceosome-Associated Gene Expression in Triple-Negative Breast Cancer (a) Principal component analysis demonstrating distinct molecular separation between TNBC and normal tissue samples. (b) Volcano plot of RNA expression highlighting differentially expressed genes with significant upregulation of key spliceosome-associated genes. (c) Volcano Plot of protein expression showing significant enrichment of splicing-related. (d) Heatmap visualization of LSM family and SNRP RNA expression patterns comparing normal and TNBC samples. (e) Heatmap visualization of LSM family and SNRP protein expression patterns comparing normal and TNBC samples. (f) Box plot demonstrating relative LSM4 RNA level in TNBC compared to normal tissue (p \u0026lt; 2e-16). (g) Comparison of LSM4 expression levels between basal (n=263) and other breast cancer subtypes (n=97) from the Fudan University Shanghai Cancer Center cohort (p = 0.0056). (h) Box plot analysis showing relative protein expression patterns of LSM family and SNRP family members between normal and TNBC samples with corresponding P-values.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/50317f6887805f3ef482af89.png"},{"id":106407678,"identity":"7e36d049-1a2e-442e-a6fc-b818c61d7e99","added_by":"auto","created_at":"2026-04-08 09:38:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":372728,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Validation of LSM4 through CRISPR Screening and Clinical Impact in Breast Cancer Progression (a) Workflow of genome-wide CRISPR screening approach in MDA-MB-231 and CA1a cell lines, showing experimental design from sgRNA library construction through assessment of primary tumor formation and metastatic potential. (b) Venn diagram illustrating the overlap between spliceosome-associated genes (186) and CA1a negative score genes (100 (c) Quantitative PCR analysis demonstrating LSM4 expression in MCF10A, MDA-MB-231 and CA1a. (d) Kaplan-Meier analysis from the Fudan University Shanghai Cancer Center cohort to show relapse-free survival (RFS) relative to LSM4 expression (p = 0.096). (e) Cumulative hazard plot showing increased risk in different LSM4 expression groups over 140 months of follow-up (p=0.096). (f) Multivariate analysis of clinical parameters showing hazard ratios and 95% confidence intervals. (*, p\u0026lt;0.05,**, p\u0026lt;0.01; ***, p\u0026lt;0.001, ****, p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/236c4b4cfb564d32bdfb3b0c.png"},{"id":106407626,"identity":"b4919385-ee43-46c5-ac90-54c119e0903d","added_by":"auto","created_at":"2026-04-08 09:38:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":744735,"visible":true,"origin":"","legend":"\u003cp\u003eLSM4 Knockdown Suppresses Breast Cancer Cell Growth, Migration, and Colony Formation (a) LSM4 expression levels in MDA-MB-231 cells following siRNA transfection. (b) LSM4 expression levels in CA1a cells following siRNA transfection. (c) LSM4 expression levels in MDA-MB-231 cells following stable shRNA transduction. (d) LSM4 expression levels in CA1a cells following stable shRNA transduction. (e) CCK8 cell proliferation assay of MDA-MB-231 cells over 5 days.(f) CCK8 cell proliferation assay of CA1a cells over 5 days. (g) Quantification of MDA-MB-231 cell invasion in transwell invasion assay. (h) Quantification of CA1a cell invasion in transwell invasion assay. (i) Representative images of transwell migration assay for MDA-MB-231 and CA1a cells. (j) Quantification of MDA-MB-231 cell migration in transwell migration assay. (k) Quantification of CA1a cell migration in transwell migration assay. (l) Representative images of transwell migration assay for MDA-MB-231 and CA1a cells. (m) Quantification of colony formation in MDA-MB-231 cells and representative images below graph. (n) Quantification of colony formation in CA1a cells and representative images below graph. (*, p\u0026lt;0.05,**, p\u0026lt;0.01; ***, p\u0026lt;0.001, ****, p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/02bb38e505cbf2bfae555d9e.png"},{"id":106407573,"identity":"7631a54a-b100-4747-b260-b00759e0f176","added_by":"auto","created_at":"2026-04-08 09:38:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":497472,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive Analysis of Alternative Splicing Events and Gene Expression Changes Following LSM4 Knockdown (a) Alternative splicing event classification showing number of splicing events for each type. (b) Pie chart showing distribution of 1,593 total splicing events. (c) Volcano plot and pathway enrichment analysis of Exon Skipping (SE) events. (d) Volcano plot and pathway enrichment analysis of Alternative 3' Splice Site events. (e) Volcano plot showing differential gene expression after LSM4 knockdown. (f) Heatmap visualization of key differentially expressed genes. (g) GO enrichment analysis showing center: Biological Process analysis (upper left); Molecular Function analysis (upper right); Cellular Component analysis(lower left). (h) KEGG Analysis showing gene ratio for multiple pathways.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/d556596730ac46753ee31080.png"},{"id":106407676,"identity":"f4e9b5df-f024-44bf-a6c2-d4bdb01cfd7b","added_by":"auto","created_at":"2026-04-08 09:38:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":335534,"visible":true,"origin":"","legend":"\u003cp\u003eOverlap Analysis, Functional Enrichment, and qPCR Validation of LSM4 Knockdown Effects (a) Venn diagram showing overlap between differentially expressed genes, Exon Skipping events and Alternative 3' Splice Site events.