Abstract
Background
Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy with poor clinical outcomes, underscoring the urgent need for robust prognostic biomarkers and therapeutically tractable regulatory molecules. Long non-coding RNAs (lncRNAs) have emerged as key modulators of tumor progression and immune regulation; however, the prognostic and functional significance of TMPO-AS1 in LUAD remains largely unexplored.
Methods
Firstly, the top upregulated lncRNAs in LUAD were identified using the lnc2cancer3.0 database, and their prognostic significance was evaluated with the Kaplan–Meier Plotter. Differential expression of the selected lncRNA candidate was validated using TCGA-based platforms, including UALCAN, ENCORI and R-based statistical packages. TMPO-AS1–associated miRNAs were then predicted using the miRNet database, and their expression correlations, prognostic relevance, and differential expressions were assessed using the ENCORI, KM Plotter databases and R-based packages, respectively. A miRNA-centered heterogeneous gene model was constructed using the CancerMIRNome database, and gene–miRNA correlations were validated via ENCORI, followed by their survival analysis using the KM Plotter. Finally, immune cell infiltration associated with the identified genes was analyzed using the GSCA dataset.
Results
TMPO-AS1 was found to be significantly overexpressed in LUAD patients (HR = 2.16). The correlation analysis revealed hsa-let-7b-5p was significantly and negatively correlated with TMPO-AS1, and its overexpression was also linked with better prognosis. Survival analysis selectively highlighted TGFBR3, RNF144B, CD59, and MAT2B as the most significant positively associated genes, while AURKA and KIFC1 emerged as the most significant negatively associated genes. Immune infiltration analysis demonstrated that the miRNA-positively associated genes were negatively correlated with nTreg cells and positively correlated with NKT cells, whereas the miRNA-negatively associated genes showed an inverse correlation pattern.
Conclusion
The study concludes that TMPO-AS1 is overexpressed in cases of LUAD, highlighting its potential as a molecular classifier for LUAD and a prognostic biomarker associated with poor clinical outcomes.
Graphical Abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s13008-026-00185-1.
Keywords: TMPO-AS1, Lung adenocarcinoma, CeRNA Network, Prognosis, Non-invasiveness
Article Highlights
Overexpression of lncRNA TMPO-AS1 is associated with poorprognosis, cellular invasion, proliferation, and metastasis in lungadenocarcinoma.
Establishment of the ceRNA (lncRNA-miRNA-mRNA) regulatorynetwork promises early prognosis in LUAD.
The sponging mechanism of TMPO-AS1 associated with hsa-let-7b-5p,induces aberrant overexpression of genes.
hsa-let-7b-5p surrounding heterogeneity showed various infiltratingand evading immune cells.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13008-026-00185-1.
Introduction
Lung adenocarcinoma (LUAD) has emerged as the most frequent and biologically heterogeneous subtype of lung cancer, accounting for a substantial proportion of cancer-related fatalities all across the world [1, 2]. In the year 2020, approximately 39% of male and 57% of female lung cancer cases were identified as adenocarcinoma, underscoring its dominance among lung cancer histotypes [3]. Despite advances in surgical, targeted, and immunotherapeutic approaches, LUAD continues to exhibit high metastatic potential and poor long-term survival [4, 5], emphasising the critical need for discovering novel biomarkers and mechanistic regulatory networks that can improve therapeutic and prognostic strategies [6].
Long non-coding RNAs (lncRNAs) have grown into significant regulators of gene expression and tumor progression through epigenetic, transcriptional, and post-transcriptional mechanisms in the last few years [7, 8]. Among these, their function as competitive endogenous RNA (ceRNA) molecules modulates microRNA (miRNA) activity and downstream target genes, significantly influencing the oncogenic signalling pathways along with molecular heterogeneity across several cancers, including LUAD [9, 10]. One such dysregulated lncRNA, Thymopoietin-Antisense RNA 1 (TMPO-AS1), has been reported to promote proliferation, cellular invasion, and metastasis, leading to several malignancies, and its overexpression has been associated with poor clinical outcomes [11–13]. However, the underlying regulatory mechanism and its downstream targets remain insufficiently explored [14, 15]. The tumor-suppressiveness of miRNA hsa-let-7b-5p is an important aspect of controlling genes that are involved in apoptosis, cell cycle control, and immunological modulation [16–18]. Aberrant suppression of hsa-let-7b-5p may trigger oncogenic signaling through ceRNA interactions with overexpressed lncRNAs such as TMPO-AS1 [19]. Understanding this interaction can therefore elucidate critical molecular axes contributing to LUAD pathogenesis [20]. Recent studies have further emphasized the importance of lncRNA–miRNA regulatory networks in shaping tumor progression and immune dynamics in lung adenocarcinoma. For instance, recent investigations have demonstrated that ceRNA-mediated interactions not only regulate oncogenic gene expression programs but also influence immune cell infiltration, immune evasion, and therapeutic response in LUAD [21, 22]. These findings highlight the emerging role of integrated RNA regulatory axes as critical modulators of both tumor heterogeneity and the tumor immune microenvironment. However, despite these advances, the contribution of specific lncRNA–miRNA pairs in coordinating transcriptional heterogeneity alongside immune infiltration patterns remains underexplored.
In this context, the present study aimed to describe the ceRNA TMPO-AS1–hsa-let-7b-5p-regulatory axis and characterize the associated heterogeneous gene model and evaluate their prognostic and immunological infiltration relevance in LUAD. By integrating the multi-database transcriptomic, survival, and immune-infiltration analyses, this article offers a thorough perspective on the molecular and immunological landscape associated with TMPO-AS1, supporting its possible benefits.
as an early prognostic biomarker, providing a better therapeutic target in LUAD.
Results
Identification of prognostically relevant upregulated lncRNAs in LUAD
lncRNAs are increasingly recognized as critical regulators of tumor biology, functioning as modulators of transcriptional programs, post-transcriptional control, and ceRNA networks that govern cell proliferation, survival, and immune evasion. To identify lncRNAs with potential relevance for small-molecule–based therapeutic targeting in LUAD, a panel of sixteen upregulated lncRNAs associated with cell growth, survival, and miRNA regulation was retrieved from the lnc2cancer 3.0 database (Supplementary Table 1). Subsequently, the prognostic significance of the lncRNAs was determined, using the Kaplan-Meier Plotter (KMP) database for their overall survival (OS) status, along with the histological subtype (LUAD), smoking history, and gender. The lncRNAs were evaluated based on their risk score (HR score) and significance level (P < 0.05). Interestingly, out of all the selected lncRNAs, only TMPO-AS1 exhibited a strong association with poor prognosis in LUAD, showing a significantly elevated risk (HR = 2.16), with even higher hazard ratios in smokers (HR = 2.34) and female smoker patients (HR = 3.13) (Table 1).
Table 1.
