WGCNA-derived lncRNA MAP3K4-AS1 regulates apoptosis and cell cycle in TNBC MDA-MB-231 cells validated by siRNA knockdown.

Introduction
Breast cancer is a heterogeneous disease, accounting for an estimated 32% of all new cancer cases among women in 2025 [1]. There are different molecular subtypes of breast cancer, which are classified into four main ones, including Luminal A (ER+, PR+, and HER2-) with a desirable prognosis; Luminal B (ER+, PR+, and HER2+/-) more aggressive; HER2-enriched (ER-, PR-, and HER2+) with a higher risk of recurrence; and triple-negative breast cancer (TNBC) (ER-, PR-, and HER2-) more challenging to treat and typically more aggressive [2–4]. The epidemiology of breast cancer reveals significant factors, for example increasing age [5, 6], ethnicity [7, 8], etc., while genetic predispositions, primarily BRCA1 and BRCA2 mutations, are crucial factors in its development [9]. Nearly 10% to 20% of breast cancer cases are attributed to TNBC, which is more common in women under the age of 50, especially those of African American and Hispanic descent. Unfortunately, TNBC is often diagnosed at more advanced stages, leading to a poorer prognosis. It tends to be more aggressive because the absence of these receptors means that hormone therapies and targeted treatments are not options. As a result, treatment usually involves chemotherapy, surgery, and radiation. This makes the management of TNBC more challenging and increases the risk of recurrence and metastasis. Due to these difficulties, ongoing research into new and effective treatments for TNBC is crucial in the field of oncology [10–12]. Recent research works have highlighted the participation of long non-coding RNAs (lncRNAs) (≥ 200 nucleotides) in the regulation of gene expression and tumor progression in different cancer types. This implies that lncRNAs could be useful therapeutic targets or biomarkers [13]. Treatment strategies have changed significantly, with targeted therapies, including the use of small interfering RNA (siRNA), which suggests new methods for disrupting cancer cell pathways and improving patient outcomes [14]. In recent decades, bioinformatics has emerged as a novel computational approach, which bridges vast biological data with meaningful insights in biological research [15, 16]. Among its many disciplines, transcriptomics, studying the sum of all RNA transcripts in an organism, is of great importance [17]. To take this a step further, systems biology aids scientists to integrate data from various biological layers, genes, proteins, and metabolites to create holistic models of cellular behavior [18]. One powerful tool in this area is Weighted Gene Co-expression Network Analysis (WGCNA), which unravels complex relationships between genes by detecting clusters of co-expressed genes and their associations with traits or conditions [19–21]. The investigation of these fields improves our familiarity of biological systems and supports the creation of novel therapeutic approaches, propelling us toward personalized medicine [22, 23]. Combining bioinformatics and experimental approaches offers a powerful and comprehensive approach to identify and functionally validate novel lncRNAs in different types of cancers [24]. This study utilized the GEO database, WGCNA, and a variety of cellular and molecular laboratory techniques to validate the findings from the bioinformatics approach, with the objective of elucidating the intricate molecular and cellular landscape of TNBC. Although MAP3K4-AS1 was briefly mentioned in a limited number of studies, its function remains largely uncharacterized, and to date, no in vitro research was conducted on this lncRNA. In this study, MAP3K4-AS1 was detected through Weighted Gene Co-expression Network Analysis (WGCNA) as a lncRNA strongly correlated with TNBC progression. This marks the first comprehensive investigation of MAP3K4-AS1 in triple-negative breast cancer, integrating WGCNA-based identification with detailed in vitro functional analyses. The study highlights its oncogenic role and proposes MAP3K4-AS1 as a novel biomarker, positioning this work as the first to experimentally characterize its biological relevance in TNBC.

Materials and methods
The overall workflow of the study is illustrated in Fig. 1, which provides a visual summary of the key steps undertaken.

