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. 2026 Jan 3;23:5. doi: 10.1186/s12014-025-09570-4

A succinylation-related prognostic model for predicting lung adenocarcinoma prognosis and guiding immunotherapy

Zongyu Li 1, Qingqing Liu 1, Erdan Lu 1, Tiantian Zhang 1, Yan Zhu 1,✉
PMCID: PMC12866607  PMID: 41484817

Abstract

Background

Lung adenocarcinoma (LUAD) is a common culprit of cancer-related deaths. Recent studies have revealed that succinylation-related genes (SRGs) are pivotal in cancer. However, the comprehensive characteristics and clinical significance of SRGs in LUAD occurrence are not clear. Therefore, our goal is to dig out the succinylation-related prognostic feature genes in LUAD.

Methods

We identified differentially expressed SRGs in LUAD, and established the LUAD prognostic model using analyses of multivariate, LASSO, and univariate Cox regression. Based on clinical information and riskscore, we graphed a nomogram of the prognostic model and analyzed the independent prognostic ability of the riskscore. Analyses of immune assessment, mutation frequency, and drug sensitivity were carried out on LUAD patients.

Results

A 9-gene prognostic model was successfully set up in this project. The receiver operation characteristic (ROC) curves illustrated that the model effectively predicted the risk of LUAD patients. The levels of immune infiltration and immune scores of LUAD patients in the high-risk (HR) group were greatly lower than those in the low-risk (LR) group. Furthermore, compared to the LR group, the HR group had a significantly elevated gene mutation rate. ENPP3 and SLC22A8 may respond to targeted drugs more sensitively. The low-expression groups of ENPP3 and SLC22A8 genes may have higher drug sensitivity to Nilotinib, ARRY-614, and Megestrol acetate, with lower drug resistance.

Conclusion

The above results indicated that the prognostic model established using SRGs can be a predictive marker for LUAD prognosis, offering references for LUAD treatment and evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-025-09570-4.

Keywords: Immune infiltration, Immunotherapy, Lung adenocarcinoma, Prognosis, Succinylation

Introduction

One of the leading culprits of cancer-related deaths is lung cancer (LC) [1], with lung adenocarcinoma (LUAD) being the most prevalent histological subtype, accounting for approximately 40% of LC cases [2, 3]. The features of LUAD are early recurrence, unfavorable prognosis, and rapid progression, with an increasing incidence [4]. Research on potential therapeutic targets for LUAD is expanding. Targeted therapy for sensitive gene such as EGFR, KRAS, BRAF and MET has brought survival benefits to LUAD patients. However, the survival time of advanced LUAD patients without sensitive genes is still short [5–8]. In addition, despite some advances in the clinical management of LUAD, the overall prognosis of LUAD patients remains relatively poor, with a 5-year overall survival rate of less than 20% [9]. Therefore, innovating new therapeutic targets and markers for LUAD prognosis is in desperate need.

One type of post-translational modification process that can regulate the structure of proteins by transferring succinyl groups (-CO-CH2-CH2-CO2H) to residues of target proteins is called succinylation [10, 11]. It can be enzymatic or non-enzymatic and requires succinyl-CoA produced by mitochondria or peroxisomes [12]. Succinylation participates in regulating oxidative stress and mitochondrial function in addition to affecting the stability, activity, and subcellular localization of metabolic enzymes in key metabolic pathways, thus exerting a great influence in various diseases including cancer [11]. In a proteomic study, 161 differentially expressed lysine succinylation sites were observed in renal cell carcinoma (RCC) tissues, along with great changes in the succinylation levels of PKM2 and PGK1, highlighting the promoting role of glycolysis in RCC progression and the significance of lysine succinylation in energy metabolism [13]. The study demonstrated that SUCLA2 pS79 and GLS K311 succinylation levels were linked with each other and positively linked to dismal prognosis of patients with advanced pancreatic ductal adenocarcinoma (PDAC) [14]. Additionally, in LUAD, it was also found that the upregulated expression of SUCLG2 (Succinyl-CoA synthetase GDP-forming subunit β) was closely related to the low survival rate of patients. Moreover, SUCLG2 gene knockout induced mitochondrial dysfunction through succinylation and significantly inhibited the proliferation of LUAD cells [15]. In summary, the regulation of succinylation modification in metabolism has become a new hot spot in cancer studies including LUAD. However, the study of succinylation-related prognostic genes in LUAD remains unclear.