Survival analysis of lncRNAs
| S.No. | Associated genes | Survival | Patient No. | p-value | HR | CI | Low expression cohort (months) | High expression cohort (months) |
|---|---|---|---|---|---|---|---|---|
| 1 | FBXL19-AS1 | OS | 1411 | 0.0021 | 1.26 | 1.09–1.46 | 88 | 60 |
| OS+LUAD | 672 | 0.0015 | 1.47 | 1.16–1.87 | 127 | 87.7 | ||
|
OS+LUAD +Smoker |
131 | 0.67 | 1.11 | 0.69–1.8 | 46 | 43.07 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.48 | 0.81 | 0.45–1.45 | 41.4 | 54.17 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.33 | 1.57 | 0.63–3.92 | – | – | ||
| 2 | HCP5 | OS | 1411 | 0.0029 | 0.83 | 0.74–0.94 | 62.2 | 76 |
| OS+LUAD | 672 | 0.034 | 0.83 | 0.7–0.99 | 74 | 86 | ||
|
OS+LUAD +Smoker |
546 | 0.099 | 0.8 | 0.62–1.04 | 78 | 80 | ||
|
OS+LUAD +Smoker+Males |
319 | 0.17 | 0.79 | 0.55–1.11 | 78 | 80 | ||
|
OS+LUAD+ Smoker+Females |
227 | 0.35 | 0.83 | 0.56–1.23 | 71 | 95 | ||
| 3 | HMMR-AS1 | OS | 1411 | 0.51 | 1.05 | 0.91–1.22 | 77.77 | 73.3 |
| OS+LUAD | 672 | 0.2 | 1.17 | 0.92–1.48 | 108 | 90 | ||
|
OS+LUAD +Smoker |
231 | 0.44 | 0.83 | 0.51–1.35 | 42 | 54.17 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.64 | 0.87 | 0.49–1.55 | 44.83 | 43.07 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.48 | 1.38 | 0.56–3.42 | 91 | 68 | ||
| 4 | HOXA11-AS | OS | 1411 | 0.019 | 1.19 | 1.03–1.38 | 86.27 | 63.03 |
| OS+LUAD | 672 | 0.00043 | 1.53 | 1.21–1.95 | 119.87 | 91 | ||
|
OS+LUAD +Smoker |
231 | 0.45 | 1.21 | 0.74–1.96 | 47.77 | 42 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.67 | 1.13 | 0.63–2.03 | 47.77 | 41.4 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.6 | 1.27 | 0.52–3.13 | 57 | 40.21 | ||
| 5 | LINC00460 | OS | 1411 | 0.75 | 0.98 | 0.84–1.13 | 76 | 72.33 |
| OS+LUAD | 672 | 0.74 | 0.96 | 0.76–1.22 | 99 | 107 | ||
|
OS+LUAD +Smoker |
231 | 0.92 | 0.98 | 0.6–1.58 | 41.4 | 47.77 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.37 | 0.77 | 0.43–1.37 | 38.1 | 48 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.37 | 1.52 | 0.61–3.79 | 68 | 35 | ||
| 6 | LINC00467 | OS | 1411 | 0.0014 | 0.79 | 0.68–0.91 | 63 | 92.97 |
| OS+LUAD | 672 | 0.00014 | 0.63 | 0.49–0.8 | 79.87 | 125.77 | ||
|
OS+LUAD +Smoker |
231 | 0.014 | 0.54 | 0.33–0.89 | 36 | 54.17 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.027 | 0.51 | 0.28–0.94 | – | – | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.19 | 0.54 | 0.21–1.38 | – | – | ||
| 7 | MAFG-AS1 | OS | 1411 | 0.086 | 1.14 | 0.98–1.32 | 87.7 | 68.1 |
| OS+LUAD | 672 | 0.00016 | 1.59 | 1.25–2.02 | 119.87 | 74 | ||
|
OS+LUAD +Smoker |
231 | 0.016 | 1.83 | 1.11–3.03 | – | – | ||
|
OS+LUAD +Smoker+Males |
162 | 0.084 | 1.69 | 0.93–3.07 | 54.17 | 38 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.02 | 3.15 | 1.13–8.76 | 91 | 19 | ||
| 8 | MALAT1 | OS | 1411 | 0.053 | 0.86 | 0.74–1.74 | 68 | 88.7 |
| OS+LUAD | 672 | 0.67 | 0.95 | 0.75–1.21 | 103 | 108 | ||
|
OS+LUAD +Smoker |
231 | 0.61 | 0.88 | 0.54–1.43 | 41.4 | 48 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.19 | 0.68 | 0.38–1.22 | 38.1 | 52 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.34 | 1.55 | 0.62–3.86 | 68 | 37 | ||
| 9 | SBF2-AS1 | OS | 1411 | 0.0013 | 1.27 | 1.1–1.47 | 92.63 | 63.4 |
| OS+LUAD | 672 | 0.26 | 1.15 | 0.9–1.46 | 112.67 | 89 | ||
|
OS+LUAD +Smoker |
231 | 0.34 | 1.27 | 0.78–2.06 | 47.63 | 42 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.091 | 1.65 | 0.92–2.97 | 63 | 41.4 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.86 | 1.09 | 0.44–2.68 | 47.63 | 57 | ||
| 10 | SNHG6 | OS | 1411 | 1e-06 | 1.45 | 1.25–1.68 | 91 | 57 |
| OS+LUAD | 672 | 0.033 | 1.31 | 1.02–1.67 | 125.77 | 95.07 | ||
|
OS+LUAD +Smoker |
231 | 0.33 | 1.27 | 0.78–2.07 | 47.77 | 38.1 | ||
|
OS+LUAD +Smoker+Males |
162 | 0.72 | 1.11 | 0.62–1.99 | 44.83 | 43.07 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.31 | 1.62 | 0.63–4.16 | 57 | 34.6 | ||
| 11 | TMPO-AS1 | OS | 1411 | 7.2e-08 | 1.5 | 1.29–1.74 | 99.43 | 52.97 |
| OS+LUAD | 672 | 4.1e-10 | 2.16 | 1.69–2.76 | 133.57 | 63 | ||
|
OS+LUAD +Smoker |
231 | 0.00066 | 2.34 | 1.41–3.88 | – | – | ||
|
OS+LUAD +Smoker+Males |
162 | 0.0046 | 2.33 | 1.28–4.25 | – | – | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.016 | 3.13 | 1.18–8.29 | – | – | ||
| 12 | TTN-AS1 | OS | 1411 | 0.00039 | 0.76 | 0.66–0.89 | 63 | 92.63 |
| OS+LUAD | 672 | 2.9e-05 | 0.59 | 0.46–0.76 | 76 | 117.33 | ||
|
OS+LUAD +Smoker |
231 | 0.0087 | 0.51 | 0.31–0.85 | – | – | ||
|
OS+LUAD +Smoker+Males |
162 | 0.0088 | 0.44 | 0.24–0.83 | – | – | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.39 | 0.67 | 0.27–1.67 | 37 | 47.63 | ||
| 13 | WDFY3-AS2 | OS | 1411 | 1.8e-11 | 0.6 | 0.52–0.7 | 53 | 104 |
| OS+LUAD | 672 | 4.1e-07 | 0.53 | 0.41–0.68 | 69.93 | 133.57 | ||
|
OS+LUAD +Smoker |
231 | 0.0095 | 0.52 | 0.31–0.86 | – | – | ||
|
OS+LUAD +Smoker+Males |
162 | 0.3 | 0.74 | 0.41–1.32 | 38.1 | 47.77 | ||
|
OS+LUAD+ Smoker+Females |
69 | 0.007 | 0.25 | 0.08–0.74 | – | – | ||
| 14 | ZFPM2-AS1 | OS | 2166 | 3.6e-07 | 0.73 | 0.65–0.83 | 54 | 81 |
| OS+LUAD | NA | |||||||
|
OS+LUAD +Smoker | ||||||||
|
OS+LUAD +Smoker+Males | ||||||||
|
OS+LUAD+ Smoker+Females | ||||||||
Validation of TMPO-AS1 overexpression across multiple platforms
Pan-cancer analysis using the OncoMX database further revealed that TMPO-AS1 was consistently upregulated across The Cancer Genome Atlas (TCGA) cancer types and displayed particularly high expression in lung cancer, with a fold change of 2.11 (Supplementary Table 2). Furthermore, to ensure the consistent upregulation of TMPO-AS1 in LUAD, its differential expression analysis was performed by using the UALCAN (P = 6.3e-53) and ENCORI (P = 8.0e-26) databases, confirming TMPO-AS1 to be significantly upregulated in tumor cases, as shown in Fig. 1A-B. To further substantiate these findings, R-based packages were also utilized for expression profiling’s in the normalized LUAD samples, which confirmed a marked overexpression of TMPO-AS1 in tumor tissues compared to normal tissues (P = 1.05e − 13), as illustrated in Fig. 1C.