In-silico analysis

Data recruitment and preprocessing
The NCBI GEO database was utilized to extract the expression profile of lncRNAs related to TNBC. The GSE115275 dataset, which includes the expression profile of TNBC-related lncRNAs in tumor and matched non-tumor breast tissues, was selected for further studies. LncRNA Arraystar Microarray analysis was applied in this dataset to find the expression profile of lncRNAs and miRNAs involved in TNBC tumors and adjacent non-tumor ones. The data of the desired dataset was analyzed with GEO2R, probability value (Benjamini and Hochberg), and normalized by the limma package. Then, gene expression data with − 2 ≥ log FC ≥ 2 and adjusted P-value ≤ 0.01 was chosen.

Weighted gene co-expression analysis (WGCNA)
To create the gene co-expression network utilizing the gene expression data, the similarity matrix for each gene was first determined by the Pearson correlation coefficient approach. The threshold value was used to convert the similarity matrix into the adjacency matrix, which was ​​calculated from the similarity matrix. A threshold value was used as an input parameter using the PickSoftThreshold () function. The networks built in this study are of the signed hybrid type. Genes with negative correlation coefficients were assigned a zero value. WGCNA was performed by means of the R programming language version 4.3.2 and the WGCNA package.

Construction of gene co-expression network
Initially, outlier data were removed, and the interaction between gene patterns was used to build the gene co-expression network, so gene correlation was considered a criterion for gene co-expression. The expression matrix used the downloaded GEO dataset as input, and the co-expression network was generated by means of the normalized expression data (DElncRNAs). Soft threshold was determined with the scale-free topology matrix standard. The adjacency matrix was calculated based on the selected power and converted into a topological overlap matrix, which was employed to identify continuously associated nodes of co-expressed lncRNAs. Gene nodes were identified by the hierarchical clustering algorithm and Dynamic Tree Cut, and lncRNAs of each node was obtained to summarize the expression profiles of each gene node. Nodes were defined as clusters of highly interconnected and related lncRNAs.

Functional enrichment analysis
At this stage, functional enrichment analysis was carried out to study the pathway enrichment analysis of lncRNAs. To study the functional enrichment of the found lncRNA, lncRNA name was searched in the LncHUB2 database as the input, and functional enrichment analysis of KEGG was obtained. LncHUB2 is a web-based database that offers established and inferred insights regarding the functions of mouse and human lncRNAs. This database has the potential to support the formulation of hypotheses for numerous future studies (29).

In-vitro analysis

Cell culture and cell line selection
The human TNBC cell lines, MDA-MB-231 and MDA-MB-468, were acquired from the Pasteur Institute Cell Bank (Tehran, Iran) and cultured in vitro in RPMI 1640 medium (Gibco, USA) with 10% FBS (Gibco, USA), 100 U/mL penicillin, and 100 mg/L streptomycin (Sigma, USA) under 5% CO₂, at 37 °C in a humidified atmosphere. The relative expression of MAP3K4-AS1 in both cell lines were evaluated employing qRT-PCR to determine the cell line with the greatest expression of MAP3K4-AS1 for this study. The primer pairs utilized for qRT-PCR are detailed in Table 1.

siRNA was transfected by liposome-mediated delivery using lipofectamine 3000
Small interfering RNA (siRNA) was obtained with HPLC purification (Metabion, Germany), annealed with annealing buffer, and utilized according to supplier’s protocol. All transfection procedures were conducted by Lipofectamine 3000 (Invitrogen, USA). MDA-MB-231 cells were seeded at 25 × 104 cells/well in a 6-well plate. Following 24 h of incubation, cells underwent transfection with siRNA-MAP3K4-AS1 utilizing Lipofectamine 3000, in line with the producer’s guidelines, in a medium devoid of serum and antibiotics. After incubation, the cells were tested by succeeding experiments. The sequences of used siRNAs are detailed in Table 1.

