hsa-let-7b-5p/TMPO-AS1-mediated ceRNA networks are linked to poor prognosis for lung cancer patients with FOXM1/MAD2L1 Axis

hsa-let-7b-5p/TMPO-AS1-mediated ceRNA networks are linked to poor prognosis for lung cancer patients with FOXM1/MAD2L1 Axis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article hsa-let-7b-5p/TMPO-AS1-mediated ceRNA networks are linked to poor prognosis for lung cancer patients with FOXM1/MAD2L1 Axis Chainsee Saini, Prerna Vats, Bhavika Baweja, Sakshi Nirmal, Rajeev Nema This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794582/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objectives MAD2L1, a spindle assembly checkpoint molecule, is associated in cancer cell proliferation and carcinogenesis, although its ceRNA network is unknown. Methods Initially, patient’s survivability associated with the gene expression was analysed by using the Kaplan-Meier plotter database. Here, we used several TCGA databases such as UALCAN, OncoDB, ENCORI, Lung cancer explorer, GEPIA2, TCGAnalyzer, and CancerMIRNome to identify differential mRNA, miRNA, and lncRNA expression. The Enrichr database was utilized to identify the transcription factor regulating MAD2L1, which was then correlated with miRNA and lncRNA, forming the ceRNA network using the miRNet database. Database miRWalk and RNA22v2 were used to predict the folding energy and binding affinity between the MAD2L1 and miRNA. TIMER and TIMER 2.0 databases were incorporated to analyse the tumor infiltrating immune cells in LUAD. Results The study found that overexpression of MAD2L1 in lung cancer patients is a high-risk factor for lung adenocarcinoma (LUAD) (HR = 1.34, P = 0.001), particularly in smoker females (HR = 1.61, P = 0.018). The study revealed MAD2L1 overexpression in LUAD cases, with a fold change of 8.7, and a strong positive correlation between RNA and protein expression levels by Cancer Proteome (R = 0.764). The study identified regulatory molecules of MAD2L1 such as transcription factor FOXM1 (R = 0.770), and lncRNA TMPO-AS1 (R = 0.565) as positively correlated with MAD2L1, while miRNA hsa-let-7b-5p, negatively correlated with MAD2L1 (R =-0.314), FOXM1 (R =-0.393), and TMPO-AS1 (R =-0.277). The study suggests that TMPO-AS1 suppresses tumor suppression activity of let-7b-5p and targeting hsa-let-7b-5p could regulate MAD2L1, FOXM1 and lncRNA expression levels in LUAD. Additionally, a strong folding and binding energy was identified between the MAD2L1 gene and hsa-let-7b-5p. After analyzing the tumor microenvironment, we found that CD4 + T cells and B cells negatively correlate with the overexpression of MAD2L1. Conclusion The study indicates that MAD2L1 is overexpressed in females with LUAD, highlighting its potential as a molecular classifier and prognostic biomarker, and introduces a novel regulatory ceRNA network. MAD2L1 FOXM1 TMPO-AS1 hsa-let-7b-5p LUAD smoker females poor prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 ceRNA network can serve as molecular classifier and therapeutic target for LUAD. A miRNA, hsa-let-7b-5p is identified as a key regulator of a ceRNA network that involves MAD2L1-FOXM1 genes and TMPO-AS1 lncRNAs in LUAD Smoker. Both MAD2L1 and FOXM1 were strongly expressed during the G2/M transition phase and were closely associated with their respective CDKs and cyclins. MAD2L1 overexpression is significantly negatively correlated with the infiltration of CD4+ T and B cells into the tumor microenvironment. Introduction Lung cancer is a significant public health challenge, accounting for approximately 2.4 million new cases and 1.8 million deaths annually, according to the World Health Organization (Metintaş, 2023 ). It is the leading cause of cancer-related mortality, surpassing breast, prostate, and colorectal cancers combined. Lung cancer is a heterogeneous model consisting of several subtypes with distinct genetic, histological, and molecular characteristics. Non- Small Cell Lung Carcinoma (NSCLC), which makes up 80% of total lung cancer, is further divided into two types: lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (LUSC). Dysregulation of cell cycle regulators or cell cycle checkpoints, links tumor progression to the altered cell cycle, a hallmark for cancer. This abnormal proliferation leads to chromosomal instability (CIN), specifically affecting the G2/M phase and increasing cancer risk (Al-Rawi et al., 2024 ). Most transcripts encode proteins, with 98% belonging to non-protein-coding RNAs (ncRNAs) (Poliseno et al., 2024 ). These ncRNAs work with the mRNA to create a competitive endogenous RNA (ceRNA) network. This network controls gene expression by competing for shared miRNAs and influences many biological processes and disease mechanisms. This study aims to explore the prognostic significance and impact of MAD2L1 on patient survival in lung cancer cases, focusing on its regulatory mechanism, which is still unknown. MAD2L1 is a gene involved in the spindle assembly checkpoint, a crucial mechanism for accurate chromosome segregation during cell division (Wei et al., 2020 ). Dysregulation in MAD2L1 expression can lead to tumor formation, highlighting the need for further research on its regulatory role (Priyamvada and Ramaiah, 2024 ). Noncoding RNAs, particularly microRNAs (miRNAs), play a crucial role in regulating post-transcriptional gene expression and acting as tumor suppressors by binding to dysregulated mRNA and degrading it, inhibiting gene expression, and invading LUAD cells (Ahmad, 2022 ). The other type of ncRNA, long non-coding RNA (lncRNA) exhibits a sponging effect on miRNA, interrupting its regulatory role and causing overexpression of the gene. The novel study looks at the ceRNA network and the diagnostic and prognostic roles of MAD2L1, suggesting that targeting the FOXM1/MAD2L1/hsa-let-7b-5p/TMPO-AS1 ceRNA network could be very useful for diagnosing and treating LUAD. Methodology Survival Analysis The Kaplan-Meier plotter (KMP) (Győrffy, 2024 ) database was used to associate gene expression levels with lung cancer patient’s survival statuses. Patients were divided into low and high expression cohorts based on median survival. In-depth analysis using various univariate and multivariate parameters was employed to strengthen the data. MAD2L1's expression was observed in various survival statuses, including Overall Survival (OS), First Progression Survival (FP), Post Progression Survival (PPS), and OS based on histological subtypes (LUAD and LUSC). Further multivariate parameters included cancer stages, gender, and smoking history of the LUAD patients. The gene symbol and Affymetrix ID used for the analysis were "MAD2L1" and 203362_s_at, respectively. MAD2L1 Expression and Transcription Factor Profiling The study analyzed MAD2L1’s differential gene expression (DGE) levels in normal and LUAD conditions using various TCGA-based databases such as UALCAN (Chandrashekar et al., 2022 ), ENCORI (Li et al., 2014 ), Lung Cancer Explorer (Cai et al., 2019 ), OncoDB (Tang et al., 2024 ), and GEPIA2 (Tang et al., 2019 ). Further, TCGAnalyzeR (Zengin et al., 2024 ) database showed MAD2L1 expression in mRNA transcripts. By utilizing R-based programs, MAD2L1 expression data for LUAD was obtained from TCGA-LUAD dataset with sample normalizing maintaining an equivalent number of tumor (n = 59) and normal (n = 59) patients. Using a significance level of p < 0.05, the raw RNA-seq data was log2-transformed and statistical analysis was conducted using the Wilcoxon rank-sum test. By incorporating ggplot2 in R, the outcomes were seen to show the variations in MAD2L1 between tumor and normal tissues. Expression levels were analyzed based on various clinico-pathological parameters and protein expression levels, using UALCAN and CancerProteome (Lv et al., 2024 ) databases. The expression levels of MAD2L1 with cell cycle phases and related checkpoints were visualized using Cyclebase v3.0 (Santos et al., 2015 ) and ENCORI databases. Transcriptional factor (TF) profiling was done using Enrichr (Xie et al., 2021 ) database, particularly TRRUST, and correlations between TF and MAD2L1 were carried out using ENCORI, OncoDB, and GEPIA2 databases. The KM plotter database was used for survival analysis identification (Gene symbol, Affy ID: “FOXM1 (TGT3), 202580_x_at; E2F1, 204947_at), and expression levels were identified in cell cycle phases and related checkpoints using Cyclebase v3.0 and ENCORI databases. DGE assessments were conducted using UALCAN, ENCORI, and TCGAnalyzeR databases, including analysis of expression levels in relation to clinical parameters which was further validated by utilizing R-packages in normalized samples. MAD2L1-ceRNA Network Analysis The investigation focused on identifying regulatory molecules that influence the MAD2L1 gene expression at a molecular level. ncRNAs, including miRNAs and lncRNAs, were investigated. A network of MAD2L1 and associated miRNAs was constructed using the miRNet (Chang and Xia, 2023 ) database. Differential miRNA expression analysis was performed using the UALCAN database. The ENCORI database identified the miRNA closely associated to MAD2L1 in LUAD conditions. The KMP database assessed its prognostic significance, while the CancerMIRNome (Li et al., 2022 ) database facilitated the analysis of the area under the curve (AUC). Differential expression of selected miRNA was assessed using the UALCAN, CancerMIRNome databases and R-based packages with normalized sample numbers (n = 46), and expression levels were compared to clinical parameters. The Enrichr database assessed lncRNAs regulating MAD2L1 expression levels, their differential expression was analyzed using UALCAN database and the correlation between MAD2L1 and upregulated lncRNAs was analyzed using the ENCORI database. The survival analysis for the lncRNA was carried out using KMP database (Gene symbol, Affy ID: “TMPO-AS1, 227578_at”). Using UALCAN, ENCORI, OncoDB, and Lung Cancer Explorer databases, differential expression analysis of the chosen lncRNAs in LUAD was performed; further validation was obtained by means of R-packages in normalized samples. A ceRNA network was constructed using the miRNet database, and cell cycle checkpoint analysis was conducted using the ENCORI database. Database miRWalk and RNA22v2 were used to predict the binding affinities between the MAD2L1 gene and miRNA. MAD2L1 Gene Enrichment and Tumor-Infiltrating Immune Cells (TIICs) Analysis To investigate the immune cells associated with MAD2L1 in LUAD, immune cell infiltration analysis was conducted using multiple databases. The TIMER and TIMER 2.0 (Li et al., 2020 ) platforms were used to assess correlations between MAD2L1 expression and various immune cell types, employing algorithms such as XCELL, CIBERSORT, and EPIC for subtype-specific analysis. The GSCA database was utilized to correlate MAD2L1-associated genes with immune infiltration using GSVA-based scoring. Additionally, top co-expressed genes of MAD2L1 were identified through the Enrichr database, and their associations with immune infiltration were evaluated via TIMER to explore their relevance within the tumor microenvironment. Results High MAD2L1 Expression is Associated with Poor Overall Survival in LUAD Patients The prognostic significance of MAD2L1 as a predictive biomarker for lung cancer was initially analyzed using the KMP database, a statistical tool used to estimate patient survivability from datasets retrieved from TCGA, GEO, and Microarray, with several inclusion and exclusion criteria set to minimize errors (Supplementary Table 1) . The univariate analysis of three survival statuses, including OS, FP, and PPS, revealed that overexpression of MAD2L1 is linked to poor OS prognosis (HR = 1.53, CI = 1.36–1.73, P = 2.9e-12, low expression cohort = 91, high expression cohort = 48), FP (HR = 1.57, CI = 1.33–1.86, P = 1.5e-07, low expression cohort = 26.33, high expression cohort = 12 ) and PPS (HR = 1.39, CI = 1.13–1.72, P = 0.0017, low expression cohort = 19.5, high expression cohort = 10.27) as shown in Fig. 1 A-C, respectively. Further, survival analysis was performed in histological subtypes of lung cancer and the results showed significant association of MAD2L1 overexpression with poor survivability in LUAD patients (HR = 1.34, CI = 1.12–1.59, P = 0.001, low expression cohort = 95, high expression cohort = 69.93) as shown in Fig. 1 D, whereas, an insignificant data was observed in the cases of LUSC patients (HR = 1.03, CI = 0.85–1.25, P = 0.76, low expression cohort = 62.3, high expression cohort = 52) as depicted in Fig. 1 E. Following this, multivariate analysis was conducted on lung adenocarcinoma (LUAD) patients, focusing on OS across stages, gender, and smoking history. Results showed that patients diagnosed at Stage 1 LUAD had more significant values compared to Stage 2 and Stage 3 patients ( Supplementary Fig. 1A-C ). Gender also played a significant role in the analysis, with MAD2L1 overexpression being strongly associated with poor survival rates in females (P = 0.011) as shown in Fig. 1 F, than males (P = 0.057) ( Supplementary Fig. 1D ). LUAD patients having a history of smoking had a poorer prognosis (HR = 1.4, CI = 1.08–1.82, P = 0.011, low expression cohort = 93, high expression cohort = 71) as shown in Fig. 1 G, while non-smokers had shown insignificant data ( Supplementary Fig. 1E ). The association between LUAD smoker patients and gender revealed that smoker females were more affected (HR = 1.61, CI = 1.08–2.39, P = 0.018, low expression cohort = 96, high expression cohort = 62) as shown in Fig. 1 H, than smoker males ( Supplementary Fig. 1F ), while non-smoker males and females had insignificant data ( Supplementary Fig. 1G-H ). The study concluded that MAD2L1 shows greater variability among low and high expression cohorts and can be used as a predictive prognostic biomarker and molecular classifier for Lung Adenocarcinoma smoker females as mentioned in Table 1 . Table 1 Survival Analysis of MAD2L1 S.NO. Gene Index Patient Number Hazard Ratio CI Log(P) Low Expression Cohort High Expression Cohort 1 MAD2L1 OS 2166 1.53 1.36–1.73 2.9e-12 91 48 FP 1252 1.57 1.33–1.86 1.5e-07 26.33 12 PPS 477 1.39 1.13–1.72 0.0017 19.5 10.27 Histology 2 MAD2L1 Adenocarcinoma 1161 1.34 1.12–1.59 0.001 95 69.93 Squamous cell Carcinoma 780 1.03 0.85–1.25 0.76 62.3 52 OS + LUAD + Stages 3 MAD2L1 Stage1 370 1.53 1.03–2.28 0.035 72 46.2 Stage2 136 0.7 0.43–1.14 0.15 48 68.67 Stage3 24 0.96 0.34–2.78 0.95 34 37.97 OS + LUAD + Gender 4 MAD2L1 Male 566 1.26 0.99–1.59 0.057 79 60.73 Female 537 1.43 1.08–1.88 0.011 106 88.7 OS + LUAD + Smoking History 5 MAD2L1 Smoker 546 1.4 1.08–1.82 0.011 93 71 Non-Smoker 192 0.79 0.44–1.42 0.43 72 75.43 OS + LUAD + Smoking History + Gender 6 MAD2L1 Smoker Male 319 1.22 0.86–1.73 0.26 79 81 Female 227 1.61 1.08–2.39 0.018 96 62 Non-smokers Male 31 0.98 0.26–3.67 0.98 44 40.97 Female 161 0.81 0.42–1.57 0.54 72 75.43 MAD2L1 is Significantly Overexpressed in LUAD compared to Normal Tissue UALCAN and ENCORI databases revealed a substantial upregulation of MAD2L1 in LUAD tumor samples, indicating significant overexpression compared to healthy tissues. The fold change of 8.7 (P = < 1e-12) and 6.37(P = 4.7e-40) in UALCAN and ENCORI datasets explains the overexpression in LUAD tumor samples as shown in Fig. 2 A-B. Further our data was strengthened by using publicly available TCGA datasets like OncoDB, Lung Cancer Explorer, and GEPIA2, consistent overexpression of MAD2L1 was observed in malignant conditions as mentioned in Fig. 2 C-E. The MAD2L1 expression data was obtained from TCGA-LUAD dataset utilizing R-based packages, with sample sizes adjusted in both cohorts (Normal = 59, Tumor = 59). The box plot demonstrated a substantial upregulation of MAD2L1 in LUAD tumor tissues relative to normal tissues (p = 4.81e-17), as illustrated in Fig. 2 F. A single-cell RNA sequencing-based volcano plot for MAD2L1’s expression in mRNA transcripts showed MAD2L1 presence in the upregulated region, with a log fold change of 2.33 as mentioned in Fig. 2 G. Additionally, the study found that the MAD2L1 gene was significantly upregulated in males, smokers, and advanced nodal metastasis status in lung cancer patients by using UALCAN (Supplementary Fig. 2A-D ). The TP53 mutation in LUAD patients was linked to increased MAD2L1 overexpression contributing to tumor progression (Supplementary Fig. 2E-F) , identified by using UALCAN and TIMER2.0 databases. The gene's overexpression was further corroborated by its protein expression analysis using UALCAN and CancerProteome databases. A strong positive correlation was found between the MAD2L1 gene and protein expression (R = 0.764), indicating its continuous translation into protein ( Supplementary Fig. 2G-I) . The Cyclebase 3.0 database identified the cell cycle phase in which MAD2L1 shows significant