This project delved into succinylation-related genes (SRGs) associated with LUAD prognosis. Firstly, SRGs associated with LUAD prognosis were screened in LUAD, followed by the construction of a LUAD risk prognostic model to classify patients and predict the survival information of LUAD patients based on clinical information. Then, we launched the immune infiltration analysis, enrichment analysis, and tumor mutation analysis on the high-risk (HR) and low-risk (LR) groups to explore the survival differences between the two groups of patients. Finally, we also conducted a drug sensitivity analysis on the feature genes in LUAD. This project proffered a reference for the evaluation of LUAD prognosis from an immunological perspective.

Materials and methods

Data download

From the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/, visited on April 17, 2023), we downloaded Gene expression profiles and clinical data information (gender, age, TNM stage, tumor grade) of LUAD samples (normal sample: 59; cancer sample: 541). LUAD patient samples with survival time exceeding 30 days were selected for survival analysis. The detailed clinicopathological information on these LUAD patients is presented in Table S1. We obtained SRGs from the GeneCards database (https://www.genecards.org/) and selected genes with higher scores based on the median values of Relevance score. From the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/) database, the validation set GSE26939 was downloaded. We included samples with survival time greater than 30 days and complete survival information.

Differentially expressed genes (DEGs) identification and cluster analysis

We carried out differential expression analysis (DEA) (FDR < 0.05, |log (FC)|>1) on the obtained cancer samples and normal samples using the edgeR package, yielding DEGs. The intersection of SRGs and DEGs was taken to obtain DEGs related to SRGs (SRGs_DEGs). FDR was obtained by multiple test correction of the original p value through Benjamini-Hochberg (BH) method.

Prognostic model construction and verification

We performed univariate Cox regression analysis (p < 0.05) as well as LASSO analysis on SRGs_DEGs, with the optimal parameter λ adjusted through ten-fold cross-validation. By utilizing the survminerR package, the genes selected from the above regression analysis were subjected to multivariate Cox regression analysis. We constructed the prognostic model. The riskscores were calculated by utilizing the following formula:

graphic file with name d33e295.gif

Patients were clustered into HR and LR groups, with the median risk value as the cut-off point. We compared the differences in survival rates using Kaplan-Meier (KM) survival curves. The receiver operation characteristic (ROC) curves over time were generated to evaluate the sensitivity and specificity of the training set of TCGA and the validation set of GSE26939. Finally, we drew the expression patterns of feature genes, as well as the distribution of survival status and riskscores in the TCGA and GEO cohorts.

Prognostic nomogram establishment and validation

The univariate and multivariate Cox regression methods were applied in the assessment of riskscore impact on patient prognosis. Based on these, a prognostic nomogram was generated when we combined the prognostic model with multivariate regression analysis. We plotted the ROC curves of the nomogram to measure model’s predictive ability.

Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO)

Using the edgeR package with |log (FC)| >1 and FDR < 0.05 as thresholds, we performed differential analysis on the HR and LR groups to obtain DEGs. By utilizing the clusterProfiler package, KEGG and GO analyses of DEGs were performed, with the results visualized with the use of the enrichplotR package.

Tumor-infiltrating immune cell analysis

We applied the estimate to the calculation of stromal scores, immune scores, and ESTIMATE scores for each sample as well as the Wilcoxon test to data comparison and violin plots generation between HR and LR groups. CIBERSORT was employed for immune cell infiltration analysis. Meanwhile, the ssGSEA method was utilized to analyze the levels of immune functions and immune cells in different groups.

Prediction of sensitivity to immunotherapy response

The TIDE scores were applied to predict the likelihood of immune escape in HR and LR groups. The Wilcoxon test was conducted, with violin plots between the two groups plotted. The immunophenoscore (IPS) of LUAD patients was available for download from The Cancer Immunome Atlas (TCIA, https://tcia.at/home) database, with the Wilcoxon test conducted and the violin plots of HR and LR groups generated.

Tumor mutation analysis

LUAD-related SNV mutation data from the TCGA database was analyzed. The mutation status of HR and LR groups was plotted using maftoolsR. The top ten genes with mutation rates in the two groups were selected, and the waterfall plots of the mutation status of model genes were graphed.

Correlation analysis and drug screening

We utilized the CellMiner database (https://discover.nci.nih.gov/cellminer/home.do) to dig out drugs related to the model genes, following the result visualization with R package ggplot2. We employed the pRopheticR package to evaluate the IC50 of model genes for different drugs.