Cell proliferation assay
To evaluate cell viability, MTT (Sigma-Aldrich, Germany) was applied in accordance with established guidelines. The cells were plated in a 96-well format at a density of 8 × 103 cells per well, transfected, and subsequently incubated for a duration of 48 hours. Additionally, a negative control (scramble siRNA, 5’ UUCUCCGAACGUGUCACGUUU 3’) without any target was utilized as a negative control and was transfected using lipofectamine in a serum-free medium. Following an incubation period of 48 h, the culture media was gently aspirated and substituted with 150 µL of a solution containing 50 µL of MTT (2 mg/mL in PBS) and 100 µL of complete media. The plates were subsequently incubated for an additional 4 h at 37 °C. The formazan crystals in each well were solubilized using 200 µL of DMSO (Sigma-Aldrich). Following a 30-minute incubation period, the optical density in each well was evaluated at 570 nm utilizing an ELISA reader (Tecan, Switzerland).

Cell apoptosis
Cell apoptosis analysis was conducted to validate the outcomes of the MTT test. To measure the rate of siRNA-MAP3K4-AS1 apoptosis induction, cells were seeded in a 6-well plate at a density of 25 × 104, transfected, and incubated for 48 h. The wells were divided into siRNA-MAP3K4-AS1 transfected, and non-transfected groups. Following a 48-hour incubation period, cells were detached using trypsin, washed three times with PBS, and subsequently resuspended in 500 µl of the binding solution. They were subsequently incubated with 5 µl of FITC-conjugated annexin-V and 5 µl of PI (Exbio et al.) for 15 min at ambient temperature in a dark environment. Flow cytometry (FACSQuant; Miltenyi, Germany) was employed to recognize the labeled cells.

Cell cycle assay
Cell cycle analysis was conducted utilizing a commercial kit to validate the findings of cell apoptosis. Cells were plated in a 6-well plate at a density of 25 × 104, followed by transfection and a 48-hour incubation period. The cells were arranged in a manner consistent with previous testing protocols. After 48 h, the cells were collected from the 6-well plates utilizing a trypsin/EDTA solution and subsequently were washed twice with pre-chilled PBS. Each batch of cells has subsequently been fixed with 1 ml of 75% ethanol and incubated overnight at -20 °C. On the following day, the cells were resuspended in PBS, and 5 µl of RNaseA was added to each group, followed by incubation for 30 min at 37 °C. The cells have subsequently been treated with DAPI and Triton X100. Flow cytometry has revealed the arrest of the cell cycle.

Colony formation assay
The in vitro colony formation test was carried out to assess the efficacy of siRNA-MAP3K4-AS1 on the colonies formed by MDA-MB-231 cells and to confirm the findings of the previous step. The cells were plated in a 6-well plate and transfected at a density of 103 cells per well. The cells were organized into the following groups: transfected, and non-transfected. The plate underwent examination for colonies following a ten-day incubation period. Subsequently, the cells were rinsed with PBS and labeled using a 0.5% solution of crystal violet (Sigma Aldrich, USA). Cells were fixed with 4% paraformaldehyde for 15 min at ambient temperature. After a final PBS wash, colony images were captured using a standard compact camera.

Wound healing assay
To evaluate the impact of MAP3K4-AS1 suppression on cell migration and to validate the findings from earlier phases of the study, a scratch test was conducted. To achieve this, cells were seeded at a density of 25 × 104 and transfected in a 6-well plate, followed by the creation of a scratch in the center of each well. Subsequently, cells were monitored until the gap in the control group was closed, and photographs of the cells were imaged at 0 h, 24 h, 48 h, and 72 h intervals. The wound area was measured at each time point using ImageJ software to quantify the percentage of wound closure.