expression and an increased MAD2L1 expression was found during the late G2 phase, suggesting its role in cell division and tumor progression during the G2/M transition as shown in Fig. 2 H. The ENCORI database also showed a strong positive correlation with the M phase CyclinB/CDK1 (R = 0.87, R = 0.88, respectively) in LUAD conditions ( Supplementary Table 2) . Overall, the differential gene and protein expression analysis demonstrated a substantial upregulation of MAD2L1 in LUAD samples. FOXM1 Identified as a Key Transcriptional Activator of MAD2L1 in LUAD Transcription factors are proteins that regulate gene expression, are crucial for cell proliferation and survival. Enrichr database was used to identify key regulators for MAD2L1 expression. Top 10 transcription factors, including E2F1, E2F4, E2F3, YBX1, RBL2, TP53, TRP53, FOXM1, ARID3A, and E2F4, were obtained (Supplementary Table 3) and their correlation values with MAD2L1 were established using the ENCORI database. Next, Supplementary Table 4 shows a positive correlation between MAD2L1 and various TFs, with FOXM1 and E2F1 being the most strongly and significantly correlated with MAD2L1 (R = 0.770, R = 0.604 respectively) as depicted in Fig. 3 A and Supplementary Fig. 3A . The R values indicate a strong correlation between these TFs. Further validation of the observation for FOXM1 and E2F1 was conducted using the OncoDB and GEPIA2 databases, revealing strong correlations for FOXM1 (R = 0.7775, R = 0.74, respectively) and E2F1 (R = 0.6069, R = 0.63, respectively) as shown in Fig. 3 B-C and Supplementary Fig. 3B-C , respectively. In addition, the study analyzed the significant TF correlated with MAD2L1 expression levels across cell cycle phases. FOXM1 expression was found during the M phase, potentially controlling mitotic progression as shown in Fig. 3 D, while E2F1 expression was found during the S phase (Supplementary Fig. 3D) . The expression of MAD2L1 was found in the G2/M transition phase of the cell cycle, suggesting FOXM1 regulates the M phase of the cell cycle and could be the most significant transcription factor regulating MAD2L1 compared to E2F1. Similarly, the correlation analysis of FOXM1 and Cyclin/CDKs in LUAD samples revealed a strong positive correlation with Cyclin B (R = 0.795), and CDK1 (R = 0.775), key M phase regulators ( Supplementary Table 5) , corroborating our findings. In addition, the study utilized the KM Plotter database to evaluate the prognostic significance of FOXM1 and E2F1 in LUAD patients. The survivability of LUAD patients, across different parameters such as OS (HR = 1.85, CI = 1.64–2.09, P < 1e-16, n = 2166), OS + LUAD (HR = 2.11, CI = 1.76–2.51, P < 1e-16, n = 1161), OS + LUAD + Smoker (HR = 2.2, CI = 1.68–2.88, P = 5.3e-09, n = 546), OS + LUAD + Smoker Male (HR = 2.08, CI = 1.38–3.14, P = 0.00035, n = 319) and OS + LUAD + Smoker Female (HR = 2.34, CI = 1.63–3.36, P = 2.2e-06, n = 227) as shown in Fig. 3 E-I, respectively showed significant overexpression of FOXM1 associated with poor prognosis. Similarly, survival analysis of E2F1 overexpression with the same parameters showed significant results as follow: OS (HR = 1.85, CI = 1.64–2.09, P < 1e-16, n = 2166), OS + LUAD (HR = 2.11, CI = 1.76–2.51, P < 1e-16, n = 1161), OS + LUAD + Smoker (HR = 2.2, CI = 1.68–2.88, P = 5.3e-09, n = 546), OS + LUAD + Smoker Male (HR = 2.08, CI = 1.38–3.14, P = 0.00035, n = 319) and OS + LUAD + Smoker Female (HR = 2.34, CI = 1.63–3.36, P = 2.2e-06, n = 227) (Supplementary Fig. 3E-I , respectively ) . Intriguingly, the high hazard ratios found in the cases of FOXM1 as compared to the E2F1 within the same sample size, suggests a higher prognostic significance of FOXM1 and hence was selected for further analysis. Additionally, DGE revealed significant (p < 0.05) as shown in Fig. 3 J. Furthermore, the R- based packages were utilized for analysis of FOXM1 expression in normalized samples (Normal = 59 and Tumor = 59) of LUAD. The results revealed a significant overexpression of FOXM1 in LUAD tumor tissues compared to normal tissues (p = 9.30e-18), as shown in Fig. 3 K. When TCGAnalyzeR database was incorporated for the LUAD condition its result showed that FOXM1 was also present in the upregulated region as MAD2L1, with a log fold change of 2.87 as shown in Fig. 3 L. Further FOXM1 gene expression analysis in association with clinical parameters based on patient gender, cancer stages, smoking history, and nodal metastasis, was conducted utilising the UALCAN database, and the boxplots showed significant FOXM1 upregulation with Males, advanced stages, smokers, and advanced nodal metastatic state, specifically N3, (Supplementary Fig. 4A-D) . The findings suggest that FOXM1 may have a higher prognostic significance in LUAD smoking patients. Construction of a Regulatory ceRNA Network Reveals hsa-let-7b-5p/TMPO-AS1 Axis Modulating MAD2L1 Expression The study investigated the regulatory mechanism governing MAD2L1 expression in LUAD using a ceRNA network analysis. miRNAs, small molecules with 18–22 bp, play a crucial role in post-translational regulation. A miRNet database was used to identify 19 miRNAs associated with both MAD2L1 and FOXM1, highlighting the interplay between non-coding RNAs, mRNAs and TF regulating gene expression ( Supplementary Fig. 4E ). Further, the UALCAN database was used for differential miRNA expression analyses, revealing six out of 19 miRNAs, including hsa-let-7b-5p, hsa-let-7g-5p, hsa-mir-138-5p, hsa-miR-221-3p, hsa-miR-29c-3p, and hsa-miR-98-5p, to be significantly downregulated in LUAD ( Supplementary Table 6 and Supplementary Fig. 4F-K ). Consequently, the Supplementary Table 7 reveals that hsa-let-7b-5p and hsa-mir-29c-3p, two miRNAs strongly negatively correlated with MAD2L1 and FOXM1, as revealed by the ENCORI database. Hence, hsa-let-7b-5p and hsa-mir-29c-3p were chosen for further analysis. The significant correlation was observed between hsa-let-7b-5p and MAD2L1 (R = -0.314), along with FOXM1 (R = -0.393), and between hsa-mir-29c-3p and MAD2L1 (R = -0.416), along with FOXM1 (R = -0.406) using ENCORI as illustrated in Fig. 4 A-D. The prognostic significance of hsa-let-7b-5p and hsa-mir-29c-3p was assessed using a survival curve obtained from KM Plotter. The results indicated that low expression of both hsa-let-7b-5p (HR = 0.72, CI = 0.54–0.96, P = 0.026) and hsa-mir-29c-3p (HR = 0.54, CI = 0.37–0.79, P = 0.0012) were significantly correlated with poor LUAD survival as shown in Fig. 4 E-F. AUC curves and ROC curves are crucial for assessing biomarkers like miRNAs' prognostic value. AUC curves show sensitivity versus specificity, while ROC curves show effectiveness. The AUC curves were obtained for both miRNAs using CancerMIRNome. hsa-let-7b-5p showed high prognostic accuracy with a narrow confidence interval (AUC = 0.89, CI = 0.85–0.92), while hsa-mir-29c-3p showed moderate accuracy with a wider confidence interval (AUC = 0.67, CI = 0.58–0.77). The results suggest that hsa-let-7b-5p may be a more precise and promising biomarker for LUAD as shown in Fig. 4 G-H. Further, UALCAN and CancerMIRNome databases revealed a significant downregulation of hsa-let-7b-5p in LUAD samples compared to normal tissues as shown in Fig. 4 I-J. Further expression analysis of hsa-let-7b-5p in normalized samples (Normal = 46 and Tumor = 46) carried out using R-based tools confirmed its notable downregulation in LUAD tumor tissues relative to normal tissues (p = 0.025), as shown in Fig. 4 K. The expression of hsa-let-7b-5p was assessed for various clinical parameters in LUAD using UALCAN and this downregulation was consistent in cancer stages, in patients with a smoking history compared to non-smokers and normal individuals, in males as compared to females and in advanced nodal metastasis stages (Supplementary Fig. 5A-D) . To validate the predicted interactions of hsa-let-7b-5p, a binding energy analysis was performed using miRWalk and RNA22v2 algorithms. The results revealed strong binding affinities of hsa-let-7b-5p with two key regulatory genes: MAD2L1 and FOXM1. Specifically, MAD2L1 exhibited a binding energy of -26.5 kcal/mol and a folding energy of -25.40 kcal/mol, while FOXM1 showed a binding energy of -21.5 kcal/mol and a folding energy of -17.90 kcal/mol, as shown in Table 2 ) . The heteroduplex structures further support this interaction, with multiple strong base-pairing regions observed. These findings reinforce the regulatory potential of hsa-let-7b-5p in LUAD, indicating that elevated LUAD progression correlates with reduced expression levels of hsa-let-7b-5p. Table 2: Binding and folding energies between hsa-let-7b-5p vs. MAD2L1, FOXM1 and TMPO-AS1 Further, the study analyzed the role of lncRNAs within the ceRNA network, which are larger than miRNAs and regulate mRNA expression levels. Supplementary Table 8 lists top 20 lncRNAs associated with MAD2L1, sourced from the Enrichr database. The UALCAN database was utilized to identify lncRNAs in LUAD samples, revealing 8 out of 20 significantly upregulated lncRNAs, as shown in Supplementary Table 9 . Subsequently, Supplementary Table 10 shows the correlations between upregulated lncRNAs and FOXM1, MAD2L1, and hsa-let-7b-5p. TMPO-AS1 showed a strong positive correlation with FOXM1 and MAD2L1, while a significant negative correlation with hsa-let-7b-5p was found. Using ENCORI database, the correlation graphs were generated between MAD2L1 and TMPO-AS1 (R = 0.659), FOXM1 and TMPO-AS1 (R = 0.581) and hsa-let-7b-5p and TMPO-AS1 (R = -0.277), as shown in Fig. 5 A-C. Additionally, the prognostic significance of TMPO-AS1 was evaluated using the KM Plotter database by using various clinical parameters such as OS (HR = 1.5, CI = 1.29–1.74, P = 7.2e-08), FP (HR = 1.9, CI = 1.52–2.38, P = 8.1e-09), PPS (HR = 1.77, CI = 1.32–2.39, P = 0.00013), OS + LUAD (HR = 2.16, CI = 1.69–2.76, P = 4.1e-10), OS + LUAD Smokers (HR = 2.34, CI = 1.41–3.88, P = 0.00066), OS + LUAD + Male + Smokers (HR = 2.33, CI = 1.28–4.25, P = 0.0046) as shown in Fig. 5 D-I. Further, TMPO-AS1 was analyzed in OS + LUAD + Stage1(HR = 2.65, CI = 1.72–4.09, P = 4.4e-06), OS + LUAD + Stage2 (HR = 2.1, CI = 1.22–3.61, P = 0.0061), OS + LUAD + Male (HR = 1.95, CI = 1.39–2.73, P = 9.1e-05), OS + LUAD + Female (HR = 2.83, CI = 1.84–4.35, P = 7.2e-07), OS + LUAD + Female Smokers (HR = 3.13, CI = 1.18–8.29, P = 0.16) (Supplementary Fig. 5E-I). The survival plots with high hazard ratios and significant difference in low and high expression cohorts (Supplementary Table 11) indicated that overexpression of TMPO-AS1 is associated with poor survival status in LUAD smoker patients. Subsequently, the significant upregulation in TMPO-AS1 expression levels was observed in LUAD as compared to normal tissues by using the UALCAN (P = 6.31e-53), OncoDB (P = 6.5e-53), ENCORI (P = 8.0e-26) and Lung Cancer Explorer (P = 8.5e-41) databases, as illustrated in Fig. 5 J-M. To validate the study, further expression analysis of TMPO-AS1 in normalized samples (Normal = 59 and Tumor = 59) performed using R-based packages demonstrated its considerable overexpression in LUAD tumor tissues relative to normal tissues (p = 1.05e-13), as illustrated in Fig. 5 N. Further, expression analysis based on cancer stages, smokers, and patient’s gender shows significant upregulation of TMPO-AS1 with advanced stages, in smoker patients, and males as compared to females, (Supplementary Fig. 5J-L) . This hypothesis was validated by a network created between MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 that was obtained from the miRNet database (Supplementary Fig. 6A) . To assess the interaction potential between hsa-let-7b-5p and lncRNA TMPO-AS1, a folding energy analysis was conducted using RNA22v2. The resulting folding energy was − 12.40 kcal/mol, indicating a moderately stable heteroduplex structure, as shown in Table 2 . The inverse correlation between the tumor suppressor hsa-let-7b-5p miRNA and the lncRNA TMPO-AS1 highlights TMPO-AS1's role as a molecular sponge, sequestering hsa-let-7b-5p, reducing its availability to target MAD2L1 and upregulating it. MAD2L1 is Enriched in Cell Cycle Pathways and Shows Progressive Upregulation Across LUAD Stages The gene expression levels of MAD2L1 across different functional states were analysed utilising the CanserSEA database as illustrated in Fig. 6 A. MAD2L1 expression exhibited significant correlations with cell cycle (R = 0.78), DNA repair (R = 0.71), DNA damage (R = 0.67), proliferation (R = 0.65) and invasion (R = 0.44) as depicted in Figs. 6 B-F. The GENI database was utilized for gene enrichment analysis validation to investigate biological processes, cellular components, and molecular functions associated with MAD2L1, FOXM1, and TMPO-AS1, which are crucial for tumour progression, particularly those linked to genomic instability and metastasis (Supplementary Fig. 6–8). The results showed a significant correlation between factors and cell cycle regulators, particularly those associated with the S and M phases. MAD2L1 and FOXM1 showed strong correlation with M phase CyclinB/CDK, while hsa-let-7b-5p and TMPO-AS1 also showed strong correlation with the M phase cell cycle checkpoints. Where CCNB1: hsa-let-7b-5p (R=-0.285), and TMPO-AS1 (R = 0.646); CDK1: hsa-let-7b-5p (R=-0.319), and TMPO-AS1 (R = 0.626) (Supplementary Table 12) . The study utilized a trend plot to analyze the expression patterns of key biomarkers in LUAD, focusing on the advanced stages using GSCA database. The plot showed a general trend of increasing expression of four genes: MKI67, FOXM1, MAD2L1, and TMPO-AS1 (Supplementary Fig. 9A) . FOXM1 showed an increase from Stage I to Stage IV, while MKI67 rose from Stage I to Stage II, followed by a slight decrease in Stage III. MAD2L1 showed an increase up to Stage II, followed by a slight increase from Stage III to Stage IV. TMPO-AS1 showed a progressive rise that could play a role in later stages. MAD2L1 has the potential to serve as a significant biomarker for the progression of LUAD, providing insights for prognosis. The GSVA (Gene Set Variation Analysis) score showed an upward trend in gene set activity as the disease advanced, indicating that the chosen gene set is progressively enriched or activated in advanced stages of LUAD (Supplementary Fig. 9B-D) . The trend plot was examined for all four genes in conjunction with the TP53 gene, revealing no significant change during the initial stages ( Supplementary Fig. 9E) . This suggests that these genes could be integral to the development and progression of LUAD. MAD2L1 Expression Correlates with Immune Cell Infiltration in LUAD Tumor immune infiltration plays a pivotal role in shaping the lung adenocarcinoma (LUAD) tumor microenvironment. Supplementary Fig. 10 presents a multi-step correlation analysis integrating data from TIMER, TIMER 2.0, and GSCA databases to explore the relationship between MAD2L1 expression and immune cell infiltration in LUAD. TIMER analysis indicates that among multiple immune subsets such as B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils and dendritic cells, CD4⁺ T cells (r=-0.196) and B cells (r=-0.209) display the strongest negative correlations with MAD2L1 expression, implicating a potential immune-evasive phenotype associated with high MAD2L1 levels (Supplementary Fig. 10A) . This trend was further validated by using TIMER 2.0 database, which confirmed that distinct subpopulations of CD4⁺ T cells including central memory, effector memory, and general CD4⁺ T cells as well as various B cell subtypes such as memory and class-switched B cells, consistently show inverse relationships with MAD2L1 expression levels ( Supplementary Fig. 10B-C) . These findings suggest a MAD2L1-mediated impairment of adaptive immunity within the LUAD microenvironment. Supporting this, GSCA analysis reveals a strong negative Spearman correlation (r =–0.61, FDR = 9.2e-54) between the CD4⁺ T cell infiltration and GSVA scores of MAD2L1 ( Supplementary Fig. 10D) . This data highlight MAD2L1 as a potential regulator of immune evasion, contributing to weakened T and B cell-mediated immunity in LUAD and thereby promoting tumor progression. To further investigate the immunosuppressive role of MAD2L1 in LUAD tumor microenvironment, we identified the top 10 co-expressed genes using the Enrichr database, including key mitotic regulators such as CCNA2, BIRC5, NEK2, EXO1, CHEK1, CDK1, CDC6, CCNB1, TTK, and NUF2. Correlation analysis with CD4⁺ T cell infiltration by using TIMER database revealed that all these genes showed a negative association, suggesting their collective involvement in modulating the tumor microenvironment ( Supplementary Fig. 11A-J) . This pattern highlights a potential link between cell cycle dysregulation and immune evasion, where high MAD2L1 expression and its co-regulated network contribute to an immune-excluded tumor