(b) GO enrichment analysis showing Biological Process analysis. (c) GO enrichment analysis showing Cellular Component analysis. (d) GO enrichment analysis showing Molecular Function analysis. (e) qPCR results showing downregulation in 4 genes. (f) qPCR analysis showing upregulation in 3 genes. (*, p\u0026lt;0.05,**, p\u0026lt;0.01; ***, p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/9f7eed7a9b6f4791104cfa34.png"},{"id":106407479,"identity":"f7e002d3-0096-4b65-8113-c6c3c8efd2c5","added_by":"auto","created_at":"2026-04-08 09:37:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113095,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Classification and Prognostic Analysis of SG High and SG Low Expression Groups (a) Distribution analysis of molecular subtypes according to FUSCC (upper panel) and Lehmann (lower panel) (b) Graph showing differences between SG High and SG low groups; top: Age at surgery; middle: HRD expression; and bottom: Ki67 expression distribution between groups. (c) Immune-related gene expression heatmap showing differential expression patterns of immune markers and checkpoint molecules across samples. (d) Kaplan-Meier survival curve over 50 months showing Overall survival (p=0.00067). (e) Kaplan-Meier survival curve over 50 months showing Recurrence free survival (p=0.05). (f) Kaplan-Meier survival curve over 50 months showing Distant metastasis-free survival (p=0.0092). (**, p \u0026lt; 0.01; ***, p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"Binder16.png","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/d671c8162ef629e2689bb3fc.png"},{"id":106415347,"identity":"86f42c39-720e-4821-8a19-61b500190769","added_by":"auto","created_at":"2026-04-08 10:33:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3190026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/80e9a622-8226-4e09-82f9-6c1803f8b6f4.pdf"},{"id":106407629,"identity":"0cfcae00-eb2a-4f34-9a76-5cb21e26e546","added_by":"auto","created_at":"2026-04-08 09:38:28","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12397,"visible":true,"origin":"","legend":"Table S1","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9275393/v1/697e31a3a1a351867167c1cc.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"LSM4 drives the progression of triple-negative breast cancer through alternative splicing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer remains a significant global health burden, accounting for a substantial proportion of cancer diagnoses and mortality in women worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Within this landscape, triple-negative breast cancer (TNBC) represents a particularly aggressive and clinically challenging subtype, characterized by the absence of estrogen receptor, progesterone receptor, and HER2 amplification. This molecular profile renders TNBC patients ineligible for targeted hormonal and anti-HER2 therapies, leaving conventional chemotherapy as the primary treatment option. Consequently, TNBC is associated with a higher relapse rate, a greater propensity for visceral metastasis, and a significantly poorer prognosis, especially in advanced stages where the 5-year survival rate plummets [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The limited therapeutic arsenal and heterogeneous nature of TNBC underscore the urgent need to elucidate its distinct molecular drivers and identify novel, effective therapeutic targets.\u003c/p\u003e \u003cp\u003eAlternative splicing is a fundamental post-transcriptional mechanism that dramatically expands the coding capacity of the human genome, allowing a single gene to generate multiple mRNA and protein isoforms with diverse or even opposing functions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is estimated that over 95% of human multi-exon genes undergo alternative splicing, highlighting its critical role in regulating cellular processes such as differentiation, proliferation, and apoptosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In cancer, the splicing landscape is frequently and profoundly dysregulated. Cancer cells exploit this mechanism to produce specific protein variants that enhance their survival, promote uncontrolled proliferation, drive invasion and metastasis, and confer resistance to therapy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These aberrant splicing events affect key oncogenic pathways and are increasingly recognized as a hallmark of cancer, making the spliceosome and its regulators a promising new frontier for therapeutic intervention [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the complex machinery governing RNA metabolism, the Like Sm (LSM) family of proteins plays an evolutionarily conserved and crucial role. These proteins form ring-shaped complexes that are integral to various aspects of RNA biology, including splicing, decay, and modification [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. LSM4 has emerged as a protein of particular interest due to its unique dual functionality. It is a core component of two distinct complexes: the nuclear U6 snRNP, where it is essential for pre-mRNA splicing, and the cytoplasmic LSM1-7 complex, which is involved in mRNA decapping and degradation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This positions LSM4 at the nexus of RNA fate determination. Notably, genomic studies have revealed that LSM4 exhibits a significant alteration frequency (approximately 9%) in breast cancer, with a pattern of upregulation and amplification, suggesting a selective advantage for cancer cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its central role in RNA processing and cancer-associated dysregulation nominates LSM4 as a potential master regulator of post-transcriptional gene expression in tumorigenesis.\u003c/p\u003e \u003cp\u003eIn this study, we establish LSM4 as a pivotal driver of TNBC progression and metastasis. Mechanistically, LSM4 governs a global alternative splicing program, predominantly through exon skipping, which reshapes the transcriptome to activate pro-tumorigenic pathways. Functionally, silencing LSM4 potently suppresses TNBC cell proliferation, migration, invasion, and clonogenicity. Clinically, high LSM4 expression correlates with an immunosuppressive microenvironment and predicts poor patient survival. Our findings unveil LSM4 as a functionally important splicing regulator in TNBC and nominate it as a promising therapeutic target for this aggressive disease.