qRT-PCR
The MDA-MB-231 cancer cells were seeded at a density of 6 × 105 cells per well in a 6-well plate. Subsequently, total RNA was isolated using the Trizol reagent (RiboEx kit, GeneAll, South Korea), and cDNA was synthesized following the kit’s guidelines (Zistvirayesh, Iran). A nanodrop spectrophotometer was employed to determine the concentration and quality of isolated RNAs through absorbance analysis at 260 and 280 nm wave lengths (Thermo Fisher Scientific Life Science, USA). 1 µl of the collected RNA was utilized in the cDNA synthesis kit for the synthesis of complementary DNA (cDNA). Furthermore, cDNA synthesis was performed utilizing RT Mater Mix to assess the expression levels of target genes (Amplicon, Denmark). The expression levels of BAX, BCL2, caspase 3, caspase 8, caspase 9, CD44, MMP3, and MMP9 were quantified through qRT-PCR utilizing SYBR Premix Ex Taq (Biofact) and the StepOne Plus rt-PCR system (Applied Biosystems, Thermo Fisher Scientific, USA). GAPDH served as a control for the detection of target genes and MAP3K4-AS1.The primer sequences for the specified genes are detailed in Table 2.

Statistical analysis
For each data point gathered in this experiment, a minimum of three repetitions of three different tests were conducted. The mean represents continuous variables alongside the standard deviation (SD). The analysis of variance, encompassing one-way and two-way ANOVA, as well as Dunnett’s test, was utilized to identify noteworthy differences among the groups. Statistical analyses were.
performed using GraphPad Prism. A P-value of ≤ 0.05 was chose to specify statistical significance.

Results

Identification of DElncRNAs
Differentially expressed lncRNAs (DElncRNAs) were achieved by implementing the adj-p value ≤ 0.01 and − 2 ≥ LOG FC ≥ 2. Finally, among the 61,025 genes and lncRNAs in the gene list, 1358 genes and lncRNAs were selected as DEGs. All samples were analyzed, and WGCNA generated a gene expression network. Data were derived using the plot() function from the Flashclust library in the R programming environment.

Reconstruction of gene co-expression network and module detection
The gene co-expression network was reconstructed using the constructed expression data of matched non-tumor and tumor samples, with the β value of 18 using the pickSoftThreshold() function, Fig. 2. By applying hierarchical clustering, 12 gene nodes (MEpink, MEgreen, MEblack, MEblue, MEturquoise, MEred, MEmagenta, MEpurple, MEgreenyellow, MEyellow, MEtan, and MEbrown) were achieved, and each node was displayed with a special color, Fig. 3A. Modules were merged by changing the CutOff value, and finally, 5 modules (Magenta, Green, Yellow, Red, and Black) were obtained, Fig. 3B.

Identification of significant modules
Among all the obtained modules, the modules with significant expression changes in TNBC were selected. For this purpose, the modules with ≤2 were selected for the successive analysis. These modules include lncRNAs, which may have major roles in TNBC. Among the 5 detected modules, 4 significant ones were identified (Magenta, Green, Yellow, Black, and Red) as significant modules, Fig. 4. All the found lncRNAs in these modules can play roles in the occurrence and progression of TNBC, which should be analyzed in vitro to validate the bioinformatic results.

Identification of hub LncRNA
Based on data analysis and the significant expression changes of MAP3K4-AS1 in the Magenta module (log fold change of 2.05 and adj P-value of 8.85E-03), Table 3, this lncRNA was selected for in vitro analysis. Also, so far, no study was done on the effect of this lncRNA in laboratory conditions on any of the cancers; this novel lncRNA may have a substantial impact on the onset and advancement of various cancer types, including TNBC. According to bioinformatic studies, the expression of MAP3K4-AS1 has significant changes in TNBC. MAP3K4-AS1 is located on chromosome number 6 (29).

Identification of MAP3K4-AS1 role in cancer-related pathways
To determine the functional enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was inquired, and it was presented that MAP3K4-AS1 is a crucial one in cancer related pathways. These include the cell cycle, ECM-receptor interaction, Glycosaminoglycan biosynthesis, Hippo signaling pathway, etc. as mentioned in Table 4.