microenvironment, reinforcing their value as early molecular classifiers and targets for immunomodulation in LUAD. Discussion Lung cancer is the leading cause of cancer-related mortality globally, but its molecular mechanism remains unclear (Torre et al., 2016 ). The aim of this study is to uncover novel ceRNA regulatory networks in lung cancer, specifically in LUAD, by using tissue-based datasets. MAD2L1, a crucial component of spindle assembly checkpoint, is located on chromosome 14 and has been found to cause chromosomal instability and overexpression in mouse embryos (Qiao et al., 2023 ). Studies have shown that both low and high MAD2L1 expression can lead to aneuploidy and tumorigenesis (Li et al., 2023a ). Recent evidence suggests that MAD2L1 is majorly overexpressed in tumors and overexpression can promote tumor formation in multiple cancer types (Xia et al., 2021 ). MAD2L1 has been linked to survival outcomes, gene expression control, and potential biomarker utility. Given its role in tumorigenesis and its overexpression in lung adenocarcinoma, MAD2L1 presents a promising target for personalized cancer therapies. By developing drugs that specifically inhibit MAD2L1, it may be possible to reduce tumor growth and improve patient outcomes. Additionally, assessing MAD2L1 expression levels could help tailor treatment strategies, offering a more personalized approach to cancer care. The study also explores the oncogenic potential of MAD2L1, its survival importance, differential expression as well as its associated transcription factors, ceRNA network interactions, and biological processes that are related to MAD2L1. Targeting these interactions could be a potential therapeutic approach, potentially modulating the expression of genes involved in tumor progression and inhibiting cancer cell growth. Identifying key transcription factors regulating MAD2L1 expression could also provide further therapeutic targets. According to Li et al., long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4. According to Li et al., long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4 (Li et al., 2019 ). El-Daly et al. has studied the Interplay of the PD-L1/MALT1/miR-200a axis during lung cancer development (El-Daly et al., 2025 ). Research has shown that breast cancer patients are diagnosed with higher histological grades, clinical stages, and early metastases due to overexpression of MAD2L1 in their tissues (Sun et al., 2013 ). Silencing MAD2L1 in liver cancer cells reduced cell proliferation in vitro and inhibited c-MYC-driven liver cancer development in vivo (Li et al., 2016 ). MAD2L1 has been identified as a cancer-driver gene in malignant pleural mesothelioma (Bisceglia et al., 2024 ), cholangiocarcinoma (Gao et al., 2021 ), and LUAD (Shi et al., 2016 ), with elevated expression associated with poor prognosis and potential therapeutic targets. High MAD2L1 expression is substantially associated with poor overall survival in lung cancer patients, particularly in LUAD. The oncogenic role of MAD2L1 has been demonstrated in breast (Sun et al., 2013 ) and colorectal malignancies (Li et al., 2023b ) through its overexpression . Furthermore, this study reveals that transcription factors, such as FOXM1, regulate MAD2L1, an oncogene known for its regulation of cell cycle-related genes, has been identified as a contributor to poor survival outcomes in various malignancies, including lung cancer. This makes it a promising biomarker in conjunction with MAD2L1. The strong correlation between MAD2L1 and FOXM1 in promoting tumor proliferation and progression is highlighted by the study's findings. The overexpression of MAD2L1 has been linked to cancer progression, but the molecular mechanisms involved, and its regulatory processes remain poorly understood. The study also focuses on the regulatory mechanisms and molecules involved with MAD2L1 in tumorigenesis, using the ceRNA mechanism, which involves lncRNAs facilitating the regulation of oncogenes by inhibiting miRNAs. The study also highlights the role of MAD2L1 in mitotic checkpoint genes, such as MAD2L1, and suggests that TMPO-AS1, upregulated in LUAD, functions as a molecular sponge by sequestering hsa-let-7b-5p, facilitating the overexpression of MAD2L1 and FOXM1. Subsequently the study also highlights the role of high expression of MAD2L1 in mitotic progression and genomic instability, underscoring its complex role in cancer biology. The study also indicates that MAD2L1 could influence the tumor microenvironment through the modulation of CD4 + Th2 immune responses. CD4 + Th2 immune responses are crucial for orchestrating the body's defense against extracellular pathogens, such as parasites (Chulanetra and Chaicumpa, 2021 ). They promote the production of antibodies by B cells and are involved in the regulation of allergic reactions (Catalán et al., 2021 ). In the context of cancer, these responses can influence tumor growth and progression by modulating the local immune environment. The correlation between MAD2L1 and CD4 + cells may yield novel understandings of immune-oncogenic interactions in LUAD. The results suggest possible therapeutic approaches aimed at MAD2L1, such as intervening in the MAD2L1-FOXM1 pathway or restoring miRNA concentrations. Small molecule inhibitors targeting MAD2L1 or its associated regulatory networks may present a new strategy for treating LUAD, backed by preclinical investigations in various other cancer types. In conclusion, the complexity of MAD2L1's regulatory ceRNA network underscores its importance as a prognostic biomarker and therapeutic target in LUAD, potentially leading to enhanced patient outcomes through personalized treatment strategies. Limitations The multi-omics data used in this study is insufficient to demonstrate the therapeutic benefits of MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 in clinical practice and trial stratification. Further research is needed to understand its unique activities in different tumor types. The study's limitations include variability in patient cohorts, data collection methods, and normalization processes, as well as insufficient clinical annotations and insufficiently annotated samples in publicly accessible datasets. Future research will focus on experimental validation and real-world data translation to overcome these limitations. Conclusion The study concludes that MAD2L1 overexpression is a significant high-risk factor for LUAD, particularly in smoker females, and is strongly correlated with FOXM1, TMPO-AS1, and hsa-let-7b-5p. The novel regulatory ceRNA network MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 could be a promising prognostic biomarker for LUAD smoker patients. Abbreviations MAD2L1 Mitotic Arrest-Deficient 2 Like1 CancerSEA Cancer Single Cell State Atlas LUAD Lung Adenocarcinoma LUSC Lung Squamous Cell Carcinoma CIN Chromosomal Instability ncRNAs Non-coding RNAs HR Hazard Ratio CI Confidence Interval TF Transcription Factor NSCLC Non- Small Cell Lung Cancer SCLC Small Cell Lung Cancer TP53 Tumor Protein p53 TRRUST Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining LBC Lepidic-predominant Adenocarcinoma ceRNA Competing Endogenous RNA DNA Deoxyribonucleic Acid E2F1 Eukaryotic Transcription Factor1 FOXM1 Forkhead box M1 ENCORI Enc yclopedia o f R NA I nteractomes ONCODB Oncology Database GEPIA2 Gene Expression Profiling Interactive Analysis hsa-let-7b Homosapiens MicroRNA Family miRNet MicroRNA Network Enrichr Enrichment Analysis Resource KMP Kaplan-Meier Plotter lncRNA long noncoding RNA miRNA Micro Ribonucleic Acid MKI67 Marker of Proliferation Ki-67 OS Overall Survival FP First Progression PPS Post Progression Survival GENI Global Environment for Network Innovations RNA Ribonucleic Acid TCGA Portal The Cancer Genomic Atlas Portal TCGAnalyzerv1.0 The Cancer Genome Altas Analyzer TIMER 2.0 Tumor Immune Estimation Resource TMPO-AS1 Thymopoietin Antisense Transcript1 TNM Plot Tumor Node Metastasis Plot UALCAN The University of Alabama At Birmingham Cancer Data Analysis Portal YBX1 Y-Box Binding Protein 1 RBL2 Retinoblastoma- Like Protein 2 ARID3A A-T rich interacting domain 3a Act_CD4+ Th2 Activated CD4+T-Helper 2 cells AUC Area Under the Curve ROC Receiver Operating Characteristic TIIC Tumor-Infiltrating Immune Cells NK Natural Killer cells CD8 Cluster of Differentiation 8 CD4 Cluster of Differentiation 4 EGFR Epidermal Growth Factor Receptor ALK Anaplastic Lymphoma Kinase APC Anaphase Promoting Complex EMT Epithelial Mesenchymal Transition GSCA Gene Set Cancer Analysis GSVA Gene Set Variation Analysis Declarations Conflicts of interest: The authors declare that they have no competing interests. Ethics Statement This study does not require ethical approval. Consent for publication: Not applicable. Funding Support: RN would like to thank the funding support from Manipal University Jaipur for the Enhanced Seed Grant under the Endowment Fund (No. E3/2023-24/QE-04-05). Author Contribution RN: Conception, study design, critical reading, intellectual assessment of the manuscript, preparation of the manuscript, and final approval. CS: Study design, and preparation of the manuscript, critical review BB: Study design, and preparation of the manuscript, critical review. PV: Study design, and preparation of the manuscript, critical review. SN: Data Analysis. Acknowledgement we expresses gratitude to Manipal University Jaipur for providing funding for the Enhanced Seed Grant under the Endowment Fund (No. E3/2023-24/QE-04-05). Data Availability Statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Ahmad, A., 2022. Epigenetic regulation of immunosuppressive tumor-associated macrophages through dysregulated microRNAs. Semin. Cell Dev. Biol., Special issue: MicroRNAs in Immunity and Cancer by Afsar Naqvi and Maryam Sarwat 124, 26–33. https://doi.org/10.1016/j.semcdb.2021.09.001 Al-Rawi, D.H., Lettera, E., Li, J., DiBona, M., Bakhoum, S.F., 2024. Targeting chromosomal instability in patients with cancer. Nat. Rev. Clin. Oncol. 21, 645–659. https://doi.org/10.1038/s41571-024-00923-w Bisceglia, L., Morani, F., Guerrieri, L., Santoni-Rugiu, E., Çakılkaya, P., Scatena, C., Scarpitta, R., Engelholm, L.H., Behrendt, N., Gemignani, F., Landi, S., 2024. BAG2, MAD2L1, and MDK are cancer-driver genes and candidate targets for novel therapies in malignant pleural mesothelioma. Cancer Gene Ther. 31, 1708–1720. https://doi.org/10.1038/s41417-024-00805-4 Cai, L., Lin, S., Girard, L., Zhou, Y., Yang, L., Ci, B., Zhou, Q., Luo, D., Yao, B., Tang, H., Allen, J., Huffman, K., Gazdar, A., Heymach, J., Wistuba, I., Xiao, G., Minna, J., Xie, Y., 2019. LCE: an open web portal to explore gene expression and clinical associations in lung cancer. Oncogene 38, 2551–2564. https://doi.org/10.1038/s41388-018-0588-2 Catalán, D., Mansilla, M.A., Ferrier, A., Soto, L., Oleinika, K., Aguillón, J.C., Aravena, O., 2021. Immunosuppressive Mechanisms of Regulatory B Cells. Front. Immunol. 12. https://doi.org/10.3389/fimmu.2021.611795 Chandrashekar, D.S., Karthikeyan, S.K., Korla, P.K., Patel, H., Shovon, A.R., Athar, M., Netto, G.J., Qin, Z.S., Kumar, S., Manne, U., Creighton, C.J., Varambally, S., 2022. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia N. Y. N 25, 18–27. https://doi.org/10.1016/j.neo.2022.01.001 Chang, L., Xia, J., 2023. MicroRNA Regulatory Network Analysis Using miRNet 2.0, in: Song, Q., Tao, Z. (Eds.), Transcription Factor Regulatory Networks. Springer US, New York, NY, pp. 185–204. https://doi.org/10.1007/978-1-0716-2815-7_14 Chulanetra, M., Chaicumpa, W., 2021. Revisiting the Mechanisms of Immune Evasion Employed by Human Parasites. Front. Cell. Infect. Microbiol. 11. https://doi.org/10.3389/fcimb.2021.702125 El-Daly, S.M., Abdelrahman, S.S., El-Bana, M.A., Abdel-Latif, Y., Medhat, D., Morsy, S.M., Wafay, H.A., 2025. Deciphering the Interplay of the PD-L1/MALT1/miR-200a Axis During Lung Cancer Development. Biotechnol. Appl. Biochem. https://doi.org/10.1002/bab.2724 Gao, Y., Liu, Y., Sun, L., Ouyang, X., Zhu, C., Qin, X., 2021. MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma. Genet. Test. Mol. Biomark. 25, 685–695. https://doi.org/10.1089/gtmb.2021.0122 Győrffy, B., 2024. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innov. Camb. Mass 5, 100625. https://doi.org/10.1016/j.xinn.2024.100625 Hayashi, A., Ruppo, S., Heilbrun, E.E., Mazzoni, C., Adar, S., Yassour, M., Rmaileh, A.A., Shaul, Y.D., 2023. GENI: A web server to identify gene set enrichments in tumor samples. Comput. Struct. Biotechnol. J. 21, 5531–5537. https://doi.org/10.1016/j.csbj.2023.10.053 Li, J.-H., Liu, S., Zhou, H., Qu, L.-H., Yang, J.-H., 2014. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42, D92–D97. https://doi.org/10.1093/nar/gkt1248 Li, Q., Tong, D., Jing, X., Ma, P., Li, F., Jiang, Q., Zhang, J., Wen, H., Cui, M., Huang, C., Zhang, M., 2023a. MAD2L1 is transcriptionally regulated by TEAD4 and promotes cell proliferation and migration in colorectal cancer. Cancer Gene Ther. 30, 727–737. https://doi.org/10.1038/s41417-022-00586-8 Li, Q., Tong, D., Jing, X., Ma, P., Li, F., Jiang, Q., Zhang, J., Wen, H., Cui, M., Huang, C., Zhang, M., 2023b. MAD2L1 is transcriptionally regulated by TEAD4 and promotes cell proliferation and migration in colorectal cancer. Cancer Gene Ther. 30, 727–737. https://doi.org/10.1038/s41417-022-00586-8 Li, R., Qu, H., Wang, S., Chater, J.M., Wang, X., Cui, Y., Yu, L., Zhou, R., Jia, Q., Traband, R., Wang, M., Xie, W., Yuan, D., Zhu, J., Zhong, W.-D., Jia, Z., 2022. CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer. Nucleic Acids Res. 50, D1139–D1146. https://doi.org/10.1093/nar/gkab784 Li, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B., Liu, X.S., 2020. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48, W509–W514. https://doi.org/10.1093/nar/gkaa407 Li, Y., Zhao, L., Zhao, P., Liu, Z., 2019. Long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4. Cancer Biomark. Sect. Dis. Markers 26, 79–91. https://doi.org/10.3233/CBM-190142 Li, Y., Zhu, Y., Prochownik, E.V., 2016. MicroRNA-based screens for synthetic lethal interactions with c-Myc. RNA Dis. Houst. Tex 3, e1330. https://doi.org/10.14800/rd.1330 Liu, C.-J., Hu, F.-F., Xie, G.-Y., Miao, Y.-R., Li, X.-W., Zeng, Y., Guo, A.-Y., 2023. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief. Bioinform. 24, bbac558. https://doi.org/10.1093/bib/bbac558 Lv, D., Li, D., Cai, Y., Guo, J., Chu, S., Yu, J., Liu, K., Jiang, T., Ding, N., Jin, X., Li, Y., Xu, J., 2024. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res. 52, D1155–D1162. https://doi.org/10.1093/nar/gkad824 Metintaş, S., 2023. Epidemiology of Lung Cancer, in: Cingi, C., Yorgancıoğlu, A., Bayar Muluk, N., Cruz, A.A. (Eds.), Airway Diseases. Springer International Publishing, Cham, pp. 939–983. https://doi.org/10.1007/978-3-031-22483-6_57-1 Poliseno, L., Lanza, M., Pandolfi, P.P., 2024. Coding, or non-coding, that is the question. Cell Res. 34, 609–629. https://doi.org/10.1038/s41422-024-00975-8 Priyamvada, P., Ramaiah, S., 2024. Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach. Biochem. Genet. 62, 2166–2194. https://doi.org/10.1007/s10528-023-10544-0 Qiao, J.-Y., Zhou, Q., Xu, K., Yue, W., Lei, W.-L., Li, Y.-Y., Gu, L.-J., Ouyang, Y.-C., Hou, Y., Schatten, H., Meng, T.-G., Wang, Z.-B., Sun, Q.-Y., 2023. Mad2 is dispensable for accurate chromosome segregation but becomes essential when oocytes are subjected to environmental stress. Development 150, dev201398. https://doi.org/10.1242/dev.201398 Ru, B., Wong, C.N., Tong, Y., Zhong, J.Y., Zhong, S.S.W., Wu, W.C., Chu, K.C., Wong, C.Y., Lau, C.Y., Chen, I., Chan, N.W., Zhang, J., 2019. TISIDB: an integrated repository portal for tumor–immune system interactions. Bioinformatics 35, 4200–4202. https://doi.org/10.1093/bioinformatics/btz210 Santos, A., Wernersson, R., Jensen, L.J., 2015. Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140–D1144. https://doi.org/10.1093/nar/gku1092 Shi, Y.-X., Zhu, T., Zou, T., Zhuo, W., Chen, Y.-X., Huang, M.-S., Zheng, W., Wang, C.-J., Li, X., Mao, X.-Y., Zhang, W., Zhou, H.-H., Yin, J.-Y., Liu, Z.-Q., 2016. Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma. Oncotarget 7, 85235–85243. https://doi.org/10.18632/oncotarget.13252 Sun, Q., Zhang, X., Liu, T., Liu, X., Geng, J., He, X., Liu, Y., Pang, D., 2013. Increased expression of Mitotic Arrest Deficient-Like 1 (MAD1L1) is associated with poor prognosis and insensitive to Taxol treatment in breast cancer. Breast Cancer Res. Treat. 140, 323–330. https://doi.org/10.1007/s10549-013-2633-8 Tang, G., Liu, X., Cho, M., Li, Y., Tran, D.-H., Wang, X., 2024. Pan-cancer discovery of somatic mutations from RNA sequencing data. Commun. Biol. 7, 1–12. https://doi.org/10.1038/s42003-024-06326-y Tang, Z., Kang, B., Li, C., Chen, T., Zhang, Z., 2019. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556–W560. https://doi.org/10.1093/nar/gkz430 Torre, L.A., Siegel, R.L., Jemal, A., 2016. Lung Cancer Statistics, in: Ahmad, A., Gadgeel, S. (Eds.), Lung Cancer and Personalized Medicine: Current Knowledge and Therapies. Springer International Publishing, Cham, pp. 1–19. https://doi.org/10.1007/978-3-319-24223-1_1 Wei, R., Wang, Z., Zhang, Y., Wang, B., Shen, N., E, L., Li, X., Shang, L., Shang, Y., Yan, W., Zhang, X., Ma, W., Wang, C., 2020. Bioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development. BMC Med. Genomics 13, 112. https://doi.org/10.1186/s12920-020-00762-5 Xia, T., Meng, L., Zhao, Z., Li, Y., Wen, H., Sun, H., Zhang, T., Wei, J., Li, F., Liu, C., 2021. Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma. Cancer Cell Int. 21, 634. https://doi.org/10.1186/s12935-021-02347-3 Xie, Z., Bailey, A., Kuleshov, M.V., Clarke, D.J.B., Evangelista, J.E., Jenkins, S.L., Lachmann, A., Wojciechowicz, M.L., Kropiwnicki, E., Jagodnik, K.M., Jeon, M., Ma’ayan, A., 2021. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 1, e90. https://doi.org/10.1002/cpz1.90 Yuan, H., Yan, M., Zhang, G., Liu, W., Deng, C., Liao, G., Xu, L., Luo, T., Yan, H., Long, Z., Shi, A., Zhao, T., Xiao, Y., Li, X., 2019. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 47, D900–D908. https://doi.org/10.1093/nar/gky939 Zengin, T., Masud, B.A., Önal-Süzek, T., 2024. TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery. Cancers 16, 345. https://doi.org/10.3390/cancers16020345 Additional Declarations No competing interests reported. Supplementary Files RevisedSupplementaryMaterial.docx Graphicalabstract.jpg Graphical Abstract The above figure demonstrates the regulatory interactions between long non-coding RNA (TMPO-AS1), microRNA (hsa-let-7b-5p) and their regulatory mechanism with a focus on their roles in lung adenocarcinoma progression. Figure A illustrates DNA-transcribed TMPO-AS1 containing miRNA response elements (MREs). Figure B shows DNA transcribed Pri-miRNAs, processed into Pre-miRNAs, then matured into mature miRNAs which binds to target mRNAs to regulate gene expression post-transcriptionally. Figure C demonstrates normal condition in which downregulated TMPO-AS1 cannot operate as miRNA sponge, upregulating hsa-let-7b-5p levels. The production of MAD2L1 mRNA transcripts takes place, regulated by the transcription factor FOXM1. Here, miRNAs can bind and degrade targeted mRNA MAD2L1 transcript, repressing its translation, thereby regulating MAD2L1 protein expression. Figure D showcases lung adenocarcinoma smoker conditions in which the elevated expression of TMPO-AS1 enables them to bind and sequester miRNAs, forming a TMPO-AS1 and hsa-let-7b-5p sponge, hence inhibiting miRNA binding with the MAD2L1 transcript. This leads to the increase of MAD2L1 mRNA and protein levels. Increased MAD2L1 protein levels facilitate the tumor progression which may be regulated by targeting TMPO-AS1 or hsa-let-7b-5p. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 May, 2025 Editor assigned by journal 06 May, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Submission checks completed at journal 18 Apr, 2025 First submitted to journal 15 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5794582","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445640864,"identity":"3526fa8c-1f01-4e19-9293-03fa536dab98","order_by":0,"name":"Chainsee Saini","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Chainsee","middleName":"","lastName":"Saini","suffix":""},{"id":445640865,"identity":"a6895663-28e0-440a-b79d-78e5b3c697dd","order_by":1,"name":"Prerna Vats","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Prerna","middleName":"","lastName":"Vats","suffix":""},{"id":445640866,"identity":"9e52ce21-ae73-4c92-bf7c-469ecd4f7bd9","order_by":2,"name":"Bhavika Baweja","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Bhavika","middleName":"","lastName":"Baweja","suffix":""},{"id":445640867,"identity":"2ad01237-5880-4e18-9b85-72e114d5a628","order_by":3,"name":"Sakshi Nirmal","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Sakshi","middleName":"","lastName":"Nirmal","suffix":""},{"id":445640868,"identity":"abd780c2-1128-4f3c-b341-de899a10c078","order_by":4,"name":"Rajeev Nema","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Rajeev","middleName":"","lastName":"Nema","suffix":""}],"badges":[],"createdAt":"2025-01-09 08:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5794582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5794582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81543647,"identity":"caf2e303-f6bd-4fbc-b63a-a358ffedebc8","added_by":"auto","created_at":"2025-04-28 11:24:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341570,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic significance of MAD2L1 expression in lung cancer patients. Kaplan-Meier survival curves were plotted for \u003cstrong\u003e(A) \u003c/strong\u003eOverall Survival (n=2166), \u003cstrong\u003e(B) \u003c/strong\u003eFirst Progression Survival (n=1252), \u003cstrong\u003e(C) \u003c/strong\u003ePost\u003cstrong\u003e \u003c/strong\u003eProgression Survival (n=477), \u003cstrong\u003e(D) \u003c/strong\u003eOS+LUAD (n=1161), \u003cstrong\u003e(E) \u003c/strong\u003eOS+LUSC (n=780), \u003cstrong\u003e(F) \u003c/strong\u003eOS+LUAD Female (n=537),\u003cstrong\u003e (G) \u003c/strong\u003eOS+LUAD Smoker (n=546), \u003cstrong\u003e(H) \u003c/strong\u003eOS+LUAD Female Smoker (n=227).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/46bbf9a0c5f8643f5c53f07b.jpg"},{"id":81543648,"identity":"47061f34-d49a-441c-af1d-da101fef52a3","added_by":"auto","created_at":"2025-04-28 11:24:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197054,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of mRNA in lung cancer. (\u003cstrong\u003eA-F)\u003c/strong\u003e MAD2L1 expression was examined in normal lung tissue and primary tumors from the available database in LUAD, \u003cstrong\u003e(A) \u003c/strong\u003eUALCAN (normal n=59, tumor n=515), \u003cstrong\u003e(B)\u003c/strong\u003e ENCORI (normal n=59, tumor n=526), \u003cstrong\u003e(C)\u003c/strong\u003e OncoDB (normal n=59, tumor n=540), \u003cstrong\u003e(D) \u003c/strong\u003eLung Cancer Explorer (normal n=59, tumor n=517), \u003cstrong\u003e(E) \u003c/strong\u003eGEPIA2 (normal n=347, tumor n=483), \u003cstrong\u003e(F)\u003c/strong\u003e R-based package (normal n=59, tumor n=59). \u003cstrong\u003e(G) \u003c/strong\u003eTranscriptome Analysis in LUAD using\u003cstrong\u003e \u003c/strong\u003eTCGAnalyzer. \u003cstrong\u003e(H) \u003c/strong\u003eCell cycle analysis using Cyclebase 3.0 database.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/db70c774abd01e2ff33a71de.jpg"},{"id":81543649,"identity":"d27c1f2c-ec7e-4b77-963d-2e4021e8f135","added_by":"auto","created_at":"2025-04-28 11:24:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333437,"visible":true,"origin":"","legend":"\u003cp\u003eTranscription Factor Analysis. Correlation analysis between MAD2L1 and FOXM1/TGT3 \u003cstrong\u003e(A) \u003c/strong\u003eENCORI, \u003cstrong\u003e(B)\u003c/strong\u003e OncoDB, \u003cstrong\u003e(C)\u003c/strong\u003e GEPIA2. \u003cstrong\u003e(D)\u003c/strong\u003e Cell cycle analysis of FOXM1 using Cyclebase 3.0 database. \u003cstrong\u003e(E-I) \u003c/strong\u003eSurvival analysis using KM Plotter \u003cstrong\u003e(E) \u003c/strong\u003eOS (n=2166), \u003cstrong\u003e(F)\u003c/strong\u003e OS+LUAD (n=1161), \u003cstrong\u003e(G)\u003c/strong\u003e OS+LUAD Smoker (n=546), \u003cstrong\u003e(H)\u003c/strong\u003e OS+LUAD Smoker Male (n=319), \u003cstrong\u003e(I)\u003c/strong\u003e OS+LUAD+Smoker Female (n=227). Differential gene expression analysis of FOXM1 in normal vs tumor tissues by using \u003cstrong\u003e(J) \u003c/strong\u003eUALCAN (normal n=59, tumor n=515), \u003cstrong\u003e(K) \u003c/strong\u003eR-based package (normal n=59, tumor n=59)\u003cstrong\u003e, \u003c/strong\u003eand \u003cstrong\u003e(L) \u003c/strong\u003eTranscriptome Analysis in LUAD using TCGAnalyzer.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/f5bc69b4162f9208216345af.jpg"},{"id":81541926,"identity":"d82642c5-2ee9-4cd7-b840-cc802bca22d3","added_by":"auto","created_at":"2025-04-28 11:16:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211322,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network analysis-miRNA. Correlation graphs between \u003cstrong\u003e(A) \u003c/strong\u003ehsa-let-7b-5p and MAD2L1, \u003cstrong\u003e(B) \u003c/strong\u003ehsa-let-7b-5p and FOXM1\u003cstrong\u003e, (C) \u003c/strong\u003ehsa-miR-29c-3p and MAD2L1,\u003cstrong\u003e (D) \u003c/strong\u003ehsa-miR-29c-3p and FOXM1 using ENCORI\u003cstrong\u003e. \u003c/strong\u003eSurvival analysis using KM Plotter: Overall Survival \u003cstrong\u003e(E) \u003c/strong\u003ehsa-let-7b-5p and \u003cstrong\u003e(F) \u003c/strong\u003ehsa-miR-29c-3p. AUC curve analysis for finding prognostic significance of miRNA \u003cstrong\u003e(G) \u003c/strong\u003ehsa-let-7b-5p and \u003cstrong\u003e(H) \u003c/strong\u003ehsa-miR-29c-3p using CancerMIRNome, and Differential gene expression analysis of hsa-let-7b-5p in normal vs tumor tissues by using \u003cstrong\u003e\u0026nbsp;(I) \u003c/strong\u003eUALCAN, \u003cstrong\u003e(J) \u003c/strong\u003eCancerMIRNome, and \u003cstrong\u003e(K)\u003c/strong\u003eR-based package (normal n=46, tumor n=46).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/302888a2fda2060fd87f1505.jpg"},{"id":81541928,"identity":"6cc34913-1acc-467f-aa3a-a3b1c49b6351","added_by":"auto","created_at":"2025-04-28 11:16:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":287223,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network analysis-lncRNA. Correlation graphs between \u003cstrong\u003e(A) \u003c/strong\u003eTMPO-AS1 and MAD2L1, \u003cstrong\u003e(B) \u003c/strong\u003eTMPO-AS1 and FOXM1\u003cstrong\u003e, (C) \u003c/strong\u003ehsa-let-7b-5p and TMPO-AS1 using ENCORI\u003cstrong\u003e. \u0026nbsp;\u003c/strong\u003eSurvival analysis of TMPO-AS1 using KM Plotter: \u003cstrong\u003e(D) \u003c/strong\u003eOS (n=1411),\u003cstrong\u003e (E) \u003c/strong\u003eFP (n=874),\u003cstrong\u003e (F) \u003c/strong\u003ePPS (n=241),\u003cstrong\u003e (G) \u003c/strong\u003eOS+LUAD (n=672),\u003cstrong\u003e (H) \u003c/strong\u003eOS+LUAD Smokers (n=231),\u003cstrong\u003e (I) \u003c/strong\u003eOS+LUAD+Male Smokers (n=162). Differential gene expression analysis of TMPO-AS1 in normal vs tumor tissues by using \u003cstrong\u003e(J) \u003c/strong\u003eUALCAN, \u003cstrong\u003e(K) \u003c/strong\u003eOncoDB\u003cstrong\u003e, (L) \u003c/strong\u003eENCORI \u003cstrong\u003e(M) \u003c/strong\u003eLung Cancer Explorer \u003cstrong\u003e(N) \u003c/strong\u003eR-based package (normal n=59, tumor n=59).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/b5432cc1afbaf9a7b75e0574.jpg"},{"id":81541931,"identity":"ad688963-ee02-4ed7-a8e4-304c1bd6718d","added_by":"auto","created_at":"2025-04-28 11:16:58","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":151271,"visible":true,"origin":"","legend":"\u003cp\u003eBiological Process Analysis. \u003cstrong\u003e(A) \u003c/strong\u003eAssociation of MAD2L1 expression with various biological processes using CancerSEA database. \u003cstrong\u003e(B-G) \u003c/strong\u003eCorrelation of MAD2L1 expression with \u003cstrong\u003e(B) \u003c/strong\u003eCell Cycle, \u003cstrong\u003e(C) \u003c/strong\u003eDNA repair, \u003cstrong\u003e(D) \u003c/strong\u003eDNA Damage, \u003cstrong\u003e(E) \u003c/strong\u003eProliferation, and \u003cstrong\u003e(F) \u003c/strong\u003eInvasion using CancerSEA.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/abffd8c50961c4344f3b9ca5.jpg"},{"id":81545587,"identity":"36adb6e0-6848-461d-843e-0d142a17debf","added_by":"auto","created_at":"2025-04-28 11:40:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3047662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/e8a99b2d-5a2b-4723-a487-d0c2b14ddbd2.pdf"},{"id":81544686,"identity":"c730c8d5-2bcf-4b69-a23a-bee091f5e3e9","added_by":"auto","created_at":"2025-04-28 11:32:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7257292,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/164a5ec7c78636252b490bfb.docx"},{"id":81541922,"identity":"1169fc1a-4467-441d-a5c0-8d4eb974856a","added_by":"auto","created_at":"2025-04-28 11:16:58","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":51873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above figure demonstrates the regulatory interactions between long non-coding RNA (TMPO-AS1), microRNA (hsa-let-7b-5p) and their regulatory mechanism with a focus on their roles in lung adenocarcinoma progression.\u003cstrong\u003e Figure A \u003c/strong\u003eillustrates DNA-transcribed TMPO-AS1 containing miRNA response elements (MREs). \u003cstrong\u003eFigure B \u003c/strong\u003eshows DNA transcribed Pri-miRNAs, processed into Pre-miRNAs, then matured into mature miRNAs which binds to target mRNAs to regulate gene expression post-transcriptionally. \u003cstrong\u003eFigure C \u003c/strong\u003edemonstrates normal condition in which downregulated TMPO-AS1 cannot operate as miRNA sponge, upregulating hsa-let-7b-5p levels. The production of MAD2L1 mRNA transcripts takes place, regulated by the transcription factor FOXM1. Here, miRNAs can bind and degrade targeted mRNA MAD2L1 transcript, repressing its translation, thereby regulating MAD2L1 protein expression. \u003cstrong\u003eFigure D \u003c/strong\u003eshowcases lung adenocarcinoma smoker conditions in which the elevated expression of TMPO-AS1 enables them to bind and sequester miRNAs, forming a TMPO-AS1 and hsa-let-7b-5p sponge, hence inhibiting miRNA binding with the MAD2L1 transcript. This leads to the increase of MAD2L1 mRNA and protein levels. Increased MAD2L1 protein levels facilitate the tumor progression which may be regulated by targeting TMPO-AS1 or hsa-let-7b-5p.\u003c/p\u003e","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5794582/v1/1d4414689b1f679a71c744b9.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"hsa-let-7b-5p/TMPO-AS1-mediated ceRNA networks are linked to poor prognosis for lung cancer patients with FOXM1/MAD2L1 Axis","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eMAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 ceRNA network can serve as molecular classifier and therapeutic target for LUAD.\u003c/li\u003e\n \u003cli\u003eA miRNA, hsa-let-7b-5p is identified as a key regulator of a ceRNA network that involves MAD2L1-FOXM1 genes and TMPO-AS1 lncRNAs in LUAD Smoker.\u003c/li\u003e\n \u003cli\u003eBoth MAD2L1 and FOXM1 were strongly expressed during the G2/M transition phase and were closely associated with their respective CDKs and cyclins.\u003c/li\u003e\n \u003cli\u003eMAD2L1 overexpression is significantly negatively correlated with the infiltration of CD4+ T and B cells into the tumor microenvironment.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eLung cancer is a significant public health challenge, accounting for approximately 2.4\u0026nbsp;million new cases and 1.8\u0026nbsp;million deaths annually, according to the World Health Organization (Metintaş, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is the leading cause of cancer-related mortality, surpassing breast, prostate, and colorectal cancers combined. Lung cancer is a heterogeneous model consisting of several subtypes with distinct genetic, histological, and molecular characteristics. Non- Small Cell Lung Carcinoma (NSCLC), which makes up 80% of total lung cancer, is further divided into two types: lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (LUSC). Dysregulation of cell cycle regulators or cell cycle checkpoints, links tumor progression to the altered cell cycle, a hallmark for cancer. This abnormal proliferation leads to chromosomal instability (CIN), specifically affecting the G2/M phase and increasing cancer risk (Al-Rawi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Most transcripts encode proteins, with 98% belonging to non-protein-coding RNAs (ncRNAs) (Poliseno et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These ncRNAs work with the mRNA to create a competitive endogenous RNA (ceRNA) network. This network controls gene expression by competing for shared miRNAs and influences many biological processes and disease mechanisms. This study aims to explore the prognostic significance and impact of MAD2L1 on patient survival in lung cancer cases, focusing on its regulatory mechanism, which is still unknown. MAD2L1 is a gene involved in the spindle assembly checkpoint, a crucial mechanism for accurate chromosome segregation during cell division (Wei et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Dysregulation in MAD2L1 expression can lead to tumor formation, highlighting the need for further research on its regulatory role (Priyamvada and Ramaiah, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Noncoding RNAs, particularly microRNAs (miRNAs), play a crucial role in regulating post-transcriptional gene expression and acting as tumor suppressors by binding to dysregulated mRNA and degrading it, inhibiting gene expression, and invading LUAD cells (Ahmad, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The other type of ncRNA, long non-coding RNA (lncRNA) exhibits a sponging effect on miRNA, interrupting its regulatory role and causing overexpression of the gene. The novel study looks at the ceRNA network and the diagnostic and prognostic roles of MAD2L1, suggesting that targeting the FOXM1/MAD2L1/hsa-let-7b-5p/TMPO-AS1 ceRNA network could be very useful for diagnosing and treating LUAD.