Fig. 1.
Differential expression of TMPO-AS1 in LUAD. By using (A) UALCAN (normal n = 59, tumor n = 533), (B) ENCORI (normal n = 59, tumor n = 526), and (C) R-based packages (n = 59). Analysis of the ceRNA network by using the (D) miRNet database to find TMPO-AS1-associated miRNAs. (E) Correlation analysis between TMPO-AS1 and hsa-let-7b-5p in LUAD (n = 512). Survival analysis of hsa-let-7b-5p in lung cancer using (F) KM Plotter database, OS + LUAD (n = 513). Differential expression of hsa-let-7b-5p in LUAD samples, using (G) R-based packages (n = 46)
Identification of TMPO-AS1-associated miRNAs
Previous studies have expressed that lncRNAs can control gene expression by interacting as molecular sponges for miRNAs, thereby influencing their associated target expression and regulation [23, 24]. To identify miRNAs associated with TMPO-AS1, the miRNet database was employed, which revealed an interaction network comprising eight candidate miRNAs (Fig. 1D). Further, based on their correlation strength (R value > 0.2), the best associated miRNA with TMPO-AS1 was determined by using the ENCORI database, and miRNA hsa-let-7b-5p was found to be highly negatively correlated with TMPO-AS1 (R = −0.277) (Fig. 1E and Table 2). Furthermore, the KMP survival analysis showed down-expression of hsa-let-7b-5p to be associated with poor OS in LUAD cases (HR = 0.71, CI = 0.53–0.96, P = 0.023), with a low expression cohort (42.17 months) and a high expression cohort (54.4 months), as shown in Fig. 1F. Additionally, expression analysis of hsa-let-7b-5p in a cohort of normalized LUAD samples (Normal = 46; Tumor = 46) using R-based statistical tools confirmed the downregulation of hsa-let-7b-5p in tumor tissues as compared to normal tissues (P = 0.025), as illustrated in Fig. 1G, thus firmly strengthening the hypothesis of the sponging effect on hsa-let-7b-5p by TMPO-AS1.
Table 2.
Correlation analysis of TMPO-AS1 associated miRNAs
| S.No. | miRNAs vs. TMPO-AS1 (R-value) |
|
|---|---|---|
| 1 | hsa-miR-429 | −0.040 |
| 2 | hsa-miR-200c-3p | 0.068 |
| 3 | hsa-let-7b-5p | −0.277 |
| 4 | hsa-miR-126-5p | −0.186 |
| 5 | hsa-miR-200b-3p | −0.093 |
| 6 | hsa-miR-98-5p | 0.068 |
| 7 | hsa-let-7 g-5p | −0.110 |
| 8 | hsa-miR-199a-5p | −0.186 |
Construction of hsa-let-7b-5p-associated heterogeneous gene network and selection of key gene signatures
Further, to explore the tumor heterogeneity in the microenvironment across LUAD, a list of the top ten positively regulating genes (CPED1, EFCAB14, COLEC12, DLC1, SEPT4, TGFBR3, RNF144B, CD59, MAT2B, and CHMP3) and top ten negatively regulating genes (ESPL1, AURKB, CCNF, EZH2, PLK1, E2F2, PUS1, AURKA, KIFC1, and HASPIN) associated with hsa-let-7b-5p were identified using the CancerMIRNome database (see Supplementary Tables 3 and 4, respectively). Their correlation analysis was validated by using the ENCORI database, as shown in Figs. 2A-J and 3A-J. Furthermore, the prognostic relevance of these genes was assessed across three survival parameters: Overall Survival (OS), First Progression (FP), and Post-progression Survival (PPS). Based on statistically significant associations, six positively associated genes: CPED1 (p-value: OS: 2.3e-13, FP: 0.00048, PPS: 0.00027), COLEC12 (p-value: OS: 1.3e-08, FP: 0.00015, PPS: 0.0014), TGFBR3 (p-value: OS: 1.4e-08, FP: 1.2e-08, PPS: 0.00078), RNF144B (p-value: OS: 1.5e-09, FP: 1.1e-06, PPS: 0.0097), CD59 (p-value: OS: 9.1e-08, FP: 7.9e-06, PPS: 8.9e-05), and MAT2B (p-value: OS: 4.9e-07, FP: 3.6e-05, PPS: 0.0054), and five negatively associated genes: AURKB (p-value: OS: <1e-16, FP: 8.3e-13, PPS: 8.3e-06), E2F2 (p-value: OS: 1.2e-10, FP: 1.2e-06, PPS: 0.0079), AURKA (p-value: OS: 2.3e-11, FP: 1.1e-08, PPS: 0.01), KIFC1 (p-value: OS: <1e-16, FP: 2.3e-14, PPS: 0.00032), and HASPIN (p-value: OS: 2.5e-09, FP: 8.1e-10, PPS: 0.014) were selected for further analysis. To refine the model within the LUAD-specific clinical context, survival analyses were further stratified by histological subtype and smoking status. This stepwise filtering identified four hsa-let-7b-5p-positively regulated genes: CPED1 (p-value: OS: 0.0041), TGFBR3 (p-value: OS: 0.00045), RNF144B (p-value: OS: 0.0046), and CD59 (p-value: OS: 0.0017), and two negatively hsa-let-7b-5p-regulated genes: AURKB (p-value: OS: 3.3e-08) and KIFC1 (p-value: OS: 1.7e-06) as the most robust prognostic markers in LUAD smokers (Tables 3 and 4). This six-gene panel was therefore selected as key components of the hsa-let-7b-5p–associated heterogeneous regulatory model for subsequent immune microenvironment analysis.
Table 4.