Results

Identification and recognition of SRGs_DEGs

By performing DEA on cancer and normal samples, we obtained 5576 DEGs related to cancer (Table S2). The intersection of SRGs and DEGs was taken, yielding 118 SRGs_DEGs (Fig. 1).

Fig. 1.

Fig. 1

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Venn Diagram of DEGs and SRGs

Prognostic model construction and validation

After the univariate Cox regression analysis, 26 genes related to survival were selected from the 118 SRGs_DEGs (P < 0.05) (Table S3). The following LASSO regression eliminated multi-collinearity from the 118 SRGs_DEGs (Fig. 2A, B). Finally, based on multivariate Cox regression analysis, 9 feature genes were yielded. A succinylation-related LUAD prognostic model was established (Fig. 2C).

Fig. 2.

Fig. 2

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Construction and Validation of Risk Scoring Model for SRGs in LUAD A: Coefficient distribution plot generated for the log(λ) sequence in the LASSO model. B: LASSO coefficient spectrum in LASSO Cox analysis. C: Forest plot of multivariate Cox regression results

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Using a formula, we scored LUAD patients. LUAD patients were clustered into the HR group and the LR group (cut-off value: median riskscore). As evident by the KM survival curve, the HR group had a lower survival rate than the LR group (Fig. 3A), indicating that our model relying on the riskscore was able to differentiate the risk levels of LUAD patients. We then validated the model using the GSE26939 validation set. According to KM survival curves, survival rates between the two risk groups were significantly different, with patients in the HR group having lower survival rates, which was congruous with the training set (Fig. 3B). To determine the model accuracy, we plotted the ROC curves, which showed that the 1-year, 3-year, and 5-year AUC values were 0.71, 0.67, and 0.69, respectively (Fig. 3C), reflecting the good predictive capability of the 9 SRGs. Moreover, we employed the ROC curves to validate the results, observing that the 1-year, 3-year, and 5-year AUC values were 0.85, 0.68, and 0.7, respectively (Fig. 3D). These results uncovered the high accuracy of the risk prognostic model we established.

Fig. 3.

Fig. 3

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Survival Curves and ROC Curves of HR and LR groups in TCGA and GEO Cohorts. A-B: Survival curves of HR and LR groups in TCGA cohort (A) and the GEO cohort (B). C-D: ROC curves of the TCGA cohort (C) and GEO cohort (D)

Furthermore, we performed risk scoring and survival curve analysis on the feature genes (Fig. 4A, B), finding that as riskscore elevated, the LUAD patients’ mortality rate also elevated. Moreover, between the two groups, great differences were observed in the expression of feature genes.

Fig. 4.

Fig. 4

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Analyses of Feature Gene Survival Status and Riskscores and the Gene Expression Heatmaps in TCGA and GEO Cohorts. A-B: Analyses of feature gene survival status and riskscores and the gene expression heatmaps in TCGA (A) and GEO (B) cohorts

Independent prognosis analysis

To figure out whether the riskscore can function as an independent indicator for LUAD patients’ prognosis, we designed analyses by incorporating the riskscore with patients’ clinical information (T, N, M, tumor stage, age, riskscore, gender). Firstly, through univariate Cox regression analysis, we observed that riskscore, T, N, and tumor stage were all statistically significant (p < 0.001) (Fig. 5A). Subsequently, we carried out a multivariate Cox analysis, unraveling that the riskscore was statistically significant (p < 0.001) (Fig. 5B). The riskscore was able to function as a predictive indicator for prognosis. Based on clinical information, the constructed LUAD prognostic model can predict the 1-year, 3-year, and 5-year overall survival (OS) rate of LUAD patients (Fig. 5C). The ROC curves of the nomogram demonstrated its AUC value at 0.71, further indicating that the nomogram possessed good predictive ability. Moreover, the ROC curves highlighted the specificity and sensitivity of the riskscore, which was more effective than age, gender, T, and M (AUC = 0.65) (Fig. 5D). Therefore, the riskscore can be utilized independently in predicting LUAD prognosis.

Fig. 5.