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier plotter (KMP) (Győrffy, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) database was used to associate gene expression levels with lung cancer patient\u0026rsquo;s survival statuses. Patients were divided into low and high expression cohorts based on median survival. In-depth analysis using various univariate and multivariate parameters was employed to strengthen the data. MAD2L1's expression was observed in various survival statuses, including Overall Survival (OS), First Progression Survival (FP), Post Progression Survival (PPS), and OS based on histological subtypes (LUAD and LUSC). Further multivariate parameters included cancer stages, gender, and smoking history of the LUAD patients. The gene symbol and Affymetrix ID used for the analysis were \"MAD2L1\" and 203362_s_at, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMAD2L1 Expression and Transcription Factor Profiling\u003c/h3\u003e\n\u003cp\u003eThe study analyzed MAD2L1\u0026rsquo;s differential gene expression (DGE) levels in normal and LUAD conditions using various TCGA-based databases such as UALCAN (Chandrashekar et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ENCORI (Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Lung Cancer Explorer (Cai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), OncoDB (Tang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and GEPIA2 (Tang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Further, TCGAnalyzeR (Zengin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) database showed MAD2L1 expression in mRNA transcripts. By utilizing R-based programs, MAD2L1 expression data for LUAD was obtained from TCGA-LUAD dataset with sample normalizing maintaining an equivalent number of tumor (n\u0026thinsp;=\u0026thinsp;59) and normal (n\u0026thinsp;=\u0026thinsp;59) patients. Using a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the raw RNA-seq data was log2-transformed and statistical analysis was conducted using the Wilcoxon rank-sum test. By incorporating ggplot2 in R, the outcomes were seen to show the variations in MAD2L1 between tumor and normal tissues. Expression levels were analyzed based on various clinico-pathological parameters and protein expression levels, using UALCAN and CancerProteome (Lv et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) databases. The expression levels of MAD2L1 with cell cycle phases and related checkpoints were visualized using Cyclebase v3.0 (Santos et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and ENCORI databases. Transcriptional factor (TF) profiling was done using Enrichr (Xie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) database, particularly TRRUST, and correlations between TF and MAD2L1 were carried out using ENCORI, OncoDB, and GEPIA2 databases. The KM plotter database was used for survival analysis identification (Gene symbol, Affy ID: \u0026ldquo;FOXM1 (TGT3), 202580_x_at; E2F1, 204947_at), and expression levels were identified in cell cycle phases and related checkpoints using Cyclebase v3.0 and ENCORI databases. DGE assessments were conducted using UALCAN, ENCORI, and TCGAnalyzeR databases, including analysis of expression levels in relation to clinical parameters which was further validated by utilizing R-packages in normalized samples.\u003c/p\u003e\n\u003ch3\u003eMAD2L1-ceRNA Network Analysis\u003c/h3\u003e\n\u003cp\u003eThe investigation focused on identifying regulatory molecules that influence the MAD2L1 gene expression at a molecular level. ncRNAs, including miRNAs and lncRNAs, were investigated. A network of MAD2L1 and associated miRNAs was constructed using the miRNet (Chang and Xia, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) database. Differential miRNA expression analysis was performed using the UALCAN database. The ENCORI database identified the miRNA closely associated to MAD2L1 in LUAD conditions. The KMP database assessed its prognostic significance, while the CancerMIRNome (Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) database facilitated the analysis of the area under the curve (AUC). Differential expression of selected miRNA was assessed using the UALCAN, CancerMIRNome databases and R-based packages with normalized sample numbers (n\u0026thinsp;=\u0026thinsp;46), and expression levels were compared to clinical parameters. The Enrichr database assessed lncRNAs regulating MAD2L1 expression levels, their differential expression was analyzed using UALCAN database and the correlation between MAD2L1 and upregulated lncRNAs was analyzed using the ENCORI database. The survival analysis for the lncRNA was carried out using KMP database (Gene symbol, Affy ID: \u0026ldquo;TMPO-AS1, 227578_at\u0026rdquo;). Using UALCAN, ENCORI, OncoDB, and Lung Cancer Explorer databases, differential expression analysis of the chosen lncRNAs in LUAD was performed; further validation was obtained by means of R-packages in normalized samples. A ceRNA network was constructed using the miRNet database, and cell cycle checkpoint analysis was conducted using the ENCORI database. Database miRWalk and RNA22v2 were used to predict the binding affinities between the MAD2L1 gene and miRNA.\u003c/p\u003e\n\u003ch3\u003eMAD2L1 Gene Enrichment and Tumor-Infiltrating Immune Cells (TIICs) Analysis\u003c/h3\u003e\n\u003cp\u003eTo investigate the immune cells associated with MAD2L1 in LUAD, immune cell infiltration analysis was conducted using multiple databases. The TIMER and TIMER 2.0 (Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) platforms were used to assess correlations between MAD2L1 expression and various immune cell types, employing algorithms such as XCELL, CIBERSORT, and EPIC for subtype-specific analysis. The GSCA database was utilized to correlate MAD2L1-associated genes with immune infiltration using GSVA-based scoring. Additionally, top co-expressed genes of MAD2L1 were identified through the Enrichr database, and their associations with immune infiltration were evaluated via TIMER to explore their relevance within the tumor microenvironment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHigh MAD2L1 Expression is Associated with Poor Overall Survival in LUAD Patients\u003c/h2\u003e \u003cp\u003eThe prognostic significance of MAD2L1 as a predictive biomarker for lung cancer was initially analyzed using the KMP database, a statistical tool used to estimate patient survivability from datasets retrieved from TCGA, GEO, and Microarray, with several inclusion and exclusion criteria set to minimize errors \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e. The univariate analysis of three survival statuses, including OS, FP, and PPS, revealed that overexpression of MAD2L1 is linked to poor OS prognosis (HR\u0026thinsp;=\u0026thinsp;1.53, CI\u0026thinsp;=\u0026thinsp;1.36\u0026ndash;1.73, P\u0026thinsp;=\u0026thinsp;2.9e-12, low expression cohort\u0026thinsp;=\u0026thinsp;91, high expression cohort\u0026thinsp;=\u0026thinsp;48), FP (HR\u0026thinsp;=\u0026thinsp;1.57, CI\u0026thinsp;=\u0026thinsp;1.33\u0026ndash;1.86, P\u0026thinsp;=\u0026thinsp;1.5e-07, low expression cohort\u0026thinsp;=\u0026thinsp;26.33, high expression cohort\u0026thinsp;=\u0026thinsp;12 ) and PPS (HR\u0026thinsp;=\u0026thinsp;1.39, CI\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;1.72, P\u0026thinsp;=\u0026thinsp;0.0017, low expression cohort\u0026thinsp;=\u0026thinsp;19.5, high expression cohort\u0026thinsp;=\u0026thinsp;10.27) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C, respectively. Further, survival analysis was performed in histological subtypes of lung cancer and the results showed significant association of MAD2L1 overexpression with poor survivability in LUAD patients (HR\u0026thinsp;=\u0026thinsp;1.34, CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;1.59, P\u0026thinsp;=\u0026thinsp;0.001, low expression cohort\u0026thinsp;=\u0026thinsp;95, high expression cohort\u0026thinsp;=\u0026thinsp;69.93) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, whereas, an insignificant data was observed in the cases of LUSC patients (HR\u0026thinsp;=\u0026thinsp;1.03, CI\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;1.25, P\u0026thinsp;=\u0026thinsp;0.76, low expression cohort\u0026thinsp;=\u0026thinsp;62.3, high expression cohort\u0026thinsp;=\u0026thinsp;52) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE.\u003c/p\u003e \u003cp\u003eFollowing this, multivariate analysis was conducted on lung adenocarcinoma (LUAD) patients, focusing on OS across stages, gender, and smoking history. Results showed that patients diagnosed at Stage 1 LUAD had more significant values compared to Stage 2 and Stage 3 patients (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A-C\u003c/b\u003e). Gender also played a significant role in the analysis, with MAD2L1 overexpression being strongly associated with poor survival rates in females (P\u0026thinsp;=\u0026thinsp;0.011) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, than males (P\u0026thinsp;=\u0026thinsp;0.057) (\u003cb\u003eSupplementary Fig.\u0026nbsp;1D\u003c/b\u003e). LUAD patients having a history of smoking had a poorer prognosis (HR\u0026thinsp;=\u0026thinsp;1.4, CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.82, P\u0026thinsp;=\u0026thinsp;0.011, low expression cohort\u0026thinsp;=\u0026thinsp;93, high expression cohort\u0026thinsp;=\u0026thinsp;71) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, while non-smokers had shown insignificant data (\u003cb\u003eSupplementary Fig.\u0026nbsp;1E\u003c/b\u003e). The association between LUAD smoker patients and gender revealed that smoker females were more affected (HR\u0026thinsp;=\u0026thinsp;1.61, CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;2.39, P\u0026thinsp;=\u0026thinsp;0.018, low expression cohort\u0026thinsp;=\u0026thinsp;96, high expression cohort\u0026thinsp;=\u0026thinsp;62) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, than smoker males (\u003cb\u003eSupplementary Fig.\u0026nbsp;1F\u003c/b\u003e), while non-smoker males and females had insignificant data (\u003cb\u003eSupplementary Fig.\u0026nbsp;1G-H\u003c/b\u003e). The study concluded that MAD2L1 shows greater variability among low and high expression cohorts and can be used as a predictive prognostic biomarker and molecular classifier for Lung Adenocarcinoma smoker females as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvival Analysis of MAD2L1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.NO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatient Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLog(P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLow Expression Cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh Expression Cohort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.36\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.33\u0026ndash;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5e-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026ndash;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e69.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSquamous cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eOS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Stages\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStage1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStage2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e68.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStage3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u0026ndash;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eOS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Gender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u0026ndash;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eOS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoking History\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026ndash;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNon-Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u0026ndash;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e75.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eOS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoking History\u0026thinsp;+\u0026thinsp;Gender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMAD2L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.08\u0026ndash;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNon-smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u0026ndash;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e75.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMAD2L1 is Significantly Overexpressed in LUAD compared to Normal Tissue\u003c/h3\u003e\n\u003cp\u003eUALCAN and ENCORI databases revealed a substantial upregulation of MAD2L1 in LUAD tumor samples, indicating significant overexpression compared to healthy tissues. The fold change of 8.7 (P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;1e-12) and 6.37(P\u0026thinsp;=\u0026thinsp;4.7e-40) in UALCAN and ENCORI datasets explains the overexpression in LUAD tumor samples as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B. Further our data was strengthened by using publicly available TCGA datasets like OncoDB, Lung Cancer Explorer, and GEPIA2, consistent overexpression of MAD2L1 was observed in malignant conditions as mentioned in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-E. The MAD2L1 expression data was obtained from TCGA-LUAD dataset utilizing R-based packages, with sample sizes adjusted in both cohorts (Normal\u0026thinsp;=\u0026thinsp;59, Tumor\u0026thinsp;=\u0026thinsp;59). The box plot demonstrated a substantial upregulation of MAD2L1 in LUAD tumor tissues relative to normal tissues (p\u0026thinsp;=\u0026thinsp;4.81e-17), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF. A single-cell RNA sequencing-based volcano plot for MAD2L1\u0026rsquo;s expression in mRNA transcripts showed MAD2L1 presence in the upregulated region, with a log fold change of 2.33 as mentioned in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG.\u003c/p\u003e \u003cp\u003eAdditionally, the study found that the MAD2L1 gene was significantly upregulated in males, smokers, and advanced nodal metastasis status in lung cancer patients by using UALCAN \u003cb\u003e(Supplementary Fig.\u0026nbsp;2A-D\u003c/b\u003e). The TP53 mutation in LUAD patients was linked to increased MAD2L1 overexpression contributing to tumor progression \u003cb\u003e(Supplementary Fig.\u0026nbsp;2E-F)\u003c/b\u003e, identified by using UALCAN and TIMER2.0 databases. The gene's overexpression was further corroborated by its protein expression analysis using UALCAN and CancerProteome databases. A strong positive correlation was found between the MAD2L1 gene and protein expression (R\u0026thinsp;=\u0026thinsp;0.764), indicating its continuous translation into protein (\u003cb\u003eSupplementary Fig.\u0026nbsp;2G-I)\u003c/b\u003e. The Cyclebase 3.0 database identified the cell cycle phase in which MAD2L1 shows significant expression and an increased MAD2L1 expression was found during the late G2 phase, suggesting its role in cell division and tumor progression during the G2/M transition as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH. The ENCORI database also showed a strong positive correlation with the M phase CyclinB/CDK1 (R\u0026thinsp;=\u0026thinsp;0.87, R\u0026thinsp;=\u0026thinsp;0.88, respectively) in LUAD conditions (\u003cb\u003eSupplementary Table\u0026nbsp;2)\u003c/b\u003e. Overall, the differential gene and protein expression analysis demonstrated a substantial upregulation of MAD2L1 in LUAD samples.