Survival analysis of hsa-let-7b-5p-associated negative genes
| S.No. | Associated genes | Survival | Patient No. | p-value | HR | CI | Low expression cohort (months) |
High expression cohort (months) |
|---|---|---|---|---|---|---|---|---|
| OS/FP/PPS | ||||||||
| 1 | ESPL1 | OS | 2166 | 5.9e-10 | 1.45 | 1.29–1.64 | 86.27 | 51 |
| FP | 1252 | 5.5e-15 | 1.97 | 1.65–2.34 | 29 | 10.5 | ||
| PPS | 477 | 0.08 | 1.2 | 0.98–1.48 | 16.07 | 10.78 | ||
| 2 | AURKB | OS | 2166 | < 1e-16 | 1.78 | 1.58–2.01 | 93 | 43.83 |
| FP | 1252 | 8.3e-13 | 1.85 | 1.56–2.2 | 31.61 | 10 | ||
| PPS | 477 | 8.3e-06 | 1.6 | 1.3–1.98 | 21.9 | 9 | ||
| 3 | CCNF | OS | 2166 | 2.5e-06 | 1.33 | 1.18–1.5 | 85 | 55 |
| FP | 1252 | 2.5e-07 | 1.56 | 1.32–1.85 | 23 | 12 | ||
| PPS | 477 | 0.57 | 1.06 | 0.86–1.31 | 15.01 | 12 | ||
| 4 | EZH2 | OS | 2166 | 1.3e-05 | 1.3 | 1.16–1.47 | 81 | 54 |
| FP | 1252 | 5.6e-06 | 1.48 | 1.25–1.75 | 25 | 12 | ||
| PPS | 477 | 0.17 | 1.15 | 0.94–1.42 | 17.25 | 10.81 | ||
| 5 | PLK1 | OS | 2166 | < 1e-16 | 1.68 | 1.49–1.9 | 91 | 48 |
| FP | 1252 | < 1e-16 | 2.28 | 1.92–2.72 | 36 | 10 | ||
| PPS | 477 | 0.072 | 1.21 | 0.98–1.49 | 18 | 11 | ||
| 6 | E2F2 | OS | 1411 | 1.2e-10 | 1.62 | 1.4–1.89 | 103 | 51 |
| FP | 874 | 1.2e-06 | 1.72 | 1.38–2.14 | 34.9 | 15 | ||
| PPS | 242 | 0.0079 | 1.49 | 1.11–2.11 | 15.7 | 9 | ||
| 7 | PUS1 | OS | 2166 | 0.32 | 0.94 | 0.84–1.06 | 68 | 72.33 |
| FP | 1252 | 0.23 | 0.9 | 0.76–1.07 | 79 | 104 | ||
| PPS | 477 | 0.77 | 0.97 | 0.79–1.19 | 15 | 12 | ||
| 8 | AURKA | OS | 2166 | 2.3e-11 | 1.5 | 1.33–1.69 | 93 | 49.47 |
| FP | 1252 | 1.1e-08 | 1.64 | 1.38–1.94 | 27 | 11 | ||
| PPS | 477 | 0.01 | 1.31 | 1.07–1.62 | 19.47 | 10.81 | ||
| 9 | KIFC1 | OS | 2166 | < 1e-16 | 1.72 | 1.52–1.94 | 96 | 45 |
| FP | 1252 | 2.3e-14 | 1.94 | 1.63–2.3 | 30 | 10.3 | ||
| PPS | 477 | 0.00032 | 1.47 | 1.19–1.81 | 20.11 | 9.26 | ||
| 10 | HASPIN | OS | 1411 | 2.5e-09 | 1.56 | 1.35–1.81 | 95.07 | 52 |
| FP | 874 | 8.1e-10 | 1.98 | 1.58–2.47 | 43 | 13 | ||
| PPS | 242 | 0.014 | 1.44 | 1.08–1.94 | 15.7 | 9.26 | ||
| OS/FP/PPS + LUAD | ||||||||
| 1 | AURKB | OS+ LUAD | 1161 | < 1e-16 | 2.11 | 1.77–2.52 | 117.33 | 49.43 |
| FP+ LUAD | 906 | 9e-14 | 2.14 | 1.74–2.63 | 35 | 11 | ||
| PPS+ LUAD | 376 | 5e-04 | 1.53 | 1.2–1.95 | 24.4 | 11 | ||
| 2 | E2F2 | OS+ LUAD | 672 | 2e-10 | 2.2 | 1.72–2.83 | 133.57 | 63.4 |
| FP+ LUAD | 528 | 0.00015 | 1.78 | 1.31–2.4 | 48.73 | 22 | ||
| PPS+ LUAD | 141 | 0.28 | 1.26 | 0.83–1.92 | 21 | 12 | ||
| 3 | AURKA | OS/LUAD | 1161 | 4.2e-07 | 1.56 | 1.31–1.85 | 106 | 63.4 |
| FP/LUAD | 906 | 4.2e-09 | 1.82 | 1.48–2.22 | 32 | 12 | ||
| PPS/LUAD | 376 | 0.087 | 1.23 | 0.97–1.57 | 21.9 | 12 | ||
| 4 | KIFC1 | OS/LUAD | 1161 | 1.6e-13 | 1.91 | 1.61–2.28 | 114 | 52 |
| FP/LUAD | 906 | 6.6e-14 | 2.15 | 1.75–2.64 | 33.23 | 11 | ||
| PPS/LUAD | 376 | 0.0033 | 1.43 | 1.13–1.83 | 22 | 11 | ||
| 5 |
HASPIN (Gsg2) |
OS/LUAD | 672 | 0.014 | 1.35 | 1.06–1.71 | 117.33 | 86 |
| FP/LUAD | 528 | 0.027 | 1.4 | 1.04–1.88 | 38.13 | 22 | ||
| PPS/LUAD | 141 | 0.35 | 1.22 | 0.8–1.86 | 21 | 13.57 | ||
| OS+LUAD+SMOKERS | ||||||||
| 1 | AURKB | OS+LUAD+ SMOKERS | 546 | 3.3e-08 | 2.1 | 1.61–2.75 | 116 | 57 |
| 2 | KIFC1 | OS+LUAD+ SMOKERS | 546 | 1.7e-06 | 1.9 | 1.45–2.47 | 114 | 57 |
Fig. 2.
Correlation analysis between hsa-let-7b-5p and all positively associated genes in LUAD samples. By using ENCORI: (A) CPED1 (n = 512), (B) EFCAB14 (n = 512), (C) COLEC12 (n = 512), (D) DLC1 (n = 512), (E) SEPT4 (n = 512), (F) TGFBR3 (n = 512), (G) RNF144B (n = 512), (H) CD59 (n = 512), (I) MAT2B (n = 512), (J) CHMP3 (n = 512)
Fig. 3.
Correlation analysis between hsa-let-7b-5p and all negatively associated genes in LUAD samples. By using ENCORI: (A) ESPL1 (n = 512), (B) AURKB (n = 512), (C) CCNF (n = 512), (D) EZH2 (n = 512), (E) PLK1 (n = 512), (F) E2F2 (n = 512), (G) PUS1 (n = 512), (H) AURKA (n = 512), (I) KIF1 (n = 512), (J) HASPIN (n = 512)
Table 3.