To explore whether MAP3K4-AS1 may regulate its host gene MAP3K4, we conducted an in silico lncRNA–mRNA interaction analysis using the lncRTarPred prediction server. The prediction integrates multiple machine learning models including Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and LightGBM—to estimate potential regulatory interactions based on sequence-derived features. When MAP3K4-AS1 and MAP3K4 were analyzed, the Decision Tree model predicted a relatively high interaction probability (75.69%), while Random Forest and LightGBM provided moderate scores (35.86% and 33.85%, respectively). The cumulative score (25.0%) suggests a modest potential regulatory association between MAP3K4-AS1 and MAP3K4. These computational results imply that strong cis-regulatory effects are unlikely, yet a mild interaction or contribution to MAP3K4 regulation cannot be excluded. Additionally, enrichment analysis of genes correlated with MAP3K4-AS1 indicated involvement in pathways such as cell cycle regulation, ECM–receptor interactions, and Hippo signaling, suggesting that MAP3K4-AS1 may also exert trans-regulatory functions in cancer-related networks.

MAP3K4-AS1 is overexpressed in MDA-MB-231 cells
The expression level of MAP3K4-AS1 in both MDA-MB-231 and MDA-MB-468 cell line indicated an overexpression of MAP3K4-AS1 in the MDA-MB-231 cell line; consequently, MDA-MB-231 was selected for subsequent experiments, as shown in Fig. 5A.

Determination of the effective dose of siRNA-MAP3K4-AS1
The MDA-MB-231 cell line was transfected with two doses of siRNA using lipofectamine (4 nM and 7.5 nM). qRT-PCR evaluation demonstrated that the 75 pmol group showed a significant greater decrease in MAP3K4-AS1 expression than the 40 pmol group, Fig. 5B.

MAP3K4-AS1 knockdown suppressed the viability of MDA-MB-231 cells
The effect of MAP3K4-AS1 silencing on the survival, growth, and proliferative abilities of MDA-MB-231 was determined to validate the previous stage’s results. The cells were transfected for 48 h at the dose of 75 pmol, with two control groups (GAPDH and Negative control), and their viability was measured using the MTT assay. SiRNA-MAP3K4-AS1 transfection resulted in a minor cell viability in contrast to control groups, Fig. 5C.

Silencing of MAP3K4-AS1 induces apoptosis in MDA-MB-31 cells
To assess the impact of MAP3K4-AS1 on cell apoptosis and confirm the findings from the previous phase, MDA-MB-231 cells were transfected with siRNA-MAP3K4-AS1 for 48 h, followed by staining with annexin V and propidium iodide (PI), and analyzed by means of flow cytometry. Apoptotic cell levels reached 20.6% in the knockdown group, whereas only 4.24% apoptosis was observed in the control group. The silencing of MAP3K4-AS1 resulted in the induction of apoptosis in MDA-MB-231 transfected cells. Figure 6.

MAP3K4-AS1 modulated the expression level of apoptosis-related genes in MDA-MB-231 cells
The expression levels of BAX and BCL2 genes demonstrated a significant upregulation and downregulation, respectively in siRNA-transfected cells in contrast to control ones, respectively. Figure 7. Additionally, the expression levels of caspase 3, caspase 8, and caspase 9 presented an upregulation in the expression of mentioned genes in siRNA-transfected cells in contrast to the control ones. The quantitative analysis for each sample was performed utilizing GAPDH as the normalization reference gene.

MAP3K4-AS1 knockdown induced the cell cycle arrest of MDA-MB-231 cells
Cell cycle progression after MAP3K4-AS1 suppression was evaluated using flow cytometry. Concluded data demonstrated that MAP3K4-AS1 knockdown induced the sub-G1 arrest in MDA-MB-231 cells in contrast to the negative control, Fig. 8A. The proportion of siRNA-MAP3K4-S1 transfected cells in the sub-G1 phase rose from 0.83 to 3.23 (Fig. 8B).