Fig. 5

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LUAD Prognostic Model Riskscore Combined with Independent Prognosis Analysis of Clinical Information. A-B: Univariate (A) and multivariate (B) analyses of clinical features and riskscore. C: Nomogram created based on clinical features and riskscores. D: ROC curve of clinical features, riskscore, and nomogram

Enrichment analyses of DEGs in HR and LR groups

The DEA yielded 1126 upregulated genes and 563 downregulated genes (Table S4). To understand the signaling pathways and functional differences that DEGs may be involved in, we performed enrichment analyses on these DEGs. The GO results uncovered that these genes were mainly linked to receptor ligand activity, hormone activity, passive transmembrane transporter activity, monoatomic ion channel activity, and cilium movement (Fig. 6A). Pathway analysis of KEGG revealed that these genes were primarily implicated in motor proteins, cAMP signaling pathway, steroid hormone biosynthesis, neuroactive ligand-receptor interaction, retinol metabolism, metabolism of xenobiotics by cytochrome P450, and complement and coagulation cascades (Fig. 6B).

Fig. 6.

Fig. 6

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Enrichment Analyses of DEGs in HR and LR Groups. A-B: GO (A) and KEGG (B) enrichment plots

Differential analysis of immune cells and functions in HR and LR groups

To elucidate the immune capabilities of the HR and LR groups, we scored the immune cell components and stromal cell components of the two groups of samples. The immune scores of the two risk groups indicated that the LR group had significantly higher stromal, immune, and ESTIMATE scores than the HR group (Fig. 7A). Moreover, by comparing the differences in immune cell components between the two risk groups, the LR group was found to have significantly elevated levels of B cell memory, Mast cells resting, Monocytes, and T cells CD4 memory resting (p < 0.05) (Fig. 7B). Finally, we detected immune cells and related functional pathways and uncovered that the aDCs, Neutrophils, DCs, B cells, Mast cells, TIL, iDCs, Treg, and T helper cells in the LR group were significantly higher than those in the HR group. Great differences were also observed in CCR, HLA, Type III FN response, and T cell co-stimulation between the two risk groups (p < 0.05) (Fig. 7C, D). Collectively, compared to the HR group, the immune cell proportion is significantly higher in the LR group.

Fig. 7.

Fig. 7

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Immune Analysis of HR and LR groups. A: Differential analysis of stromal score, immune score, and ESTIMATE score. B: Differential analysis of immune cell composition using CIBERSORT. C-D: Box plots for ssGSEA results of immune cell analysis (C) and immune function pathway analysis (D)

Immunotherapy response of HR and LR groups

Statistical comparisons were conducted for the TIDE scores and IPS to illuminate the effects of immunotherapy on two risk groups. The analysis results demonstrated that in comparison with those in the HR group, patients in the LR group exhibited lower TIDE scores and higher IPS (Fig. 8A, B). Therefore, the results above indicated the potential better therapeutic function of immunotherapy on the LR group compared to the HR group.

Fig. 8.

Fig. 8

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Immunotherapy Response in LR and HR Groups. A-B: Violin plots of IPS (A) and TIDE (B) in HR and LR groups

Tumor mutation analysis of the HR and LR groups

To further interpret the disparities in gene mutations between the LR group and HR group, we compared the mutation profiles of the two risk groups by utilizing the maftoolsR package. We finally revealed the same top 5 genes that had the highest mutation rates in both risk groups, namely TTN, TP53, MUC16, CSMD3, and RYR2. The missense mutations were most prevalent in the two groups, but their mutation rates differed. There were two different genes in the top ten genes with the highest mutation rates between the HR and LR groups, including FLG (28%) and SPTA1 (30%) in the HR group, and KRAS (27%) and ZNF536 (19%) in the LR group (Fig. 9A, B). The mutation rates of the 9 feature genes in the two risk groups exhibited differences, at 5.33% and 11.52% respectively (Fig. 9C, D).

Fig. 9.

Fig. 9

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Gene Mutation Analysis Between HR and LR Groups. A-B: Statistical overview of mutation types and their distribution in the LR group (A) and HR group (B). C-D: Waterfall plots of gene mutations in the LR (C) and HR groups (D)

Drug sensitivity analysis

To delve into the targeted drugs related to LUAD, a drug sensitivity analysis on the characteristic genes in LUAD was carried out, which demonstrated that ENPP3 was significantly positively linked with AZD-3965 (Cor = 0.634), ABL-001 (Cor = 0.615), Imatinib (Cor = 0.607), ARQ-087 (Cor = 0.546), Nilotinib (Cor = 0.530), ARRY-614 (Cor = 0.519), Tivozanib (Cor = 0.475), but significantly negatively correlated with GSK-2,126,458 (Cor = 0.519). SLC22A8 was significantly positively connected with Isotretinoin (Cor = 0.585), Imiquimod (Cor = 0.570), Fluphenazine (Cor = 0.539), Megestrolacetate (Cor = 0.530) (p < 0.001) (Fig. 10). Therefore, the above results suggested that ENPP3 and SLC22A8 may respond more sensitively to targeted drugs.