\u003c/p\u003e\n\u003ch3\u003eFOXM1 Identified as a Key Transcriptional Activator of MAD2L1 in LUAD\u003c/h3\u003e\n\u003cp\u003eTranscription factors are proteins that regulate gene expression, are crucial for cell proliferation and survival. Enrichr database was used to identify key regulators for MAD2L1 expression. Top 10 transcription factors, including E2F1, E2F4, E2F3, YBX1, RBL2, TP53, TRP53, FOXM1, ARID3A, and E2F4, were obtained \u003cb\u003e(Supplementary Table\u0026nbsp;3)\u003c/b\u003e and their correlation values with MAD2L1 were established using the ENCORI database. Next, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e shows a positive correlation between MAD2L1 and various TFs, with FOXM1 and E2F1 being the most strongly and significantly correlated with MAD2L1 (R\u0026thinsp;=\u0026thinsp;0.770, R\u0026thinsp;=\u0026thinsp;0.604 respectively) as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand Supplementary Fig.\u0026nbsp;3A\u003c/b\u003e. The R values indicate a strong correlation between these TFs. Further validation of the observation for FOXM1 and E2F1 was conducted using the OncoDB and GEPIA2 databases, revealing strong correlations for FOXM1 (R\u0026thinsp;=\u0026thinsp;0.7775, R\u0026thinsp;=\u0026thinsp;0.74, respectively) and E2F1 (R\u0026thinsp;=\u0026thinsp;0.6069, R\u0026thinsp;=\u0026thinsp;0.63, respectively) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C and \u003cb\u003eSupplementary Fig.\u0026nbsp;3B-C\u003c/b\u003e, respectively.\u003c/p\u003e \u003cp\u003eIn addition, the study analyzed the significant TF correlated with MAD2L1 expression levels across cell cycle phases. FOXM1 expression was found during the M phase, potentially controlling mitotic progression as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, while E2F1 expression was found during the S phase \u003cb\u003e(Supplementary Fig.\u0026nbsp;3D)\u003c/b\u003e. The expression of MAD2L1 was found in the G2/M transition phase of the cell cycle, suggesting FOXM1 regulates the M phase of the cell cycle and could be the most significant transcription factor regulating MAD2L1 compared to E2F1. Similarly, the correlation analysis of FOXM1 and Cyclin/CDKs in LUAD samples revealed a strong positive correlation with Cyclin B (R\u0026thinsp;=\u0026thinsp;0.795), and CDK1 (R\u0026thinsp;=\u0026thinsp;0.775), key M phase regulators (\u003cb\u003eSupplementary Table\u0026nbsp;5)\u003c/b\u003e, corroborating our findings.\u003c/p\u003e \u003cp\u003eIn addition, the study utilized the KM Plotter database to evaluate the prognostic significance of FOXM1 and E2F1 in LUAD patients. The survivability of LUAD patients, across different parameters such as OS (HR\u0026thinsp;=\u0026thinsp;1.85, CI\u0026thinsp;=\u0026thinsp;1.64\u0026ndash;2.09, P\u0026thinsp;\u0026lt;\u0026thinsp;1e-16, n\u0026thinsp;=\u0026thinsp;2166), OS\u0026thinsp;+\u0026thinsp;LUAD (HR\u0026thinsp;=\u0026thinsp;2.11, CI\u0026thinsp;=\u0026thinsp;1.76\u0026ndash;2.51, P\u0026thinsp;\u0026lt;\u0026thinsp;1e-16, n\u0026thinsp;=\u0026thinsp;1161), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker (HR\u0026thinsp;=\u0026thinsp;2.2, CI\u0026thinsp;=\u0026thinsp;1.68\u0026ndash;2.88, P\u0026thinsp;=\u0026thinsp;5.3e-09, n\u0026thinsp;=\u0026thinsp;546), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker Male (HR\u0026thinsp;=\u0026thinsp;2.08, CI\u0026thinsp;=\u0026thinsp;1.38\u0026ndash;3.14, P\u0026thinsp;=\u0026thinsp;0.00035, n\u0026thinsp;=\u0026thinsp;319) and OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker Female (HR\u0026thinsp;=\u0026thinsp;2.34, CI\u0026thinsp;=\u0026thinsp;1.63\u0026ndash;3.36, P\u0026thinsp;=\u0026thinsp;2.2e-06, n\u0026thinsp;=\u0026thinsp;227) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-I, respectively showed significant overexpression of FOXM1 associated with poor prognosis. Similarly, survival analysis of E2F1 overexpression with the same parameters showed significant results as follow: OS (HR\u0026thinsp;=\u0026thinsp;1.85, CI\u0026thinsp;=\u0026thinsp;1.64\u0026ndash;2.09, P\u0026thinsp;\u0026lt;\u0026thinsp;1e-16, n\u0026thinsp;=\u0026thinsp;2166), OS\u0026thinsp;+\u0026thinsp;LUAD (HR\u0026thinsp;=\u0026thinsp;2.11, CI\u0026thinsp;=\u0026thinsp;1.76\u0026ndash;2.51, P\u0026thinsp;\u0026lt;\u0026thinsp;1e-16, n\u0026thinsp;=\u0026thinsp;1161), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker (HR\u0026thinsp;=\u0026thinsp;2.2, CI\u0026thinsp;=\u0026thinsp;1.68\u0026ndash;2.88, P\u0026thinsp;=\u0026thinsp;5.3e-09, n\u0026thinsp;=\u0026thinsp;546), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker Male (HR\u0026thinsp;=\u0026thinsp;2.08, CI\u0026thinsp;=\u0026thinsp;1.38\u0026ndash;3.14, P\u0026thinsp;=\u0026thinsp;0.00035, n\u0026thinsp;=\u0026thinsp;319) and OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Smoker Female (HR\u0026thinsp;=\u0026thinsp;2.34, CI\u0026thinsp;=\u0026thinsp;1.63\u0026ndash;3.36, P\u0026thinsp;=\u0026thinsp;2.2e-06, n\u0026thinsp;=\u0026thinsp;227) \u003cb\u003e(Supplementary Fig.\u0026nbsp;3E-I\u003c/b\u003e, respectively\u003cb\u003e)\u003c/b\u003e. Intriguingly, the high hazard ratios found in the cases of FOXM1 as compared to the E2F1 within the same sample size, suggests a higher prognostic significance of FOXM1 and hence was selected for further analysis. Additionally, DGE revealed significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ. Furthermore, the R- based packages were utilized for analysis of FOXM1 expression in normalized samples (Normal\u0026thinsp;=\u0026thinsp;59 and Tumor\u0026thinsp;=\u0026thinsp;59) of LUAD. The results revealed a significant overexpression of FOXM1 in LUAD tumor tissues compared to normal tissues (p\u0026thinsp;=\u0026thinsp;9.30e-18), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK. When TCGAnalyzeR database was incorporated for the LUAD condition its result showed that FOXM1 was also present in the upregulated region as MAD2L1, with a log fold change of 2.87 as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL. Further FOXM1 gene expression analysis in association with clinical parameters based on patient gender, cancer stages, smoking history, and nodal metastasis, was conducted utilising the UALCAN database, and the boxplots showed significant FOXM1 upregulation with Males, advanced stages, smokers, and advanced nodal metastatic state, specifically N3, \u003cb\u003e(Supplementary Fig.\u0026nbsp;4A-D)\u003c/b\u003e. The findings suggest that FOXM1 may have a higher prognostic significance in LUAD smoking patients.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a Regulatory ceRNA Network Reveals hsa-let-7b-5p/TMPO-AS1 Axis Modulating MAD2L1 Expression\u003c/h2\u003e \u003cp\u003eThe study investigated the regulatory mechanism governing MAD2L1 expression in LUAD using a ceRNA network analysis. miRNAs, small molecules with 18\u0026ndash;22 bp, play a crucial role in post-translational regulation. A miRNet database was used to identify 19 miRNAs associated with both MAD2L1 and FOXM1, highlighting the interplay between non-coding RNAs, mRNAs and TF regulating gene expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;4E\u003c/b\u003e). Further, the UALCAN database was used for differential miRNA expression analyses, revealing six out of 19 miRNAs, including hsa-let-7b-5p, hsa-let-7g-5p, hsa-mir-138-5p, hsa-miR-221-3p, hsa-miR-29c-3p, and hsa-miR-98-5p, to be significantly downregulated in LUAD (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;4F-K\u003c/b\u003e). Consequently, the \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e reveals that hsa-let-7b-5p and hsa-mir-29c-3p, two miRNAs strongly negatively correlated with MAD2L1 and FOXM1, as revealed by the ENCORI database. Hence, hsa-let-7b-5p and hsa-mir-29c-3p were chosen for further analysis. The significant correlation was observed between hsa-let-7b-5p and MAD2L1 (R = -0.314), along with FOXM1 (R = -0.393), and between hsa-mir-29c-3p and MAD2L1 (R = -0.416), along with FOXM1 (R = -0.406) using ENCORI as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D. The prognostic significance of hsa-let-7b-5p and hsa-mir-29c-3p was assessed using a survival curve obtained from KM Plotter. The results indicated that low expression of both hsa-let-7b-5p (HR\u0026thinsp;=\u0026thinsp;0.72, CI\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;0.96, P\u0026thinsp;=\u0026thinsp;0.026) and hsa-mir-29c-3p (HR\u0026thinsp;=\u0026thinsp;0.54, CI\u0026thinsp;=\u0026thinsp;0.37\u0026ndash;0.79, P\u0026thinsp;=\u0026thinsp;0.0012) were significantly correlated with poor LUAD survival as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F. AUC curves and ROC curves are crucial for assessing biomarkers like miRNAs' prognostic value. AUC curves show sensitivity versus specificity, while ROC curves show effectiveness. The AUC curves were obtained for both miRNAs using CancerMIRNome. hsa-let-7b-5p showed high prognostic accuracy with a narrow confidence interval (AUC\u0026thinsp;=\u0026thinsp;0.89, CI\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;0.92), while hsa-mir-29c-3p showed moderate accuracy with a wider confidence interval (AUC\u0026thinsp;=\u0026thinsp;0.67, CI\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.77). The results suggest that hsa-let-7b-5p may be a more precise and promising biomarker for LUAD as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H. Further, UALCAN and CancerMIRNome databases revealed a significant downregulation of hsa-let-7b-5p in LUAD samples compared to normal tissues as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-J. Further expression analysis of hsa-let-7b-5p in normalized samples (Normal\u0026thinsp;=\u0026thinsp;46 and Tumor\u0026thinsp;=\u0026thinsp;46) carried out using R-based tools confirmed its notable downregulation in LUAD tumor tissues relative to normal tissues (p\u0026thinsp;=\u0026thinsp;0.025), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK. The expression of hsa-let-7b-5p was assessed for various clinical parameters in LUAD using UALCAN and this downregulation was consistent in cancer stages, in patients with a smoking history compared to non-smokers and normal individuals, in males as compared to females and in advanced nodal metastasis stages \u003cb\u003e(Supplementary Fig.\u0026nbsp;5A-D)\u003c/b\u003e. To validate the predicted interactions of hsa-let-7b-5p, a binding energy analysis was performed using miRWalk and RNA22v2 algorithms. The results revealed strong binding affinities of hsa-let-7b-5p with two key regulatory genes: MAD2L1 and FOXM1. Specifically, MAD2L1 exhibited a binding energy of -26.5 kcal/mol and a folding energy of -25.40 kcal/mol, while FOXM1 showed a binding energy of -21.5 kcal/mol and a folding energy of -17.90 kcal/mol, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The heteroduplex structures further support this interaction, with multiple strong base-pairing regions observed. These findings reinforce the regulatory potential of hsa-let-7b-5p in LUAD, indicating that elevated LUAD progression correlates with reduced expression levels of hsa-let-7b-5p.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eBinding and folding energies between hsa-let-7b-5p vs. MAD2L1, FOXM1 and TMPO-AS1\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"694\" height=\"434\"\u003e\u003c/p\u003e\n\u003cp\u003eFurther, the study analyzed the role of lncRNAs within the ceRNA network, which are larger than miRNAs and regulate mRNA expression levels. \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e lists top 20 lncRNAs associated with MAD2L1, sourced from the Enrichr database. The UALCAN database was utilized to identify lncRNAs in LUAD samples, revealing 8 out of 20 significantly upregulated lncRNAs, as shown in \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e. Subsequently, \u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e shows the correlations between upregulated lncRNAs and FOXM1, MAD2L1, and hsa-let-7b-5p. TMPO-AS1 showed a strong positive correlation with FOXM1 and MAD2L1, while a significant negative correlation with hsa-let-7b-5p was found. Using ENCORI database, the correlation graphs were generated between MAD2L1 and TMPO-AS1 (R\u0026thinsp;=\u0026thinsp;0.659), FOXM1 and TMPO-AS1 (R\u0026thinsp;=\u0026thinsp;0.581) and hsa-let-7b-5p and TMPO-AS1 (R = -0.277), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C. Additionally, the prognostic significance of TMPO-AS1 was evaluated using the KM Plotter database by using various clinical parameters such as OS (HR\u0026thinsp;=\u0026thinsp;1.5, CI\u0026thinsp;=\u0026thinsp;1.29\u0026ndash;1.74, P\u0026thinsp;=\u0026thinsp;7.2e-08), FP (HR\u0026thinsp;=\u0026thinsp;1.9, CI\u0026thinsp;=\u0026thinsp;1.52\u0026ndash;2.38, P\u0026thinsp;=\u0026thinsp;8.1e-09), PPS (HR\u0026thinsp;=\u0026thinsp;1.77, CI\u0026thinsp;=\u0026thinsp;1.32\u0026ndash;2.39, P\u0026thinsp;=\u0026thinsp;0.00013), OS\u0026thinsp;+\u0026thinsp;LUAD (HR\u0026thinsp;=\u0026thinsp;2.16, CI\u0026thinsp;=\u0026thinsp;1.69\u0026ndash;2.76, P\u0026thinsp;=\u0026thinsp;4.1e-10), OS\u0026thinsp;+\u0026thinsp;LUAD Smokers (HR\u0026thinsp;=\u0026thinsp;2.34, CI\u0026thinsp;=\u0026thinsp;1.41\u0026ndash;3.88, P\u0026thinsp;=\u0026thinsp;0.00066), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Male\u0026thinsp;+\u0026thinsp;Smokers (HR\u0026thinsp;=\u0026thinsp;2.33, CI\u0026thinsp;=\u0026thinsp;1.28\u0026ndash;4.25, P\u0026thinsp;=\u0026thinsp;0.0046) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-I. Further, TMPO-AS1 was analyzed in OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Stage1(HR\u0026thinsp;=\u0026thinsp;2.65, CI\u0026thinsp;=\u0026thinsp;1.72\u0026ndash;4.09, P\u0026thinsp;=\u0026thinsp;4.4e-06), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Stage2 (HR\u0026thinsp;=\u0026thinsp;2.1, CI\u0026thinsp;=\u0026thinsp;1.22\u0026ndash;3.61, P\u0026thinsp;=\u0026thinsp;0.0061), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Male (HR\u0026thinsp;=\u0026thinsp;1.95, CI\u0026thinsp;=\u0026thinsp;1.39\u0026ndash;2.73, P\u0026thinsp;=\u0026thinsp;9.1e-05), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Female (HR\u0026thinsp;=\u0026thinsp;2.83, CI\u0026thinsp;=\u0026thinsp;1.84\u0026ndash;4.35, P\u0026thinsp;=\u0026thinsp;7.2e-07), OS\u0026thinsp;+\u0026thinsp;LUAD\u0026thinsp;+\u0026thinsp;Female Smokers (HR\u0026thinsp;=\u0026thinsp;3.13, CI\u0026thinsp;=\u0026thinsp;1.18\u0026ndash;8.29, P\u0026thinsp;=\u0026thinsp;0.16) \u003cb\u003e(Supplementary Fig.\u0026nbsp;5E-I).\u003c/b\u003e The survival plots with high hazard ratios and significant difference in low and high expression cohorts \u003cb\u003e(Supplementary Table\u0026nbsp;11)\u003c/b\u003e indicated that overexpression of TMPO-AS1 is associated with poor survival status in LUAD smoker patients.\u003c/p\u003e \u003cp\u003eSubsequently, the significant upregulation in TMPO-AS1 expression levels was observed in LUAD as compared to normal tissues by using the UALCAN (P\u0026thinsp;=\u0026thinsp;6.31e-53), OncoDB (P\u0026thinsp;=\u0026thinsp;6.5e-53), ENCORI (P\u0026thinsp;=\u0026thinsp;8.0e-26) and Lung Cancer Explorer (P\u0026thinsp;=\u0026thinsp;8.5e-41) databases, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-M. To validate the study, further expression analysis of TMPO-AS1 in normalized samples (Normal\u0026thinsp;=\u0026thinsp;59 and Tumor\u0026thinsp;=\u0026thinsp;59) performed using R-based packages demonstrated its considerable overexpression in LUAD tumor tissues relative to normal tissues (p\u0026thinsp;=\u0026thinsp;1.05e-13), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eN. Further, expression analysis based on cancer stages, smokers, and patient\u0026rsquo;s gender shows significant upregulation of TMPO-AS1 with advanced stages, in smoker patients, and males as compared to females, \u003cb\u003e(Supplementary Fig.\u0026nbsp;5J-L)\u003c/b\u003e. This hypothesis was validated by a network created between MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 that was obtained from the miRNet database \u003cb\u003e(Supplementary Fig.\u0026nbsp;6A)\u003c/b\u003e. To assess the interaction potential between hsa-let-7b-5p and lncRNA TMPO-AS1, a folding energy analysis was conducted using RNA22v2. The resulting folding energy was \u0026minus;\u0026thinsp;12.40 kcal/mol, indicating a moderately stable heteroduplex structure, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The inverse correlation between the tumor suppressor hsa-let-7b-5p miRNA and the lncRNA TMPO-AS1 highlights TMPO-AS1's role as a molecular sponge, sequestering hsa-let-7b-5p, reducing its availability to target MAD2L1 and upregulating it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMAD2L1 is Enriched in Cell Cycle Pathways and Shows Progressive Upregulation Across LUAD Stages\u003c/h2\u003e \u003cp\u003eThe gene expression levels of MAD2L1 across different functional states were analysed utilising the CanserSEA database as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. MAD2L1 expression exhibited significant correlations with cell cycle (R\u0026thinsp;=\u0026thinsp;0.78), DNA repair (R\u0026thinsp;=\u0026thinsp;0.71), DNA damage (R\u0026thinsp;=\u0026thinsp;0.67), proliferation (R\u0026thinsp;=\u0026thinsp;0.65) and invasion (R\u0026thinsp;=\u0026thinsp;0.44) as depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-F. The GENI database was utilized for gene enrichment analysis validation to investigate biological processes, cellular components, and molecular functions associated with MAD2L1, FOXM1, and TMPO-AS1, which are crucial for tumour progression, particularly those linked to genomic instability and metastasis \u003cb\u003e(Supplementary Fig.