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell Lines\u003c/h2\u003e \u003cp\u003eMCF10 series cell lines (MCF10A, MCF10AT, MCF10DCIS, and MCF10CA1a) and MDA-MB-231 cells were purchased from the Cell Bank of Chinese Academy of Sciences. HEK-293T cells were obtained from ATCC (American Type Culture Collection). All cell lines were authenticated by STR profiling to verify their identity. MCF10 series cells were cultured in DMEM/F12 medium (Gibco) supplemented with 5% horse serum, 10 \u0026micro;g/mL insulin, 0.5 \u0026micro;g/mL hydrocortisone (Sigma), 100 ng/mL cholera toxin (Sigma), and 20 ng/mL epidermal growth factor (PeproTech). MDA-MB-231 cells were maintained in high-glucose DMEM (Basal Media) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin-streptomycin. All cells were cultured at 37\u0026deg;C in a humidified atmosphere with 5% CO₂.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA Extraction and Reverse Transcription Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eRNA Extraction and Reverse Transcription Protocol\u003c/div\u003e \u003cp\u003eTotal RNA was extracted from cultured cells using TRIzol reagent (Invitrogen). 3 \u0026times; 10⁶ cells were harvested at 90% confluence, washed with PBS, and lysed with 500 \u0026micro;L lysis buffer. Following manufacturer's instructions, 500 \u0026micro;L ethanol was added and mixed thoroughly. The lysate was transferred to RNA binding columns and centrifuged at 12,000 \u0026times; g for 2 minutes. RNA was washed with 500 \u0026micro;L wash buffer, centrifuged at 12,000 \u0026times; g for 1 minute, and eluted with 20\u0026ndash;50 \u0026micro;L elution buffer by centrifugation at 12,000 \u0026times; g for 1 minute. RNA concentration and purity were determined by spectrophotometry (NanoDrop/specify instrument). High-quality RNA samples exhibited OD₂₆₀/₂₈₀ ratios between 1.9\u0026ndash;2.2. RNA was stored at -80\u0026deg;C or kept on ice for immediate use. For reverse transcription, 1\u0026ndash;2 \u0026micro;g total RNA was treated with 4 \u0026micro;L 4\u0026times; gDNA Wiper Mix (specify manufacturer) in a total volume of 16 \u0026micro;L with nuclease-free water. After incubation at 42\u0026deg;C for 2 minutes, 4 \u0026micro;L HiScript III RT SuperMix (specify manufacturer) was added. Reverse transcription was performed using the following program: 37\u0026deg;C for 15 minutes, followed by 85\u0026deg;C for 5 minutes. The resulting cDNA was diluted 1:10 with nuclease-free water for subsequent quantitative PCR analysis.\u003c/p\u003e\n\u003ch3\u003eQuantification PCR Protocol\u003c/h3\u003e\n\u003cp\u003eFor quantitative PCR analysis, reaction mixtures were prepared containing 0.4 \u0026micro;L each of forward and reverse primers (10 \u0026micro;M), 10 \u0026micro;L of SYBR Green Master Mix, 2 \u0026micro;L of diluted cDNA template, and nuclease-free water to achieve a final volume of 20 \u0026micro;L. The threshold cycle (Ct) values were recorded for each sample, and relative gene expression was analyzed using the ΔΔCt method.\u003c/p\u003e\n\u003ch3\u003eBreast Cancer Tissue Microarray Cohort Acquisition\u003c/h3\u003e\n\u003cp\u003eBreast cancer tissue microarray cohort data was obtained from patients who had undergone surgery at our center since 2000, randomly sampling a total of 360 patients from Luminal A, Luminal B, HER2-overexpressing, and TNBC/Basal subtypes. Basal and TNBC subtypes were classified as one type, whereas Luminal A, Luminal B, and HER 2-overexpressing were classified as \u0026lsquo;Others\u0026rsquo;. Exclusion criteria were: (1) non-invasive cancer; (2) not primary surgery; (3) recurrence or metastasis present; (4) neoadjuvant chemotherapy or other treatments before this surgery. Tissue microarray chips (TMA) of breast cancer patients were prepared with assistance from our center's pathology department. The use of patient specimens and follow-up data was approved by the FUSCC Ethics Committee. Patients had informed consent rights and signed consent forms.\u003c/p\u003e\n\u003ch3\u003eConstruction of Gene Knockout Cell Lines\u003c/h3\u003e\n\u003cp\u003eFor lentivirus packaging, the knockout system was prepared by combining 5.856 \u0026micro;g of target plasmid, 4.389 \u0026micro;g of psPAX2 packaging plasmid, and 1.755 \u0026micro;g of pMD2.G packaging plasmid in 600 \u0026micro;L of PBS, followed by thorough mixing through 30 pipetting cycles and a 5-minute incubation period. Subsequently, 36 \u0026micro;L of PEI was added to this mixture, mixed thoroughly by 30 pipetting cycles, and allowed to stand for 15 minutes to form transfection complexes. HEK-293T cells cultured to 80\u0026ndash;90% confluence had their medium changed before the addition of the transfection mixture, and the medium was replaced again after 8\u0026ndash;12 hours. The viral supernatant was collected 48 hours post-transfection and filtered through a 0.45 \u0026micro;m membrane to remove cellular debris prior to use for cell infection. For cell infection, target cells were cultured to approximately 50% confluence to ensure optimal infection conditions. The packaged lentivirus was mixed with fresh culture medium at a 1:1 ratio, supplemented with Polybrene to a final concentration of 10 \u0026micro;g/mL to enhance viral infection efficiency, and this mixture was added to the target cells. After 48 hours of infection, the cells were resuspended in medium containing puromycin at a final concentration of 1 \u0026micro;g/mL to select for successfully infected cells that had integrated the antibiotic resistance gene along with the target construct.