Knockdown of MAP3K4-AS1 inhibited colony formation ability of MDA-MB-231 cells
Colony-forming ability MDA-MB-231 cells was notably suppressed following siRNA-MAP3K4-AS1 transfection, Fig. 9A. Additionally, it was presented that CD44 expression levels was notably decreased in siRNA-MAP3K4-AS1 transfected cells, Fig. 9B.

Knockdown of MAP3K4-AS1 inhibited cell migration and invasion in MDA-MB-231 cells
The outcomes of migration assays indicated that the siRNA-MAP3K4-AS1 transfection results in a delay in wound area closure 72 h post-wound creation in contrast to the control group, Fig. 10A. The number of migrated cells after 72 h was increased in the MAP3K4-AS1 transfected group in comparison to the control group, as shown in Fig. 10B. Furthermore, the expression rates of MMP3 and MMP9 was assessed as genes related to migration to validate the findings. Cells transfected with siRNA-MAP3K4-AS1 exhibited a notable reduction in the expression levels of MMP3 and MMP9, as illustrated in Fig. 11.

Discussion
Despite significant advances in breast cancer research, triple-negative breast cancer (TNBC) continues to present therapeutic challenges owing to its molecular diversity, aggressiveness, and limited targeted treatment options [25, 26]. This highlights the necessity for detecting novel molecular regulators that contribute to TNBC pathogenesis and may serve as disease markers.
Long non-coding RNAs (lncRNAs), known for their regulatory functions in gene expression, epigenetic modifications, and cellular signaling, have increasingly been involved in tumor development and progression [27–30]. A variety of oncogenic lncRNAs such as NEAT1 [31–33], HOTAIR [34–36], and MALAT1 [37, 38] were previously linked to proliferation, invasion, and migration in breast cancer, and some of their roles are briefly summarized in Table 5. However, many lncRNAs remain poorly characterized, especially in the context of TNBC.
In this study, MAP3K4-AS1 was identified as a novel lncRNA of interest by means of Weighted Gene Co-expression Network Analysis (WGCNA) applied to TNBC datasets from the NCBI GEO repository. This approach facilitated the unbiased identification of modules associated with TNBC biology. Although MAP3K4-AS1 was previously studied in limited contexts, namely, in cardiac fibroblasts where it was shown to mitigate doxorubicin-induced cytotoxicity [39] and in lung cancer where its expression correlated with patient prognosis [40], its role in breast cancer had not been previously explored.