\u0026nbsp;6\u0026ndash;8).\u003c/b\u003e The results showed a significant correlation between factors and cell cycle regulators, particularly those associated with the S and M phases. MAD2L1 and FOXM1 showed strong correlation with M phase CyclinB/CDK, while hsa-let-7b-5p and TMPO-AS1 also showed strong correlation with the M phase cell cycle checkpoints. Where CCNB1: hsa-let-7b-5p (R=-0.285), and TMPO-AS1 (R\u0026thinsp;=\u0026thinsp;0.646); CDK1: hsa-let-7b-5p (R=-0.319), and TMPO-AS1 (R\u0026thinsp;=\u0026thinsp;0.626) \u003cb\u003e(Supplementary Table\u0026nbsp;12)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe study utilized a trend plot to analyze the expression patterns of key biomarkers in LUAD, focusing on the advanced stages using GSCA database. The plot showed a general trend of increasing expression of four genes: MKI67, FOXM1, MAD2L1, and TMPO-AS1 \u003cb\u003e(Supplementary Fig.\u0026nbsp;9A)\u003c/b\u003e. FOXM1 showed an increase from Stage I to Stage IV, while MKI67 rose from Stage I to Stage II, followed by a slight decrease in Stage III. MAD2L1 showed an increase up to Stage II, followed by a slight increase from Stage III to Stage IV. TMPO-AS1 showed a progressive rise that could play a role in later stages. MAD2L1 has the potential to serve as a significant biomarker for the progression of LUAD, providing insights for prognosis. The GSVA (Gene Set Variation Analysis) score showed an upward trend in gene set activity as the disease advanced, indicating that the chosen gene set is progressively enriched or activated in advanced stages of LUAD \u003cb\u003e(Supplementary Fig.\u0026nbsp;9B-D)\u003c/b\u003e. The trend plot was examined for all four genes in conjunction with the TP53 gene, revealing no significant change during the initial stages (\u003cb\u003eSupplementary Fig.\u0026nbsp;9E)\u003c/b\u003e. This suggests that these genes could be integral to the development and progression of LUAD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMAD2L1 Expression Correlates with Immune Cell Infiltration in LUAD\u003c/h2\u003e \u003cp\u003eTumor immune infiltration plays a pivotal role in shaping the lung adenocarcinoma (LUAD) tumor microenvironment. \u003cb\u003eSupplementary Fig.\u0026nbsp;10\u003c/b\u003e presents a multi-step correlation analysis integrating data from TIMER, TIMER 2.0, and GSCA databases to explore the relationship between MAD2L1 expression and immune cell infiltration in LUAD. TIMER analysis indicates that among multiple immune subsets such as B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils and dendritic cells, CD4⁺ T cells (r=-0.196) and B cells (r=-0.209) display the strongest negative correlations with MAD2L1 expression, implicating a potential immune-evasive phenotype associated with high MAD2L1 levels \u003cb\u003e(Supplementary Fig.\u0026nbsp;10A)\u003c/b\u003e. This trend was further validated by using TIMER 2.0 database, which confirmed that distinct subpopulations of CD4⁺ T cells including central memory, effector memory, and general CD4⁺ T cells as well as various B cell subtypes such as memory and class-switched B cells, consistently show inverse relationships with MAD2L1 expression levels (\u003cb\u003eSupplementary Fig.\u0026nbsp;10B-C)\u003c/b\u003e. These findings suggest a MAD2L1-mediated impairment of adaptive immunity within the LUAD microenvironment. Supporting this, GSCA analysis reveals a strong negative Spearman correlation (r =\u0026ndash;0.61, FDR\u0026thinsp;=\u0026thinsp;9.2e-54) between the CD4⁺ T cell infiltration and GSVA scores of MAD2L1 (\u003cb\u003eSupplementary Fig.\u0026nbsp;10D)\u003c/b\u003e. This data highlight MAD2L1 as a potential regulator of immune evasion, contributing to weakened T and B cell-mediated immunity in LUAD and thereby promoting tumor progression.\u003c/p\u003e \u003cp\u003eTo further investigate the immunosuppressive role of MAD2L1 in LUAD tumor microenvironment, we identified the top 10 co-expressed genes using the Enrichr database, including key mitotic regulators such as CCNA2, BIRC5, NEK2, EXO1, CHEK1, CDK1, CDC6, CCNB1, TTK, and NUF2. Correlation analysis with CD4⁺ T cell infiltration by using TIMER database revealed that all these genes showed a negative association, suggesting their collective involvement in modulating the tumor microenvironment (\u003cb\u003eSupplementary Fig.\u0026nbsp;11A-J)\u003c/b\u003e. This pattern highlights a potential link between cell cycle dysregulation and immune evasion, where high MAD2L1 expression and its co-regulated network contribute to an immune-excluded tumor microenvironment, reinforcing their value as early molecular classifiers and targets for immunomodulation in LUAD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung cancer is the leading cause of cancer-related mortality globally, but its molecular mechanism remains unclear (Torre et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The aim of this study is to uncover novel ceRNA regulatory networks in lung cancer, specifically in LUAD, by using tissue-based datasets. MAD2L1, a crucial component of spindle assembly checkpoint, is located on chromosome 14 and has been found to cause chromosomal instability and overexpression in mouse embryos (Qiao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies have shown that both low and high MAD2L1 expression can lead to aneuploidy and tumorigenesis (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Recent evidence suggests that MAD2L1 is majorly overexpressed in tumors and overexpression can promote tumor formation in multiple cancer types (Xia et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). MAD2L1 has been linked to survival outcomes, gene expression control, and potential biomarker utility. Given its role in tumorigenesis and its overexpression in lung adenocarcinoma, MAD2L1 presents a promising target for personalized cancer therapies. By developing drugs that specifically inhibit MAD2L1, it may be possible to reduce tumor growth and improve patient outcomes. Additionally, assessing MAD2L1 expression levels could help tailor treatment strategies, offering a more personalized approach to cancer care.\u003c/p\u003e \u003cp\u003eThe study also explores the oncogenic potential of MAD2L1, its survival importance, differential expression as well as its associated transcription factors, ceRNA network interactions, and biological processes that are related to MAD2L1. Targeting these interactions could be a potential therapeutic approach, potentially modulating the expression of genes involved in tumor progression and inhibiting cancer cell growth. Identifying key transcription factors regulating MAD2L1 expression could also provide further therapeutic targets. According to Li et al., long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4. According to Li et al., long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4 (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). El-Daly et al. has studied the Interplay of the PD-L1/MALT1/miR-200a axis during lung cancer development (El-Daly et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research has shown that breast cancer patients are diagnosed with higher histological grades, clinical stages, and early metastases due to overexpression of MAD2L1 in their tissues (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Silencing MAD2L1 in liver cancer cells reduced cell proliferation in vitro and inhibited c-MYC-driven liver cancer development in vivo (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). MAD2L1 has been identified as a cancer-driver gene in malignant pleural mesothelioma (Bisceglia et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), cholangiocarcinoma (Gao et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and LUAD (Shi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with elevated expression associated with poor prognosis and potential therapeutic targets. High MAD2L1 expression is substantially associated with poor overall survival in lung cancer patients, particularly in LUAD. The oncogenic role of MAD2L1 has been demonstrated in breast (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and colorectal malignancies (Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) through its overexpression .\u003c/p\u003e \u003cp\u003eFurthermore, this study reveals that transcription factors, such as FOXM1, regulate MAD2L1, an oncogene known for its regulation of cell cycle-related genes, has been identified as a contributor to poor survival outcomes in various malignancies, including lung cancer. This makes it a promising biomarker in conjunction with MAD2L1. The strong correlation between MAD2L1 and FOXM1 in promoting tumor proliferation and progression is highlighted by the study's findings. The overexpression of MAD2L1 has been linked to cancer progression, but the molecular mechanisms involved, and its regulatory processes remain poorly understood. The study also focuses on the regulatory mechanisms and molecules involved with MAD2L1 in tumorigenesis, using the ceRNA mechanism, which involves lncRNAs facilitating the regulation of oncogenes by inhibiting miRNAs. The study also highlights the role of MAD2L1 in mitotic checkpoint genes, such as MAD2L1, and suggests that TMPO-AS1, upregulated in LUAD, functions as a molecular sponge by sequestering hsa-let-7b-5p, facilitating the overexpression of MAD2L1 and FOXM1. Subsequently the study also highlights the role of high expression of MAD2L1 in mitotic progression and genomic instability, underscoring its complex role in cancer biology. The study also indicates that MAD2L1 could influence the tumor microenvironment through the modulation of CD4\u0026thinsp;+\u0026thinsp;Th2 immune responses. CD4\u0026thinsp;+\u0026thinsp;Th2 immune responses are crucial for orchestrating the body's defense against extracellular pathogens, such as parasites (Chulanetra and Chaicumpa, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They promote the production of antibodies by B cells and are involved in the regulation of allergic reactions (Catal\u0026aacute;n et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of cancer, these responses can influence tumor growth and progression by modulating the local immune environment. The correlation between MAD2L1 and CD4\u0026thinsp;+\u0026thinsp;cells may yield novel understandings of immune-oncogenic interactions in LUAD. The results suggest possible therapeutic approaches aimed at MAD2L1, such as intervening in the MAD2L1-FOXM1 pathway or restoring miRNA concentrations. Small molecule inhibitors targeting MAD2L1 or its associated regulatory networks may present a new strategy for treating LUAD, backed by preclinical investigations in various other cancer types. In conclusion, the complexity of MAD2L1's regulatory ceRNA network underscores its importance as a prognostic biomarker and therapeutic target in LUAD, potentially leading to enhanced patient outcomes through personalized treatment strategies.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe multi-omics data used in this study is insufficient to demonstrate the therapeutic benefits of MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 in clinical practice and trial stratification. Further research is needed to understand its unique activities in different tumor types. The study's limitations include variability in patient cohorts, data collection methods, and normalization processes, as well as insufficient clinical annotations and insufficiently annotated samples in publicly accessible datasets. Future research will focus on experimental validation and real-world data translation to overcome these limitations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study concludes that MAD2L1 overexpression is a significant high-risk factor for LUAD, particularly in smoker females, and is strongly correlated with FOXM1, TMPO-AS1, and hsa-let-7b-5p. The novel regulatory ceRNA network MAD2L1/FOXM1/hsa-let-7b-5p/TMPO-AS1 could be a promising prognostic biomarker for LUAD smoker patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMAD2L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eMitotic Arrest-Deficient 2 Like1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCancerSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eCancer Single Cell State Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eLung Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eLung Squamous Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eChromosomal Instability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003encRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eNon-coding RNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTranscription Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eNon- Small Cell Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eSmall Cell Lung Cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTumor Protein p53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTRRUST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTranscriptional Regulatory Relationships Unraveled by Sentence-based Text mining\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eLepidic-predominant Adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eceRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eCompeting Endogenous RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE2F1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eEukaryotic Transcription Factor1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFOXM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eForkhead box M1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eENCORI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u003cu\u003eEnc\u003c/u\u003eyclopedia \u003cu\u003eo\u003c/u\u003ef \u003cu\u003eR\u003c/u\u003eNA \u003cu\u003eI\u003c/u\u003enteractomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eONCODB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eOncology Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGEPIA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eGene Expression Profiling Interactive Analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ehsa-let-7b\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eHomosapiens MicroRNA Family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003emiRNet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eMicroRNA Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEnrichr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eEnrichment Analysis Resource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eKMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eKaplan-Meier Plotter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003elong noncoding RNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eMicro Ribonucleic Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMKI67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eMarker of Proliferation Ki-67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eOverall Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eFirst Progression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003ePost Progression Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGENI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eGlobal Environment for Network Innovations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRNA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eRibonucleic Acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTCGA Portal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eThe Cancer Genomic Atlas Portal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTCGAnalyzerv1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eThe Cancer Genome Altas Analyzer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTIMER 2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTumor Immune Estimation Resource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTMPO-AS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eThymopoietin Antisense Transcript1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTNM Plot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u0026nbsp;Tumor Node Metastasis Plot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eUALCAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eThe University of Alabama At Birmingham Cancer Data Analysis Portal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eYBX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eY-Box Binding Protein 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRBL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eRetinoblastoma- Like Protein 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eARID3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eA-T rich interacting domain 3a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAct_CD4+ Th2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eActivated CD4+T-Helper 2 cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eTumor-Infiltrating Immune Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eNatural Killer cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eCluster of Differentiation 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eCluster of Differentiation 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eEpidermal Growth Factor Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eAnaplastic Lymphoma Kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eAnaphase Promoting Complex\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eEpithelial Mesenchymal Transition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGSCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eGene Set Cancer Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 520px;\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eConflicts of interest:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThis study does not require ethical approval.