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCCK-8 Proliferation Assay\u003c/h2\u003e \u003cp\u003eFor CCK-8 proliferation assays, cells were diluted to 1\u0026times;10^4 cells/mL and plated at 100 \u0026micro;L per well in 96-well plates with three replicate wells established for each experimental group. The cells were cultured for 6 days, with Cell Counting Kit-8 (CCK-8) reagent added daily followed by incubation periods of 30, 60, and 90 minutes before measuring absorbance at 450 nm using a plate reader to assess cell viability and proliferation rates.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eColony Formation Assay\u003c/h3\u003e\n\u003cp\u003eColony formation assays were performed by plating 1\u0026times;10^3 cells per well in 6-well plates with three replicate wells per group. The cells were cultured for 2 weeks with medium changes every 3\u0026ndash;4 days to maintain optimal growth conditions. At the end of the culture period, the cells were fixed with methanol and stained for photographic documentation and quantitative analysis of colony formation capacity.\u003c/p\u003e\n\u003ch3\u003eTranswell Migration and Invasion Assay\u003c/h3\u003e\n\u003cp\u003eFor Transwell migration assays, 1\u0026times;10^5 MCF10 CA1a cells or 5\u0026times;10^4 MDA-MB-231 cells were suspended in 200 \u0026micro;L of serum-free medium and added to Transwell chambers, with three replicate wells established per experiment. The lower chambers were filled with 600 \u0026micro;L of medium containing 20% serum to create a chemotactic gradient, and the chambers were placed in the incubator. After the incubation period, non-migrated cells remaining on the inner chamber wall were removed using cotton swabs, and the migrated cells were fixed with methanol, stained, and photographed for analysis. Transwell invasion assays followed a similar protocol but with additional preparation steps involving coating the Transwell chambers with 100 \u0026micro;L of Matrigel diluted 1:8 in serum-free medium, followed by incubation at 37\u0026deg;C for 4\u0026ndash;6 hours to allow solidification. Subsequently, 2\u0026times;10^5 MCF10 CA1a cells or 1\u0026times;10^5 MDA-MB-231 cells were suspended in 200 \u0026micro;L of serum-free medium and added to the Matrigel-coated upper chambers, with three replicate wells per experiment. The lower chambers contained 600 \u0026micro;L of medium with 20% serum, and after incubation, non-invaded cells and residual Matrigel on the inner chamber wall were removed with cotton swabs. The invaded cells were then fixed with methanol for 15 minutes, stained with 0.1% crystal violet for 15 minutes, and photographed under a microscope for quantitative analysis of invasive capacity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA-Seq\u003c/h2\u003e \u003cp\u003eTotal RNA was collected from LSM4 knockdown cells and normal control cells using MCF10 CA1a cell lines for RNA sequencing analysis. The quality of extracted RNA was assessed using the Agilent 2100 Bioanalyzer to ensure sample integrity prior to library preparation. RNA library construction and sequencing services were performed by Novogene company following standard protocols for high-throughput sequencing. Alternative splicing analysis was conducted using MISO (Mixture of Isoforms) software to identify differential splicing events between experimental conditions. Differential expression gene analysis was performed using DESeq2 (version 1.6.3) within the Bioconductor software package to identify significantly up- and down-regulated genes. Gene Ontology (GO) functional enrichment analysis was carried out using the DAVID (Database for Annotation, Visualization and Integrated Discovery) database to determine the biological processes, molecular functions, and cellular components associated with differentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Samples\u003c/h2\u003e \u003cp\u003eBreast cancer specimens and adjacent normal tissues were acquired from breast cancer patients who underwent surgery at FUSCC. All experiments involving humans were conducted on the basis of the Declaration of Helsinki. Before the patients were enrolled in the experiment, informed consent was provided, and the study was permitted by the Independent Ethical Committee/Institutional Review Board of Fudan University Shanghai Cancer Center.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis Methods\u003c/h2\u003e \u003cp\u003eFollow-up time is defined as the date when a patient was first diagnosed with breast cancer at our center until breast cancer recurrence, death, or last follow-up. Overall survival (OS) is defined as the time from the date when a patient was first diagnosed with breast cancer at our center until patient death from any cause. Recurrence-free survival (RFS) is defined as the time from the date when a patient was first diagnosed with breast cancer at our center until breast cancer recurrence. This paper used Graphpad Prism 10.0 software, R studio, and IBM SPSS Statistics Version 29.0.1.0 software for data processing and plotting. The T-test was used for continuous variables analysis, and the chi-square test was used for categorical variables analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLSM4 is Identified as a Upregulated Spliceosomal Gene in TNBC Through Integrated Omics Analysis\u003c/h2\u003e \u003cp\u003eOur comprehensive molecular profiling of TNBC revealed distinct transcriptomic and proteomic characteristics compared to normal breast tissue. Principal Component Analysis (PCA) demonstrated a clear and robust separation between TNBC and normal samples, with the first principal component (PC1) accounting for 51.27% of the total variance, indicating substantial molecular differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Differential gene expression analysis further identified LSM4 as one of the most significantly upregulated genes in TNBC. Differential expression analysis identified LSM4 as significantly upregulated in TNBC, exhibiting high statistical significance (-log10(p-value)\u0026thinsp;\u0026gt;\u0026thinsp;75) with a Log2FC of approximately 1.