Survival analysis of hsa-let-7b-5p-associated positive genes
| S. No. | Associated genes | Survival | Patient No. | p-value | HR | CI | Low expression cohort (months) | High expression cohort (months) |
|---|---|---|---|---|---|---|---|---|
| OS/FP/PPS | ||||||||
| 1 | CPED1 | OS | 1411 | 2.3e-13 | 0.58 | 0.5–0.67 | 51 | 107 |
| FP | 874 | 0.00048 | 0.68 | 0.55–0.84 | 14 | 33.23 | ||
| PPS | 242 | 0.00027 | 0.58 | 0.43–0.78 | 8 | 16.07 | ||
| 2 | EFCAB14 | OS | NA | |||||
| FP | ||||||||
| PPS | ||||||||
| 3 | COLEC12 | OS | 2166 | 1.3e-08 | 0.71 | 0.63–0.8 | 52 | 84 |
| FP | 1252 | 0.00015 | 0.72 | 0.61-0.085.61.085 | 70 | 104.9 | ||
| PPS | 477 | 0.0014 | 0.71 | 0.58–0.88 | 9 | 19 | ||
| 4 | DLC1 | OS | 2166 | 1e-09 | 1.45 | 1.29–1.64 | 86 | 52 |
| FP | 1252 | 0.034 | 1.2 | 1.01–1.42 | 102 | 81 | ||
| PPS | 477 | 0.00019 | 1.49 | 1.2–1.83 | 20 | 9 | ||
| 5 | SEPT4 | OS | 2166 | 0.069 | 0.9 | 0.8–1.01 | 65 | 72.33 |
| FP | 1252 | 0.088 | 0.86 | 0.73–1.02 | 88 | 104 | ||
| PPS | 477 | 0.58 | 1.06 | 0.86–1.31 | 13.57 | 13.2 | ||
| 6 | TGFBR3 | OS | 2166 | 1.4e-08 | 0.71 | 0.63–0.8 | 53 | 85 |
| FP | 1252 | 1.2e-08 | 0.61 | 0.52–0.73 | 50 | 164 | ||
| PPS | 477 | 0.00078 | 0.7 | 0.57–0.86 | 10 | 20 | ||
| 7 | RNF144B | OS | 1411 | 1.5e-09 | 0.63 | 0.55–0.74 | 52 | 91 |
| FP | 874 | 1.1e-06 | 0.58 | 0.47–0.73 | 14 | 34.9 | ||
| PPS | 242 | 0.0097 | 0.68 | 0.5–0.91 | 8.98 | 15 | ||
| 8 | CD59 | OS | 1411 | 9.1e-08 | 0.67 | 0.58–0.78 | 52 | 96.2 |
| FP | 874 | 7.9e-06 | 0.61 | 0.49–0.76 | 14.43 | 35 | ||
| PPS | 242 | 8.9e-05 | 0.56 | 0.41–0.75 | 8 | 20 | ||
| 9 | MAT2B | OS | 2166 | 4.9e-07 | 0.74 | 0.66–0.83 | 57 | 81 |
| FP | 1252 | 3.6e-05 | 0.7 | 0.59–0.83 | 12 | 22.53 | ||
| PPS | 477 | 0.0054 | 0.75 | 0.61–0.92 | 10.38 | 17.67 | ||
| 10 | CHMP3 | OS | 2166 | 9.4e-07 | 0.74 | 0.66–0.84 | 56 | 84 |
| FP | 1252 | 1.5e-10 | 0.58 | 0.48–0.68 | 11 | 26.33 | ||
| PPS | 477 | 0.2 | 0.87 | 0.71–1.08 | 11.4 | 15 | ||
| OS/FP/PPS+LUAD | ||||||||
| 1 | CPED1 | OS+ LUAD | 672 | 8e-12 | 0.42 | 0.32–0.54 | 69 | 175 |
| FP+ LUAD | 528 | 0.0012 | 0.61 | 0.45–0.83 | 21 | 43.07 | ||
| PPS+ LUAD | 141 | 0.0054 | 0.55 | 0.36–0.84 | 11 | 22 | ||
| 2 | COLEC12 | OS+ LUAD | 1161 | 7.8e-07 | 0.65 | 0.54–0.77 | 62 | 96.2 |
| FP + LUAD | 906 | 1.6e-06 | 0.61 | 0.5–0.75 | 50 | 164 | ||
| PPS+ LUAD | 376 | 0.13 | 0.83 | 0.65–1.05 | 13.57 | 20 | ||
| 3 | TGFBR3 | OS+ LUAD | 672 | 3.9e-10 | 0.45 | 0.35–0.58 | 66.47 | 175 |
| FP+ LUAD | 528 | 1.6e-05 | 0.52 | 0.38–0.7 | 19 | 48.73 | ||
| PPS+ LUAD | 141 | 0.0061 | 0.56 | 0.36–0.85 | 10.81 | 24.47 | ||
| 4 | RNF144B | OS+ LUAD | 672 | 1.4e-06 | 0.54 | 0.42–0.7 | 24.33 | 49.4 |
| FP+ LUAD | 528 | 0.014 | 0.69 | 0.51–0.93 | 25 | 41.4 | ||
| PPS+ LUAD | 141 | 0.00043 | 0.47 | 0.31–0.72 | 11 | 27.07 | ||
| 5 | CD59 | OS+ LUAD | 672 | 6.8e-09 | 0.48 | 0.38–0.62 | 69 | 133.57 |
| FP+ LUAD | 528 | 0.0022 | 0.63 | 0.46–0.85 | 21.3 | 42.7 | ||
| PPS+ LUAD | 141 | 0.00014 | 0.44 | 0.29–0.68 | 10.38 | 27.07 | ||
| 6 | MAT2B | OS+ LUAD | 1161 | 5.1e-08 | 0.62 | 0.52–0.74 | 64 | 107 |
| FP+ LUAD | 906 | 1.9e-06 | 0.62 | 0.5–0.75 | 13 | 26.33 | ||
| PPS+ LUAD | 376 | 0.038 | 0.78 | 0.61–0.99 | 13 | 21 | ||
| OS+LUAD+SMOKERS | ||||||||
| 1 | CPED1 | OS+LUAD+ SMOKERS | 231 | 0.0041 | 0.48 | 0.29–0.8 | - | - |
| 2 | TGFBR3 | OS+LUAD+ SMOKERS | 231 | 0.00045 | 0.41 | 0.24–0.68 | - | - |
| 3 | RNF144B | OS+LUAD+ SMOKERS | 231 | 0.0046 | 0.49 | 0.3–0.81 | - | - |
| 4 | CD59 | OS+LUAD+ SMOKERS | 231 | 0.0017 | 0.45 | 0.27–0.75 | 35 | 68 |
| 5 | MAT2B | OS+LUAD+ SMOKERS | 231 | 0.1 | 0.8 | 0.62–1.04 | 71 | 93 |
Immune infiltration patterns associated with the regulatory axis
In normal conditions, immune cells play a pivotal role in maintaining the homeostasis of the body. Certain immune cells are also involved in tumor invasion, EMT transition (epithelial-mesenchymal transition), and their metastasis, hence any dysregulation in these bioprocesses might lead to tumorigenesis [25, 26]. To understand the immunological interactions of the selected genes in the tumor microenvironment, the study further examined the correlations between gene expression and immune cell infiltration levels using the GSCA database. Distinct immune-interaction patterns for both the positively and negatively regulated gene groups, along with the TMPO-AS1, were observed, as shown in Fig. 4A-C, respectively. While multiple immune cell types were assessed, only those demonstrating the most consistent and biologically relevant correlation patterns are presented for clarity. Among the positively associated genes (TGFBR3, RNF144B, and CD59), a combined analysis showed a highly significant negative correlation with nTreg immune cells (R = −0.53, −0.56, and − 0.37, respectively), indicating that increased expression of such genes may inhibit the anti-cancer immune response of nTreg. In contrast, these same genes exhibited positive correlation with NKT cells: TGFBR3, RNF144B, and CD59 (R = 0.52, 0.54, and 0.38, respectively), suggesting that they may enhance NKT cell infiltration within the tumor microenvironment, as mentioned in Table 5. On the other hand, the genes negatively associated with hsa-let-7b-5p (AURKB and KIFC1) displayed positive correlations with nTreg cells (R = 0.56 and 0.55, respectively) and negative correlations with CD4 + T cells (R = −0.39 and − 0.40, respectively), as mentioned in Table 6. Similarly, with this, TMPO-AS1 also exhibited positive correlations with nTreg cells (R = 0.45) and negative correlations with CD4 + T cells (R = −0.24), as mentioned in Table 7. These interaction patterns suggest