MAP3K4-AS1 was identified as the primary candidate lncRNA based on its strong statistical significance and biological relevance within the WGCNA module. It exhibited one of the highest levels of differential expression, characterized by a substantial log fold-change (2.05066151) and an adjusted P-value (8.85E-03) indicative of pronounced dysregulation in triple-negative breast cancer (TNBC) tissues. Notably, MAP3K4-AS1 remains completely uncharacterized in breast cancer or other malignancies, with no previous reports describing its potential functional role or regulatory mechanisms.
KEGG pathway enrichment analysis of MAP3K4-AS1 revealed its potential involvement in several critical cellular processes relevant to TNBC pathogenesis, including cell cycle regulation, where dysregulation leads to unrestrained cell proliferation [41], ECM-receptor interaction, as interactions between the extracellular matrix (ECM) and cell surface receptors are crucial for cancer cell adhesion, migration, and invasion, thereby facilitating metastasis [42], glycosaminoglycan biosynthesis, which is implicated in tumor growth and angiogenesis [43], and Hippo signaling, where aberrant signaling can promote cancer development by disrupting tissue homeostasis and enhancing cell survival [44]. Disruptions in these pathways are well-known to contribute to cancer progression. These pathways are well-known for their involvement in promoting tumor cell proliferation, invasion, and metastasis, thereby supporting our hypothesis that MAP3K4-AS1 plays a role in TNBC biology.
Our experimental findings validate this hypothesis. qRT-PCR analysis showed notable upregulation of MAP3K4-AS1 in the MDA-MB-231 TNBC cell line. This overexpression prompted us to investigate its functional role in more detail. Knockdown of MAP3K4-AS1 using its specific siRNA resulted in a reduction in cell viability, indicating that MAP3K4-AS1 likely contributes to cell survival. The MTT assay confirmed this reduction in viability, with approximately 25% less viability in the knockdown cells in contrast to controls, highlighting the pro-survival role of MAP3K4-AS1.
Further analysis revealed that MAP3K4-AS1 knockdown significantly increased apoptosis as analyzed by flow cytometry. The proportion of apoptotic cells in the knockdown group was 20.6%, contrasted to 4.24% in the control group. This increased apoptosis was associated with the upregulation of pro-apoptotic genes including Bax [45], caspase 3 [46], caspase 8 [47], and caspase 9 [48], and the downregulation of Bcl-2, an anti-apoptotic protein [49]. These findings suggests that MAP3K4-AS1 suppresses apoptosis in TNBC cells by apoptotic pathways. Cell cycle analysis presented a significant increase in the sub-G1 population, from 0.83% to 3.23%, which demonstrates its role in regulating cell cycle. Colony formation assays demonstrated that silencing MAP3K4-AS1 negatively affected the ability of MDA-MB-231 cells to form colonies, further supporting its role in cancer progression. CD44, a stem cell marker involved in cancer stem cell self-renewal and metastasis [50], significantly downregulated in MAP3K4-AS1 knockdown cells. Wound healing assays further confirmed the migration defect in the knockdown group, with an approximately 8.8-fold greater wound area in contrast to control cells, suggesting that MAP3K4-AS1 is essential for TNBC cell migration. Additionally, MAP3K4-AS1 knockdown reduced the expression of MMP-3 [51] and MMP-9 [52], enzymes that promote cell invasion and metastasis.
In sum, concluded data uphold a multifaceted role for MAP3K4-AS1 in TNBC progression, contributing to cell survival, apoptosis, cell cycle arrest, migration, and invasion. These results are in line with studies on other lncRNAs such as NEAT1, HOTAIR, and MALAT1, which are renowned to regulate proliferation, migration, and invasion in breast cancer [31–36]. MAP3K4-AS1’s similarities with these lncRNAs reinforce its potential as an oncogenic driver in TNBC.
While current research work clarifies the role of MAP3K4-AS1 in TNBC, there are still some limitations. All experimentations were directed in a single TNBC cell line (MDA-MB-231), which may not fully represent the molecular heterogeneity of TNBC. Further studies involving other TNBC cell lines and patient-derived xenografts (PDXs) are required to strengthen the generalizability of our findings. Additionally, our study did not include protein-level validation of key markers involved in apoptosis and migration. Western blotting or immunohistochemistry would help confirm the expression of these markers and provide a more comprehensive view of MAP3K4-AS1’s regulatory impact.
Another limitation is the lack of in vivo data. While RNA interference (RNAi) targeting MAP3K4-AS1 shows promising results in cell culture, supplementary analysis is needed to evaluate its function in animal models of TNBC. This would help assess the potential of MAP3K4-AS1 as a treatment candidate in preclinical settings.

Conclusion
In conclusion, current analysis is the first to state the functional role of MAP3K4-AS1 in TNBC using in vitro validation, following its identification via WGCNA. This lncRNA exhibits oncogenic characteristics. SiRNA-mediated knockdown of this lncRNA promotes cell survival, induces cell apoptosis and cell cycle arrest, inhibits migration, and invasion of TNBC cancer cells. MAP3K4-AS1 is a promising therapeutic target. Future studies should aim to validate its function in vivo, explore its regulatory mechanisms in greater detail, and evaluate its value for clinical applications such as diagnostics or targeted therapy.