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Subsequent pathway enrichment analysis of the differentially expressed genes highlighted a significant overrepresentation of splicing-related pathways, including the spliceosome and Sm/LSm core pathways, suggesting a central role for RNA processing dysregulation in TNBC pathogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). A heatmap visualization of spliceosome-associated genes confirmed this widespread dysregulation, demonstrating a consistent pattern of upregulation across multiple LSM family members and SNRP genes in TNBC samples compared to their normal counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Parallel analysis at the protein level confirmed distinct expression patterns of LSM and SNRP family members in TNBC versus normal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Box plot analysis specifically demonstrated markedly elevated LSM4 RNA levels in TNBC compared to normal tissue (p\u0026thinsp;\u0026lt;\u0026thinsp;2e-16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Notably, LSM4 expression was significantly elevated in basal-type breast cancer compared to other subtypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0056), underscoring its potential subtype-specific relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Critically, this transcriptional upregulation was strongly validated at the protein level, where LSM4 exhibited the most pronounced and statistically significant increase among all LSM family members analyzed (p\u0026thinsp;=\u0026thinsp;0.0000028) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh). Collectively, these multi-omics data established LSM4 as a prominently and consistently dysregulated component of the spliceosomal network in TNBC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Genomics and Clinical Analysis Validate LSM4 as a Critical Player in TNBC Progression\u003c/h2\u003e \u003cp\u003eWe next employed a functional genomics approach to pinpoint spliceosomal genes essential for breast cancer malignancy. A genome-wide CRISPR screen was conducted in the aggressive MDA-MB-231 and CA1a cell lines to identify genes whose knockout negatively impacted cell fitness (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This screen identified 177 spliceosome-associated genes and 91 CA1a negative score genes, with a critical overlap of 9 high-confidence candidates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). LSM4 was prioritized among these overlapping hits for further investigation. Validation by quantitative PCR (qPCR) confirmed a progressive increase in LSM4 expression that correlated directly with cellular aggressiveness. Expression was lowest in normal breast epithelial cells (MCF10A), significantly elevated in the MDA-MB-231 cell line (2.5-fold increase, p\u0026thinsp;=\u0026thinsp;0.0417), and highest in the highly aggressive CA1a cells (approximately 3-fold increase, p\u0026thinsp;=\u0026thinsp;0.0021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the clinical relevance of these findings, we analyzed data from the FUSCC cohort. Survival analysis revealed a notable trend towards reduced relapse-free survival (RFS) in patients with high LSM4 expression (p\u0026thinsp;=\u0026thinsp;0.096) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Cumulative hazard analysis further showed a dramatic increase in risk accumulation within the first 40 months of follow-up specifically for the high LSM4 expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Multivariate Cox regression analysis confirmed that established clinicopathological parameters were strong independent prognostic factors, with advanced lymph node status (N3 vs. N0, HR\u0026thinsp;=\u0026thinsp;7.78, 95% CI: 3.118\u0026ndash;17.82) being the most powerful predictor (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Together, these results establish LSM4 as a critical driver of breast cancer progression, with its prognostic impact being significant alongside other clinical factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLSM4 Knockdown Suppresses Malignant Phenotypes in TNBC Cells\u003c/h2\u003e \u003cp\u003eWe established efficient LSM4 knockdown models using both siRNA and shRNA approaches in MDA-MB-231 and CA1a cell lines to investigate its functional role. Molecular analysis confirmed successful suppression across all constructs, with statistically significant reduction in LSM4 expression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The shRNA construct sh-11 demonstrated particularly potent knockdown efficiency, achieving approximately 95% reduction in LSM4 expression compared to controls (p\u0026thinsp;=\u0026thinsp;0.0004) in both cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-d). Having validated the knockdown efficiency, we examined the impact on cancer cell proliferation using CCK-8 assays. The results revealed that LSM4 suppression significantly inhibited cell growth in a time-dependent manner, with substantial differences emerging after day 3 of treatment. This inhibitory effect was most pronounced at day 5, particularly in CA1a cells where wild-type cells reached an OD 450 of 1.0 while si-11-treated cells showed only 0.2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and f), indicating a crucial role for LSM4 in sustaining TNBC cell proliferation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond proliferation, we investigated the effects of LSM4 knockdown on metastatic capabilities using transwell assays. Migration assays demonstrated that LSM4 suppression profoundly impaired cell motility, reducing migration by up to 80% compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-i). A similar trend was observed in invasion assays, which showed an approximate 50% reduction in the number of invaded cells following siRNA-mediated LSM4 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej-l). Most notably, plate clone formation assays revealed that LSM4 depletion substantially compromised long-term survival and clonogenic capacity. The sh-11 construct reduced colony formation by approximately 75% compared to controls, with both MDA-MB-231 (sh-NC\u0026thinsp;=\u0026thinsp;129.3 vs sh-11\u0026thinsp;=\u0026thinsp;34, p\u0026thinsp;=\u0026thinsp;0.0003) and CA1a (sh-NC\u0026thinsp;=\u0026thinsp;260 vs sh-11\u0026thinsp;=\u0026thinsp;68.