that upregulation of these negatively associated genes, along with TMPO-AS1, might promote an nTreg-dominant oncogenic immune microenvironment while suppressing broader CD4 + T cell infiltration, which could contribute to immune evasion in LUAD. Notably, nTreg cells represent a distinct immunosuppressive subset of CD4⁺ T cells, and their enrichment may not directly correspond with total CD4⁺ T cell abundance. This divergence likely reflects functional heterogeneity within the CD4⁺ T cell compartment and a shift toward an immunosuppressive tumor microenvironment. Collectively, these outcomes indicate that the TMPO-AS1–hsa-let-7b-5p regulatory axis may influence LUAD progression not only through transcriptional heterogeneity but also by modulating the tumor immune landscape. Upregulation of hsa-let-7b-5p–positively associated genes appear to favor an antitumor immune profile characterized by enhanced NKT cell infiltration and reduced nTreg activity, whereas suppression of this axis may promote a pro-tumor immune microenvironment dominated by nTregs and diminished CD4 + T cell responses.
Fig. 4.
Tumor immune infiltration analysis. (A) Positively associated genes, (B) Negatively associated genes, and (C) TMPO-AS1 by using the GSCA database
Table 5.
Immune cells Vs positive genes
| S.No. | Genes | Correlation | P- value |
|---|---|---|---|
| Positive regulators | |||
| Genes vs. nTreg Immune cells | |||
| 1 | CPED1 | −0.45 | 2.44e-30 |
| 2 | TGFBR3 | −0.53 | 2.94e-43 |
| 3 | RNF144B | −0.56 | 7.47e-49 |
| 4 | CD59 | −0.37 | 2.44e-20 |
| Genes Vs NKT Immune Cells | |||
| 1 | CPED1 | 0.58 | 2.14e-52 |
| 2 | TGFBR3 | 0.52 | 1.96e-40 |
| 3 | RNF144B | 0.54 | 4.29e-45 |
| 4 | CD59 | 0.38 | 1.14e-21 |
Table 6.
Immune cells Vs negative genes
| S.NO | Genes | Correlation | P- value |
|---|---|---|---|
| Negative regulators | |||
| Genes vs. nTreg Immune cells | |||
| 1 | AURKB | 0.56 | 4.02e-48 |
| 2 | KIFC1 | 0.55 | 4.61e-47 |
| Genes Vs CD4 + T Immune Cells | |||
| 1 | AURKB | −0.39 | 7.07e-22 |
| 2 | KIFC1 | −0.40 | 4.87e-23 |
Table 7.
Immune cells Vs TMPO-AS1
| S.NO. | Immune cells | Correlation | P- value |
|---|---|---|---|
| TMPO-AS1 vs. Immune cells | |||
| 1 | nTreg | 0.45 | 1.86e-29 |
| 2 | CD4 + T | −0.24 | 5.15e-9 |
Discussion
A comprehensive understanding of cancer progression requires an integrative model that captures the dynamic interplay of the ceRNA network along with the tumor-infiltrating immune populations [27, 28]. Within this regulatory network, lncRNAs modulate gene expression by performing as competing endogenous RNAs, sequestering miRNAs via a sponging process and thereby minimising miRNA-mediated suppression of target transcripts, thereby potentially influencing oncogenic and immune-related signalling pathways [29–31]. Guided by this framework, the present study investigates a specific lncRNA–miRNA regulatory circuit involved in LUAD progression, focusing on the dysregulation of lncRNA TMPO-AS1 and its interaction with hsa-let-7b-5p, and examining how this axis influences associated gene programs and the infiltration patterns.
Several lncRNAs, such as MALAT1 [32], HOTAIR [33], and PCA3 [34], etc., are well established for their roles in cancer metastasis and progression. So, building on this knowledge, we initiated our analysis by systematically screening the upregulated lncRNAs in LUAD. A list of sixteen candidate lncRNAs was sorted out, among which TMPO-AS1 emerged as the only transcript most significantly associated with poor overall survival in LUAD. This observation was further supported by previous reports demonstrating TMPO-AS1 overexpression in breast invasive carcinoma [35], prostate cancer [36], and lung cancer [14, 37], which further strengthened our findings. TMPO-AS1 is a long non-coding RNA transcript (> 200 nucleotides) that comes from a part of the genome that doesn’t code for proteins i.e., the non-coding genomic regions [38], and because of its unique compact size, it can be considered as an appropriate candidate for small-molecule-based targeted delivery and may function as a prospective biomarker in cancer biology [15]. TMPO-AS1 has been implicated in multiple biological events, including the mTOR, PI3K, and Akt cell-signalling pathways [39], and has been reported to sponge several tumor-suppressive miRNAs, such as miR-200c, miR-320a, and miR-329-3p, thereby promoting dysregulated gene expression and tumorigenesis [40].
To delineate the sponging mechanism of TMPO-AS1 in LUAD, we constructed its associated ceRNA network and identified eight candidate interacting miRNAs. Among these, hsa-let-7b-5p exhibited the strongest negative correlation with TMPO-AS1, indicating a prominent regulatory interaction. Also, given the heterogeneous nature of cancer, we next examined the gene-level heterogeneity governed by the hsa-let-7b-5p regulatory axis. A heterogeneity model was developed to identify the top ten positively and top ten negatively associated target genes of hsa-let-7b-5p. Notably, the positively associated genes, CPED1, TGFBR3, RNF144B, and CD59, have established tumor-suppressive functions and were significantly downregulated in parallel with hsa-let-7b-5p suppression under cancerous conditions. In contrast, the negatively associated genes, AURKA and KIFC1, have exhibited oncogenic potentials and were upregulated upon hsa-let-7b-5p downregulation under cancerous conditions. Together, these findings establish a gene panel influenced by TMPO-AS1-mediated miRNA sponging and underscore the contribution of this axis to transcriptional heterogeneity in LUAD.