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Support:\u003c/h2\u003e \u003cp\u003eRN would like to thank the funding support from Manipal University Jaipur for the Enhanced Seed Grant under the Endowment Fund (No. E3/2023-24/QE-04-05).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRN: Conception, study design, critical reading, intellectual assessment of the manuscript, preparation of the manuscript, and final approval. CS: Study design, and preparation of the manuscript, critical review BB: Study design, and preparation of the manuscript, critical review. PV: Study design, and preparation of the manuscript, critical review. SN: Data Analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003ewe expresses gratitude to Manipal University Jaipur for providing funding for the Enhanced Seed Grant under the Endowment Fund (No. E3/2023-24/QE-04-05).\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, A., 2022. Epigenetic regulation of immunosuppressive tumor-associated macrophages through dysregulated microRNAs. Semin. Cell Dev. Biol., Special issue: MicroRNAs in Immunity and Cancer by Afsar Naqvi and Maryam Sarwat 124, 26\u0026ndash;33. https://doi.org/10.1016/j.semcdb.2021.09.001\u003c/li\u003e\n\u003cli\u003eAl-Rawi, D.H., Lettera, E., Li, J., DiBona, M., Bakhoum, S.F., 2024. Targeting chromosomal instability in patients with cancer. Nat. Rev. Clin. Oncol. 21, 645\u0026ndash;659. https://doi.org/10.1038/s41571-024-00923-w\u003c/li\u003e\n\u003cli\u003eBisceglia, L., Morani, F., Guerrieri, L., Santoni-Rugiu, E., \u0026Ccedil;akılkaya, P., Scatena, C., Scarpitta, R., Engelholm, L.H., Behrendt, N., Gemignani, F., Landi, S., 2024. BAG2, MAD2L1, and MDK are cancer-driver genes and candidate targets for novel therapies in malignant pleural mesothelioma. Cancer Gene Ther. 31, 1708\u0026ndash;1720. https://doi.org/10.1038/s41417-024-00805-4\u003c/li\u003e\n\u003cli\u003eCai, L., Lin, S., Girard, L., Zhou, Y., Yang, L., Ci, B., Zhou, Q., Luo, D., Yao, B., Tang, H., Allen, J., Huffman, K., Gazdar, A., Heymach, J., Wistuba, I., Xiao, G., Minna, J., Xie, Y., 2019. LCE: an open web portal to explore gene expression and clinical associations in lung cancer. Oncogene 38, 2551\u0026ndash;2564. https://doi.org/10.1038/s41388-018-0588-2\u003c/li\u003e\n\u003cli\u003eCatal\u0026aacute;n, D., Mansilla, M.A., Ferrier, A., Soto, L., Oleinika, K., Aguill\u0026oacute;n, J.C., Aravena, O., 2021. Immunosuppressive Mechanisms of Regulatory B Cells. Front. Immunol. 12. https://doi.org/10.3389/fimmu.2021.611795\u003c/li\u003e\n\u003cli\u003eChandrashekar, D.S., Karthikeyan, S.K., Korla, P.K., Patel, H., Shovon, A.R., Athar, M., Netto, G.J., Qin, Z.S., Kumar, S., Manne, U., Creighton, C.J., Varambally, S., 2022. UALCAN: An update to the integrated cancer data analysis platform. Neoplasia N. Y. N 25, 18\u0026ndash;27. https://doi.org/10.1016/j.neo.2022.01.001\u003c/li\u003e\n\u003cli\u003eChang, L., Xia, J., 2023. MicroRNA Regulatory Network Analysis Using miRNet 2.0, in: Song, Q., Tao, Z. (Eds.), Transcription Factor Regulatory Networks. Springer US, New York, NY, pp. 185\u0026ndash;204. https://doi.org/10.1007/978-1-0716-2815-7_14\u003c/li\u003e\n\u003cli\u003eChulanetra, M., Chaicumpa, W., 2021. Revisiting the Mechanisms of Immune Evasion Employed by Human Parasites. Front. Cell. Infect. Microbiol. 11. https://doi.org/10.3389/fcimb.2021.702125\u003c/li\u003e\n\u003cli\u003eEl-Daly, S.M., Abdelrahman, S.S., El-Bana, M.A., Abdel-Latif, Y., Medhat, D., Morsy, S.M., Wafay, H.A., 2025. Deciphering the Interplay of the PD-L1/MALT1/miR-200a Axis During Lung Cancer Development. Biotechnol. Appl. Biochem. https://doi.org/10.1002/bab.2724\u003c/li\u003e\n\u003cli\u003eGao, Y., Liu, Y., Sun, L., Ouyang, X., Zhu, C., Qin, X., 2021. MAD2L1 Functions As a Novel Diagnostic and Predictive Biomarker in Cholangiocarcinoma. Genet. Test. Mol. Biomark. 25, 685\u0026ndash;695. https://doi.org/10.1089/gtmb.2021.0122\u003c/li\u003e\n\u003cli\u003eGyőrffy, B., 2024. Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors. Innov. Camb. Mass 5, 100625. https://doi.org/10.1016/j.xinn.2024.100625\u003c/li\u003e\n\u003cli\u003eHayashi, A., Ruppo, S., Heilbrun, E.E., Mazzoni, C., Adar, S., Yassour, M., Rmaileh, A.A., Shaul, Y.D., 2023. GENI: A web server to identify gene set enrichments in tumor samples. Comput. Struct. Biotechnol. J. 21, 5531\u0026ndash;5537. https://doi.org/10.1016/j.csbj.2023.10.053\u003c/li\u003e\n\u003cli\u003eLi, J.-H., Liu, S., Zhou, H., Qu, L.-H., Yang, J.-H., 2014. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein\u0026ndash;RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42, D92\u0026ndash;D97. https://doi.org/10.1093/nar/gkt1248\u003c/li\u003e\n\u003cli\u003eLi, Q., Tong, D., Jing, X., Ma, P., Li, F., Jiang, Q., Zhang, J., Wen, H., Cui, M., Huang, C., Zhang, M., 2023a. MAD2L1 is transcriptionally regulated by TEAD4 and promotes cell proliferation and migration in colorectal cancer. Cancer Gene Ther. 30, 727\u0026ndash;737. https://doi.org/10.1038/s41417-022-00586-8\u003c/li\u003e\n\u003cli\u003eLi, Q., Tong, D., Jing, X., Ma, P., Li, F., Jiang, Q., Zhang, J., Wen, H., Cui, M., Huang, C., Zhang, M., 2023b. MAD2L1 is transcriptionally regulated by TEAD4 and promotes cell proliferation and migration in colorectal cancer. Cancer Gene Ther. 30, 727\u0026ndash;737. https://doi.org/10.1038/s41417-022-00586-8\u003c/li\u003e\n\u003cli\u003eLi, R., Qu, H., Wang, S., Chater, J.M., Wang, X., Cui, Y., Yu, L., Zhou, R., Jia, Q., Traband, R., Wang, M., Xie, W., Yuan, D., Zhu, J., Zhong, W.-D., Jia, Z., 2022. CancerMIRNome: an interactive analysis and visualization database for miRNome profiles of human cancer. Nucleic Acids Res. 50, D1139\u0026ndash;D1146. https://doi.org/10.1093/nar/gkab784\u003c/li\u003e\n\u003cli\u003eLi, T., Fu, J., Zeng, Z., Cohen, D., Li, J., Chen, Q., Li, B., Liu, X.S., 2020. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48, W509\u0026ndash;W514. https://doi.org/10.1093/nar/gkaa407\u003c/li\u003e\n\u003cli\u003eLi, Y., Zhao, L., Zhao, P., Liu, Z., 2019. Long non-coding RNA LINC00641 suppresses non-small-cell lung cancer by sponging miR-424-5p to upregulate PLSCR4. Cancer Biomark. Sect. Dis. Markers 26, 79\u0026ndash;91. https://doi.org/10.3233/CBM-190142\u003c/li\u003e\n\u003cli\u003eLi, Y., Zhu, Y., Prochownik, E.V., 2016. MicroRNA-based screens for synthetic lethal interactions with c-Myc. RNA Dis. Houst. Tex 3, e1330. https://doi.org/10.14800/rd.1330\u003c/li\u003e\n\u003cli\u003eLiu, C.-J., Hu, F.-F., Xie, G.-Y., Miao, Y.-R., Li, X.-W., Zeng, Y., Guo, A.-Y., 2023. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief. Bioinform. 24, bbac558. https://doi.org/10.1093/bib/bbac558\u003c/li\u003e\n\u003cli\u003eLv, D., Li, D., Cai, Y., Guo, J., Chu, S., Yu, J., Liu, K., Jiang, T., Ding, N., Jin, X., Li, Y., Xu, J., 2024. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res. 52, D1155\u0026ndash;D1162. https://doi.org/10.1093/nar/gkad824\u003c/li\u003e\n\u003cli\u003eMetintaş, S., 2023. Epidemiology of Lung Cancer, in: Cingi, C., Yorgancıoğlu, A., Bayar Muluk, N., Cruz, A.A. (Eds.), Airway Diseases. Springer International Publishing, Cham, pp. 939\u0026ndash;983. https://doi.org/10.1007/978-3-031-22483-6_57-1\u003c/li\u003e\n\u003cli\u003ePoliseno, L., Lanza, M., Pandolfi, P.P., 2024. Coding, or non-coding, that is the question. Cell Res. 34, 609\u0026ndash;629. https://doi.org/10.1038/s41422-024-00975-8\u003c/li\u003e\n\u003cli\u003ePriyamvada, P., Ramaiah, S., 2024. Potential Signature Therapeutic Biomarkers TOP2A, MAD2L1, and CDK1 in Colorectal Cancer: A Systems Biomedicine-Based Approach. Biochem. Genet. 62, 2166\u0026ndash;2194. https://doi.org/10.1007/s10528-023-10544-0\u003c/li\u003e\n\u003cli\u003eQiao, J.-Y., Zhou, Q., Xu, K., Yue, W., Lei, W.-L., Li, Y.-Y., Gu, L.-J., Ouyang, Y.-C., Hou, Y., Schatten, H., Meng, T.-G., Wang, Z.-B., Sun, Q.-Y., 2023. Mad2 is dispensable for accurate chromosome segregation but becomes essential when oocytes are subjected to environmental stress. Development 150, dev201398. https://doi.org/10.1242/dev.201398\u003c/li\u003e\n\u003cli\u003eRu, B., Wong, C.N., Tong, Y., Zhong, J.Y., Zhong, S.S.W., Wu, W.C., Chu, K.C., Wong, C.Y., Lau, C.Y., Chen, I., Chan, N.W., Zhang, J., 2019. TISIDB: an integrated repository portal for tumor\u0026ndash;immune system interactions. Bioinformatics 35, 4200\u0026ndash;4202. https://doi.org/10.1093/bioinformatics/btz210\u003c/li\u003e\n\u003cli\u003eSantos, A., Wernersson, R., Jensen, L.J., 2015. Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140\u0026ndash;D1144. https://doi.org/10.1093/nar/gku1092\u003c/li\u003e\n\u003cli\u003eShi, Y.-X., Zhu, T., Zou, T., Zhuo, W., Chen, Y.-X., Huang, M.-S., Zheng, W., Wang, C.-J., Li, X., Mao, X.-Y., Zhang, W., Zhou, H.-H., Yin, J.-Y., Liu, Z.-Q., 2016. Prognostic and predictive values of CDK1 and MAD2L1 in lung adenocarcinoma. Oncotarget 7, 85235\u0026ndash;85243. https://doi.org/10.18632/oncotarget.13252\u003c/li\u003e\n\u003cli\u003eSun, Q., Zhang, X., Liu, T., Liu, X., Geng, J., He, X., Liu, Y., Pang, D., 2013. Increased expression of Mitotic Arrest Deficient-Like 1 (MAD1L1) is associated with poor prognosis and insensitive to Taxol treatment in breast cancer. Breast Cancer Res. Treat. 140, 323\u0026ndash;330. https://doi.org/10.1007/s10549-013-2633-8\u003c/li\u003e\n\u003cli\u003eTang, G., Liu, X., Cho, M., Li, Y., Tran, D.-H., Wang, X., 2024. Pan-cancer discovery of somatic mutations from RNA sequencing data. Commun. Biol. 7, 1\u0026ndash;12. https://doi.org/10.1038/s42003-024-06326-y\u003c/li\u003e\n\u003cli\u003eTang, Z., Kang, B., Li, C., Chen, T., Zhang, Z., 2019. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556\u0026ndash;W560. https://doi.org/10.1093/nar/gkz430\u003c/li\u003e\n\u003cli\u003eTorre, L.A., Siegel, R.L., Jemal, A., 2016. Lung Cancer Statistics, in: Ahmad, A., Gadgeel, S. (Eds.), Lung Cancer and Personalized Medicine: Current Knowledge and Therapies. Springer International Publishing, Cham, pp. 1\u0026ndash;19. https://doi.org/10.1007/978-3-319-24223-1_1\u003c/li\u003e\n\u003cli\u003eWei, R., Wang, Z., Zhang, Y., Wang, B., Shen, N., E, L., Li, X., Shang, L., Shang, Y., Yan, W., Zhang, X., Ma, W., Wang, C., 2020. Bioinformatic analysis revealing mitotic spindle assembly regulated NDC80 and MAD2L1 as prognostic biomarkers in non-small cell lung cancer development. BMC Med. Genomics 13, 112. https://doi.org/10.1186/s12920-020-00762-5\u003c/li\u003e\n\u003cli\u003eXia, T., Meng, L., Zhao, Z., Li, Y., Wen, H., Sun, H., Zhang, T., Wei, J., Li, F., Liu, C., 2021. Bioinformatics prediction and experimental verification identify MAD2L1 and CCNB2 as diagnostic biomarkers of rhabdomyosarcoma. Cancer Cell Int. 21, 634. https://doi.org/10.1186/s12935-021-02347-3\u003c/li\u003e\n\u003cli\u003eXie, Z., Bailey, A., Kuleshov, M.V., Clarke, D.J.B., Evangelista, J.E., Jenkins, S.L., Lachmann, A., Wojciechowicz, M.L., Kropiwnicki, E., Jagodnik, K.M., Jeon, M., Ma\u0026rsquo;ayan, A., 2021. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 1, e90. https://doi.org/10.1002/cpz1.90\u003c/li\u003e\n\u003cli\u003eYuan, H., Yan, M., Zhang, G., Liu, W., Deng, C., Liao, G., Xu, L., Luo, T., Yan, H., Long, Z., Shi, A., Zhao, T., Xiao, Y., Li, X., 2019. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 47, D900\u0026ndash;D908. https://doi.org/10.1093/nar/gky939\u003c/li\u003e\n\u003cli\u003eZengin, T., Masud, B.A., \u0026Ouml;nal-S\u0026uuml;zek, T., 2024. TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery. Cancers 16, 345. https://doi.org/10.3390/cancers16020345\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MAD2L1, FOXM1, TMPO-AS1, hsa-let-7b-5p, LUAD smoker females, poor prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5794582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eMAD2L1, a spindle assembly checkpoint molecule, is associated in cancer cell proliferation and carcinogenesis, although its ceRNA network is unknown.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eInitially, patient\u0026rsquo;s survivability associated with the gene expression was analysed by using the Kaplan-Meier plotter database. Here, we used several TCGA databases such as UALCAN, OncoDB, ENCORI, Lung cancer explorer, GEPIA2, TCGAnalyzer, and CancerMIRNome to identify differential mRNA, miRNA, and lncRNA expression. The Enrichr database was utilized to identify the transcription factor regulating MAD2L1, which was then correlated with miRNA and lncRNA, forming the ceRNA network using the miRNet database. Database miRWalk and RNA22v2 were used to predict the folding energy and binding affinity between the MAD2L1 and miRNA. TIMER and TIMER 2.0 databases were incorporated to analyse the tumor infiltrating immune cells in LUAD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study found that overexpression of MAD2L1 in lung cancer patients is a high-risk factor for lung adenocarcinoma (LUAD) (HR\u0026thinsp;=\u0026thinsp;1.34, P\u0026thinsp;=\u0026thinsp;0.001), particularly in smoker females (HR\u0026thinsp;=\u0026thinsp;1.61, P\u0026thinsp;=\u0026thinsp;0.018). The study revealed MAD2L1 overexpression in LUAD cases, with a fold change of 8.7, and a strong positive correlation between RNA and protein expression levels by Cancer Proteome (R\u0026thinsp;=\u0026thinsp;0.764). The study identified regulatory molecules of MAD2L1 such as transcription factor FOXM1 (R\u0026thinsp;=\u0026thinsp;0.770), and lncRNA TMPO-AS1 (R\u0026thinsp;=\u0026thinsp;0.565) as positively correlated with MAD2L1, while miRNA hsa-let-7b-5p, negatively correlated with MAD2L1 (R =-0.314), FOXM1 (R =-0.393), and TMPO-AS1 (R =-0.277). The study suggests that TMPO-AS1 suppresses tumor suppression activity of let-7b-5p and targeting hsa-let-7b-5p could regulate MAD2L1, FOXM1 and lncRNA expression levels in LUAD. Additionally, a strong folding and binding energy was identified between the MAD2L1 gene and hsa-let-7b-5p. After analyzing the tumor microenvironment, we found that CD4\u0026thinsp;+\u0026thinsp;T cells and B cells negatively correlate with the overexpression of MAD2L1.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study indicates that MAD2L1 is overexpressed in females with LUAD, highlighting its potential as a molecular classifier and prognostic biomarker, and introduces a novel regulatory ceRNA network.\u003c/p\u003e","manuscriptTitle":"hsa-let-7b-5p/TMPO-AS1-mediated ceRNA networks are linked to poor prognosis for lung cancer patients with FOXM1/MAD2L1 Axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:16:53","doi":"10.21203/rs.3.rs-5794582/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T09:03:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T08:26:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T11:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124328108893204742023324611428231536693","date":"2025-04-23T11:18:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-21T08:31:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-18T10:51:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-15T09:58:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76868501-2302-4d32-be3f-806f48e0c867","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-08T19:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 11:16:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794582","identity":"rs-5794582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}