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) cells showing significant sensitivity to LSM4 knockdown despite their differential baseline colony formation capacities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em and n). The consistency of these inhibitory effects across multiple functional assays, including proliferation, migration, invasion, and colony formation, firmly establishes LSM4 as a critical regulator of numerous malignant phenotypes in TNBC cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLSM4 Regulates Global Alternative Splicing and Gene Expression in TNBC\u003c/h2\u003e \u003cp\u003eTo elucidate the mechanistic basis of LSM4's functional impact, we performed comprehensive RNA-seq analysis following LSM4 knockdown. Characterization of alternative splicing events identified a total of 1,593 significant splicing alterations, with exon skipping (SE) being the predominant type (1,060 events, 66.5%), followed by mutually exclusive exons (160, 10.0%), alternative 3' splice sites (A3'SS, 158, 9.9%), intron retention (119, 7.5%), and alternative 5' splice sites (A5'SS, 96, 6.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and b). Detailed analysis of specific splicing types revealed widespread differential splicing patterns, with volcano plots for both A3'SS and SE events showing numerous statistically significant alterations (-log10 p-value\u0026thinsp;\u0026gt;\u0026thinsp;10) across inclusive difference ranges from \u0026minus;\u0026thinsp;1.5 to 1.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParallel examination of transcriptional changes identified 295 differentially expressed genes (124 downregulated and 171 upregulated) following LSM4 suppression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Heatmap visualization highlighted consistent expression patterns across biological replicates, showing marked downregulation of LSM4, EDN2, RASSF6 and TATDN3, alongside upregulation of MAST1, CSAD, and ARHGEF4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Functional enrichment analysis provided insight into the biological consequences of these changes. Gene Ontology (GO) analysis revealed significant enrichment in crucial cellular components including the presynaptic active zone, nuclear exosome, and CUL3-RING ubiquitin ligase complex, while biological processes showed enrichment in macrophage migration, regulation of peptide secretion, and face development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Molecular function analysis highlighted exoribonuclease activity and G-protein coupled receptor binding. KEGG pathway analysis further identified significant alterations in Toll-like receptor signaling and inflammatory TRP channel regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated Analysis Reveals Overlap Between Splicing and Expression Alterations\u003c/h2\u003e \u003cp\u003eWe next integrated the splicing and gene expression data to identify coordinated regulatory mechanisms. Venn diagram analysis revealed complex interactions between different regulatory layers, identifying 10 genes overlapping between differentially expressed genes (DEGs) and exon skipping events, 23 genes between SE and A3'SS events, and 12 genes common to all three categories (DEG, SE, and A3'SS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). GO enrichment analysis of these overlapping gene sets demonstrated significant enrichment in DNA repair and telomere maintenance pathways across biological process, cellular component, and molecular function categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-d). Finally, we validated the RNA-seq findings using quantitative PCR, which confirmed the differential expression of seven key genes: four downregulated (EDN2, NAP1L1, TATDN3, and RASSF6) and three upregulated (MAST1, CSAD, and ARHGEF4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee and f). These integrated analyses establish that LSM4 knockdown induces extensive reprogramming of both alternative splicing and gene expression, with coordinated effects on pathways critical for TNBC progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLSM4-Associated Immune Signature Predicts Clinical Outcomes in TNBC\u003c/h2\u003e \u003cp\u003eWe further investigated the clinical implications of LSM4-associated molecular alterations by analyzing the FUSCC cohort. Classification of patients into Signature (SG) High and Signature (SG) Low expression groups revealed distinct distribution patterns across molecular subtypes using both FUSCC (Basal-Like Immune-Suppressed, Immunomodulatory, Luminal Androgen Receptor, Mesenchymal) and Lehmann classification systems (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Comparative analysis demonstrated significant differences in clinicopathological features between these groups. The SG High group comprised significantly younger patients (peak age around 50 years) compared to the SG Low group (peak age around 63 years) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and exhibited markedly higher Ki67 expression levels (peak 80\u0026ndash;120 vs 70 in SG Low) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Analysis of the tumor immune microenvironment through heatmap visualization revealed distinct expression patterns of immune-related genes, including key markers such as CD8A, CXCL9, CXCL10, GZMB, PRF1, and checkpoint molecules (CD274, PDCD1, PDCD1LG2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurvival analyses over 50 months demonstrated the significant prognostic power of the LSM4-associated signature. Most strikingly, overall survival (OS) analysis revealed consistently lower survival probability in the SG High group (approximately 0.80) compared to the SG Low group (approximately 0.90) (p\u0026thinsp;=\u0026thinsp;0.00067) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Similarly, recurrence-free survival (RFS) analysis showed clinically relevant differences between the groups (p\u0026thinsp;=\u0026thinsp;0.05), though slightly less pronounced than OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Analysis of distant metastasis-free survival (DMFS) further confirmed significant stratification, with the SG High group maintaining lower survival rates throughout the follow-up period (p\u0026thinsp;=\u0026thinsp;0.0092) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Notably, all survival curves showed early separation at approximately 10 months and maintained this differential throughout the entire follow-up period, supporting the robust and consistent prognostic value of the LSM4-associated immune signature in predicting patient outcomes across multiple survival endpoints.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious investigations into LSM4 have primarily focused on its fundamental roles in RNA processing, particularly as a component of the U6 snRNP complex in nuclear mRNA splicing and the LSM1-7 complex in cytoplasmic mRNA decay [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While genomic studies have noted a distinctive alteration pattern of LSM4 in breast cancer, with approximately 9% frequency [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], its specific functional significance and mechanistic contributions in triple-negative breast cancer remained largely unexplored. This study provides compelling evidence that establishes LSM4 as a critical oncogenic driver in TNBC through its regulatory role in alternative splicing, with particular implications for tumor immune regulation.\u003c/p\u003e \u003cp\u003eOur findings demonstrate that LSM4 expression is significantly elevated in TNBC tissues and progressively increases with cellular aggressiveness. Through comprehensive splicing analysis, we identified that LSM4 regulates 1,593 alternative splicing events, predominantly through exon skipping mechanisms, thereby reshaping the transcriptomic landscape to activate pro-tumorigenic pathways. These results position LSM4 among a select group of splicing factors that serve as critical dependencies in cancer cells [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], extending previous observations of spliceosomal dysregulation in TNBC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe connection between LSM4 and tumor immune regulation represents a particularly significant aspect of our findings. This is particularly relevant given the growing recognition that alternative splicing serves as a key mechanism linking RNA processing to antitumor immunity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our analysis revealed that LSM4-associated splicing events significantly impact immune-related pathways, including Toll-like receptor signaling and inflammatory TRP channel regulation. More importantly, the LSM4-defined gene signature identifies a distinct TNBC subgroup characterized by specific alterations in key immune markers. This signature shows markedly different expression patterns of critical immune-related genes including CD8A, CXCL9, CXCL10, GZMB, PRF1, and various checkpoint molecules (CD274, PDCD1, PDCD1LG2). The coordinated downregulation of T-cell recruiting chemokines (CXCL9, CXCL10) and cytolytic effectors (GZMB, PRF1) in SG High tumors suggests that LSM4 may contribute to establishing an immune-suppressive microenvironment, potentially facilitating immune evasion. This finding aligns with emerging evidence linking alternative splicing to immune regulation in cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and provides a mechanistic basis for the aggressive clinical behavior of LSM4-high tumors.\u003c/p\u003e \u003cp\u003eThe molecular mechanisms underlying LSM4's oncogenic function involve extensive reprogramming of both splicing patterns and gene expression networks. Our integrated analysis revealed that LSM4-dependent splicing alterations converge on pathways crucial for cancer progression, including RNA degradation and DNA repair. This multifaceted impact aligns with LSM4's established dual functionality in RNA processing [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and provides mechanistic insight into how splicing regulators can coordinate complex oncogenic programs. The significant overlap between differentially expressed genes and splicing events further suggests that LSM4 operates within an interconnected regulatory network that simultaneously controls both splicing patterns and expression levels of target genes.\u003c/p\u003e \u003cp\u003eThe robust prognostic value of the LSM4-associated immune signature across multiple survival endpoints (OS: p\u0026thinsp;=\u0026thinsp;0.00067; DMFS: p\u0026thinsp;=\u0026thinsp;0.0092) underscores its clinical relevance for patient stratification. The early separation of survival curves at approximately 10 months and maintained differential throughout follow-up suggests that LSM4-mediated immune alterations have lasting impact on disease progression. This finding gains additional significance given the growing importance of immunotherapy in cancer treatment and the urgent need for reliable biomarkers to guide therapeutic decisions in TNBC.