Beyond this, an in-depth investigation of the ceRNA network in relation to immune cell infiltration in LUAD was performed. Analysis of immune cell infiltration revealed that nTreg invasion was found to be positively associated with the TMPO-AS1 and negatively associated with hsa-let-7b-5p expression. In contrast, infiltration of NKT cells and CD4 + T cells exhibited an opposite trend, showing negative associations with TMPO-AS1 and a positive association with hsa-let-7b-5p. These reciprocal trends indicate that elevated TMPO-AS1 expression, together with increased nTreg infiltration, contributes to an oncogenic, pro-tumoral microenvironment, whereas restoration of hsa-let-7b-5p expression and enrichment of NKT and CD4 + T cells support an immunosuppressive, anti-tumor immune landscape, which is also supported by multiple previous studies that focus on how these immune cells get modulated under cancerous conditions [41–45]. However, this study is limited by the focused evaluation of selected immune cell populations. Other functionally relevant immune subsets, such as CD8⁺ T cells, macrophage subtypes (M1/M2), dendritic cells, and B cells, were not comprehensively analyzed. Inclusion of these populations, along with deeper functional subclassification, may provide a more complete understanding of the tumor immune microenvironment and should be explored in future studies. While this study provides a systems-level view of the TMPO-AS1/hsa-let-7b-5p regulatory axis, the findings are derived from bulk transcriptomic datasets and lack single-cell resolution. Future studies incorporating single-cell RNA sequencing and experimental validation will be necessary to delineate cell-type–specific regulatory mechanisms and confirm causality.
Collectively, the integrated analysis of the TMPO-AS1/hsa-let-7b-5p regulatory axis, its downstream gene programs, and immune infiltration patterns highlights a coordinated post-transcriptional and immunological mechanism driving LUAD progression and metastasis. This competitive endogenous RNA-regulatory network underscores the emerging significance of TMPO-AS1 as a biomarker and therapeutic target and provides a framework for the development of more precise and personalized RNA-based and immune-modulatory strategies in cancer therapy.
Conclusion
In conclusion, this study identifies TMPO-AS1 as a significantly overexpressed lncRNA in lung adenocarcinoma, strongly associated with poor clinical outcomes. Through integrative multi-database analysis, we establish a TMPO-AS1/hsa-let-7b-5p regulatory axis that contributes to transcriptional heterogeneity by modulating key downstream genes. Furthermore, immune infiltration analysis suggests that this regulatory network is associated with distinct immunological patterns, particularly involving nTreg, NKT, and CD4⁺ T cell populations, indicating a potential role in shaping the tumor immune microenvironment. Taken together, these findings highlight TMPO-AS1 as a potential prognostic biomarker associated with unfavorable outcomes and provide a foundation for future experimental validation of this regulatory axis as a candidate target for RNA-based and immunomodulatory therapeutic strategies in LUAD.
Methodology
lncRNA expression analysis in LUAD
A list of upregulated lncRNAs associated with cell growth, survival, and miRNA regulation in LUAD was sorted out from the lnc2cancer3.0 database [46]. Subsequently, their prognostic significance was evaluated using the KMP database [47], which correlates gene expression with patient overall survival (OS) using non-parametric statistics and allows stratification based on histological subtype (LUAD), smoking status, and gender. The “gene symbol and the corresponding Affy ID used for the analysis are as follows: FBXL19-AS1 (1553586_at), HCP5 (206082_at), HMMR-AS1 (1557029_at), HOXA11-AS (230666_at), LINC00460 (1558930_at), LINC00467 (224444_s_at), MAFG-AS1 (1559352_a_at), MALAT1 (224559_at), SBF2-AS1 (242786_at), SNHG6 (225547_at), TMPO-AS1 (227578_at), TTN-AS1 (1556043_a_at), WDFY3-AS2 (1562953_s_at), and ZFPM2-AS1 (219778_at). Next, expression levels of TMPO-AS1 were assessed across all TCGA cancers using the OncoMX [48] database. Furthermore, the differential expression of TMPO-AS1 has been validated using the UALCAN [49]. and ENCORI [50] databases. In addition, RNA-seq expression data from the TCGA-LUAD cohort were retrieved and analyzed using R (version 4.4.2). Data acquisition and preprocessing were performed using the TCGAbiolinks package (version 2.34.1). Data manipulation and visualization were conducted using tidyverse (version 2.0.0), dplyr (version 1.1.4), ggplot2 (version 4.0.1), and ggpubr (version 0.6.2). Gene annotation was carried out using biomaRt (version 2.62.1), while data structures were managed using SummarizedExperiment (version 1.36.0). Additional packages including maftools (version 2.22.0) and pheatmap (version 1.0.13) were used where applicable. Statistical analysis was performed using the Wilcoxon rank-sum test. To ensure reproducibility of random sampling, a fixed seed was applied (set.seed = 123).
Identification and prognostic evaluation of TMPO-AS1-associated miRNAs
Potential miRNAs interacting with TMPO-AS1 were further identified using the miRNet 2.0 database [51], which employs a central network topology to assess the proximity range between the genes and targeted molecules. Homo sapiens was selected as the organism, and TMPO-AS1 was queried to retrieve all validated interacting miRNAs and construct the interaction network. Subsequently, the expression correlation between TMPO-AS1 and the identified miRNAs was analyzed using the ENCORI (StarBase v3.0) platform, which integrates CLIP-Seq and degradome sequencing data. Based on correlation strength, the most significantly associated miRNA was selected for further evaluation. Kaplan–Meier survival analysis was performed using the KMP database, where LUAD patients were stratified into high- and low-expression groups according to the median expression of miRNA, and OS was compared. Hazard ratios (HRs), 95% confidence intervals (CIs), and log-rank P-values were assessed for statistical significance.
Construction of hsa-let-7b-5p-associated heterogeneous gene model
To investigate the gene expression heterogeneity associated with hsa-let-7b-5p in LUAD, the CancerMIRNome database [52] was utilized to analyze miRNA–mRNA correlations within the TCGA-LUAD cohort. Genes showing significant positive or negative correlations with hsa-let-7b-5p expression were identified, and the top ten positively as well as top ten negatively correlated genes were selected based on Pearson correlation coefficients and corresponding p-values, representing potential downstream effectors of hsa-let-7b-5p–mediated regulation. To validate these associations, correlation analyses between hsa-let-7b-5p and the identified candidate genes were independently confirmed using the ENCORI database. Pearson’s correlation coefficients (R) and corresponding p-values were calculated, and graphical correlation plots were generated to assess the strength and direction of expression relationships. Subsequently, the prognostic relevance of these genes was assessed using the KMP database. Each gene was analyzed across three distinct survival parameters: Overall Survival (OS), First Progression (FP), and Post-Progression Survival (PPS). The study specifically focused on the LUAD histological subtype, and patients were categorised into high and low expression groups according to median expression levels. Only genes demonstrating statistically significant associations (p < 0.05) were retained for further analysis, with additional stratification based on smoking status. The “gene symbol, and the corresponding Affy ID used for the analysis of all ten positively associated genes are CPED1 (228728_at), COLEC12 (221019_s_at), DLC1 (200703_at), SEPT4 (210657_s_at), TGFBR3 (226625_at), RNF144B (228153_at), CD59 (228748_at), MAT2B (217993_s_at), and CHMP3 (217837_s_at), and for all ten negatively associated genes are ESPL1 (38158_at), AURKB (209464_at), CCNF (204826_at), EZH2 (203358_s_at), PLK1 (202240_at), E2F2 (228361_at), PUS1 (218670_at), AURKA (208079_s_at), KIFC1 (209680_s_at), and HASPIN (223759_s_at).
Evaluation of immunological significance
The immunological relevance of the hsa-let-7b-5p–associated genes was investigated using the Gene Set Cancer Analysis (GSCA) database [53], which integrates multi-omics data to evaluate gene expression and immune cell infiltration patterns. The TCGA-LUAD cohort was selected to evaluate associations between gene expression levels and tumor-infiltrating immune cell populations. The positively regulated genes and negatively regulated genes identified from the heterogeneous gene model were analyzed for their correlations with immune infiltration scores. The GSCA immune infiltration module, based on single-sample gene set enrichment analysis, was used to estimate the relative abundance of various immune cell subsets in LUAD tumor samples. Among the various immune cell populations available in the GSCA dataset, nTreg, NKT, and CD4⁺ T cells were prioritized for detailed analysis based on (i) their significant correlation coefficients with the identified gene panel and TMPO-AS1, and (ii) their well-established roles in tumor immune regulation, including immune suppression, cytotoxic immune surveillance (NKT), and adaptive immune response modulation. Spearman’s correlation analysis was used to determine the relationship between the expression of each gene and immune cell infiltration levels; corresponding correlation coefficients (r) and p-values were extracted.
Supplementary Information
Acknowledgements
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) and DST-FIST project (DST/2022/1012) from Govt. of India to Departmental of Biosciences, Manipal University Jaipur.
Abbreviations
- AURKA
Aurora kinase A
- AURKB
Aurora kinase B
- CCNF
Cyclin F
- CD4 + T
Cluster of Differentiation 4-positive T Lymphocytes
- CD59
CD59 Molecule (CD59 Blood Group)
- ceRNA
Competitive Endogenous RNA Network
- CHMP3
Charged Multivesicular Body Protein 3
- CI
Confidence Intervals
- COLEC12
Collectin Subfamily Member 12
- CPED1
Cadherin-Like And PC-esterase Domain Containing 1
- DLC1
DLC1 Rho GTPase Activating Protein
- E2F2
E2F Transcription Factor 2
- EFCAB14
EF-hand Calcium-Binding Domain 14
- ENCORI
Encyclopedia of RNA Interactomes
- ESPL1
Extra Spindle Pole Bodies Like 1, Separase
- EZH2
Enhancer Of Zeste 2 Polycomb Repressive Complex 2 Subunit
- FBXL19-AS1
F-box and Leucine-rich Repeat Protein 19 Antisense RNA 1
- FP
First Progression
- GSCA
Gene Set Cancer Analysis
- HASPIN
Histone H3 Associated Protein Kinase
- HCP5
HLA Complex P5
- HMMR-AS1
Hyaluronan-Mediated Motility Receptor Antisense RNA 1
- HOXA11-AS
HOXA (Homeobox A) 11 Antisense RNA
- HR
Hazard Ratios
- hsa-let-7b-5p
Homo sapiens (human) let-7b microRNA, 5p arm
- hsa-let-7g-5p
Human microRNA let-7 g, 5p strand
- hsa-miR-126-5p
Homo sapiens microRNA-126-5p
- hsa-miR-199a-5p
Homo sapiens (human) microRNA-199a-5p
- hsa-miR-200b-3p
Homo sapiens (human) microRNA-200b, 3p strand
- hsa-miR-200c-3p
Homo sapiens (human) microRNA-200c, the 3p strand
- hsa-miR-429
Homo sapiens (human) microRNA-429
- hsa-miR-98-5p
Homo sapiens (human) microRNA-98, 5p arm
- KIFC1
Kinesin Family Member C1
- KMP
Kaplan Meier Plotter
- LINC00460
Long intergenic non-protein coding RNA 460
- LINC00467
Long intergenic non-protein coding RNA 467
- lncRNA
Long non-coding RNA
- LUAD
Lung Adenocarcinoma
- MAFG-AS1
MAFG Antisense RNA 1
- MALAT1
Metastasis-Associated Lung Adenocarcinoma Transcript 1
- MAT2B
Methionine Adenosyltransferase 2B
- miRNA
MicroRNA
- miRNet
miRNA-centric network visual analytics platform
- mRNA
Messenger RNA
- NKT
Natural Killer T cells
- nTreg
Natural regulatory T cells
- OS
Overall Survival
- PLK1
Polo Like Kinase 1
- PPS
Post Progression Survival
- PUS1
Pseudouridine Synthase 1
- RNF144B
Ring Finger Protein 144B
- SBF2-AS1
SET Binding Factor2 Antisense RNA 1
- SEPT4
Septin 4
- SNHG6
Small nucleolar RNA Host Gene 6
- TCGA
The Cancer Genome Atlas
- TGFBR3
Transforming Growth Factor Beta Receptor 3
- TMPO-AS1
Thymopoietin Antisense RNA 1
- TTN-AS1
Titin Antisense RNA 1
- UALCAN
The University of ALabama at Birmingham CANcer data analysis Portal
- WDFY3-AS2
WD Repeat and FYVE Domain Containing 3 Antisense RNA 2
- ZFPM2-AS1
Zinc Finger Protein, FOG Family Member 2 Antisense RNA 1
Author contributions
RN: Conception, study design, critical reading, intellectual assessment of the manuscript, preparation of the manuscript, and final approval. SN: Study design and preparation of the manuscript, critical review. CS: Study design and preparation of the manuscript. PV: Study design and preparation of the manuscript. BB: Study design and preparation of the manuscript. PP: Study design and preparation of the manuscript. KJ: Study design and preparation of the manuscript.
Funding
Open access funding provided by Manipal University Jaipur.
Data availability
The data for this in-silico study were sourced from publicly accessible databases. The respective links and references for these datasets are provided for transparency and reproducibility. The complete list of databases, along with all R scripts and visualization is available in our GitHub repository: [https://github.com/sakshinirmal24/TMPO-AS1](https:/github.com/sakshinirmal24/TMPO-AS1). Further inquiries can be directed to the corresponding author.
Declarations
Ethics approval and consent to participate
Since this study exclusively utilized publicly available online databases for data extraction and analysis, ethical clearance was not required as per institutional guidelines.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data for this in-silico study were sourced from publicly accessible databases. The respective links and references for these datasets are provided for transparency and reproducibility. The complete list of databases, along with all R scripts and visualization is available in our GitHub repository: [https://github.com/sakshinirmal24/TMPO-AS1](https:/github.com/sakshinirmal24/TMPO-AS1). Further inquiries can be directed to the corresponding author.