\u003c/p\u003e \u003cp\u003eIn the context of TNBC treatment challenges, where current therapeutic options remain limited, our functional studies demonstrate that LSM4 suppression profoundly impairs multiple malignant phenotypes. These findings not only provide a mechanistic explanation for the poor prognosis associated with high LSM4 expression but also highlight the therapeutic potential of targeting this splicing regulator. Given the limited targeted therapy options for TNBC, developing strategies to modulate LSM4 activity or its downstream splicing network may offer new therapeutic avenues, particularly in combination with immunotherapeutic approaches.\u003c/p\u003e \u003cp\u003eIn summary, our study elucidates the oncogenic role of LSM4 in TNBC progression through its function as an important splicing regulator with significant impact on tumor immune microenvironment. These findings not only advance our understanding of splicing dysregulation in TNBC pathogenesis but also identify LSM4 as a potential prognostic biomarker and therapeutic target. Future investigations exploring LSM4-targeted strategies and its specific roles in modulating anti-tumor immunity may yield significant insights for improving TNBC management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary Materials\u003c/h2\u003e \u003cp\u003eAll sequences used in this study are provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (S1.1\u0026ndash;S1.4).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eJN, YXL and HL conceived the study design and methodology. JN and YXL planned the experimental design and analyzed the data; JN and BH conducted the experiments and collected most of the data; XH and HL provided materials and clinical specimens; JN and YXL analyzed the final results and wrote the manuscript. All authors read and approved the final manuscript. JN and YXL contributed equally to this work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to Professor Zhimin Shao for providing the laboratory facilities and resources that made this research possible. We are also deeply thankful to Wenxiao Yang and Luo Hong for their invaluable support and expertise throughout the course of this study. Their contributions to experimental work and data analysis were instrumental in the successful completion of this research.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eAll data is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024; 74:12\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmansour NM. Triple-negative breast cancer: a brief review about epidemiology, risk factors, signaling pathways, treatment and role of artificial intelligence. Front Mol Biosci. 2022; 9:836417.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MA, Nasrin F, Bhattacharjee S, Nandi S. Hallmarks of splicing defects in cancer: clinical applications in the era of personalized medicine. 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Int Rev Cell Mol Biol. 2009; 272:149\u0026ndash;189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerea-Resa C, Carrasco-L\u0026oacute;pez C, Catal\u0026aacute; R, Turečkov\u0026aacute; V, Novak O, Zhang W, \u003cem\u003eet al\u003c/em\u003e. The LSM1-7 complex differentially regulates arabidopsis tolerance to abiotic stress conditions by promoting selective mRNA decapping. Plant Cell. 2016; 28:505\u0026ndash;520.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Zhang Q, Wang H, Yang X, Mu H. Alternative splicing and related RNA binding proteins in human health and disease. Signal Transduct Target Ther. 2024; 9:26.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9275393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9275393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriple-negative breast cancer (TNBC) remains a therapeutic challenge due to its aggressive nature and limited treatment options. Through integrated genomic and proteomic profiling, we identified the RNA-binding protein LSM4 as a key driver of TNBC progression. LSM4 was significantly upregulated in TNBC tissues and showed progressively increasing expression from normal breast epithelial cells to highly aggressive cancer models. Functional genomics using CRISPR screening nominated LSM4 as a functionally essential spliceosomal gene among nine critical candidates. Genetic suppression of LSM4 in TNBC cell lines (MDA-MB-231 and CA1a) markedly inhibited malignant phenotypes including proliferation, migration, invasion, and colony formation. RNA-sequencing analysis demonstrated that LSM4 knockdown alters 1,593 alternative splicing events, predominantly through exon skipping, and disrupts genes involved in key cancer pathways including RNA degradation. We established an LSM4-associated gene signature that correlated with an immunosuppressive microenvironment characterized by altered expression of immune markers and checkpoint molecules. Clinical validation using the FUSCC cohort revealed that this signature strongly predicted poor overall survival (p\u0026thinsp;=\u0026thinsp;0.00067), recurrence-free survival (p\u0026thinsp;=\u0026thinsp;0.05), and distant metastasis-free survival (p\u0026thinsp;=\u0026thinsp;0.0092). Our findings establish LSM4 as an important splicing regulator in TNBC, linking spliceosomal dysfunction to tumor progression and immune evasion, and nominate LSM4 as a promising prognostic biomarker and therapeutic target.\u003c/p\u003e","manuscriptTitle":"LSM4 drives the progression of triple-negative breast cancer through alternative splicing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 09:19:14","doi":"10.21203/rs.3.rs-9275393/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":"d8430926-acb7-4f83-a442-50bb2ac74955","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65848353,"name":"Biological sciences/Cancer/Breast cancer"},{"id":65848354,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2026-04-08T09:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 09:19:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9275393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9275393","identity":"rs-9275393","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}