Fig. 10.

Fig. 10

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IC50 of Feature Genes on Drugs

Then, the IC50 of ENPP3 and SLC22A8 genes was analyzed. The ENPP3 gene low-expression group had dramatically lower corresponding IC50 values of Nilotinib and ARRY-614 than the high-expression group. The SLC22A8 low-expression group had a considerably lower corresponding IC50 value of Megestrol acetate than the high-expression group (Fig. 11), indicating that the ENPP3 and SLC22A8 low-expression groups may have higher drug sensitivity to Nilotinib, ARRY-614 and Megestrol acetate and lower drug resistance.

Fig. 11.

Fig. 11

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Box Plot of IC50 for Genes in Different Expression Groups

Discussion

The project demonstrated that although there has been significant progress in the clinical treatment of LUAD, its high mortality and high recurrence rates remain the most challenging issues in clinical practice. The important role of SRGs in various diseases, including cancer [12], has been verified. However, there have been no reports on the succinylation characteristics of the prognostic genes in LUAD. Therefore, this project constructed the prognostic model of SRGs in LUAD, which is beneficial for better assessing LUAD patients’ prognosis and survival status.

Herein, we first screened out SRGs in LUAD and then obtained 9 feature genes related to succinylation in LUAD through LASSO regression, univariate Cox, and multivariate Cox analyses, namely GAPDH, ENPP3, SLC22A8, F2, ALDOA, H2BC4, LDHA, H2BC12, and H2AX. GAPDH is implicated in various cellular functions, mainly affecting cancer or neurodegenerative diseases by translocating to different subcellular compartments for endocytosis, DNA replication and repair, cancer development, exocytosis, and cell death, thus impacting a variety of diseases [16, 17]. Chen et al. [18] found that GAPDH was upregulated in LUAD and negatively correlated with overall survival. F2, known as the coagulation factor, has been shown by Yang et al..[19, 20] to be linked with LUAD dismal prognosis, making it a promising candidate for a LUAD biomarker. Evidence from the human proteome atlas suggests that ALDOA is a gene associated with cancer, and many projects have indicated that ALDOA upregulation may reinforce cancer cell proliferation and be tightly linked to the occurrence, metastatic potential, progression, and unfavorable prognosis of various tumors [21–23]. In LUAD, the upregulation of ALDOA expression was significantly associated with tumor progression, poor survival, and immune infiltration [24]. Therefore, ALDOA may be a potential prognostic biomarker and therapeutic target for LUAD. H2BC4 and H2BC12 belong to the H2B family, and monoubiquitination of H2B may lead to chromosome instability (CIN) phenotypes related to defects in chromatin compaction during mitosis, which is an essential feature of tumorigenesis with adverse outcomes. Therefore, H2B plays a pivotal role in many pathological processes of tumors and is associated with poorer prognosis [25, 26]. A study has shown that the loss of H2B monoubiquitination may be associated with low differentiation and enhanced malignancy of LUAD [27]. This suggests that the loss of H2B monoubiquitination may lead to enhanced proliferation and metastatic capacity of LUAD cells, thereby promoting the progression of LUAD. LDHA plays a key role in glycolysis, responsible for the reduction of pyruvate to lactic acid, and its abnormal expression and activation have been found to be closely related to many cancers [28]. In LUAD, high expression of LDHA was associated with shorter overall survival and progression-free survival [29]. H2AX and other repair proteins act synergistically in tumor suppression and DNA damage response by enhancing efficient and high-fidelity repair of DNA double-strand breaks [30, 31]. Sakurai et al. [32] found that γH2AX, as a marker of DNA double-strand breaks, is associated with PD-L1 expression in smoking-related LUAD. However, current research on ENPP3 and SLC22A8 in LUAD is still relatively limited and requires further investigations. In conclusion, we speculated that these 9 characteristic genes may serve as potential effective biomarkers for LUAD.

Great differences in immune infiltration between LUAD patients in the two risk groups were observed, with Mast cells, T helper cells, TIL, B cells, etc. having high infiltration in the LR group. As a result, the LR group had higher levels of immune cell infiltration, which meant that the group was more likely to develop a “hot” tumor state that could accelerate immune system suppression of tumor progression [33]. Previous studies have shown that high density of tumor-infiltrating lymphocytes can inhibit tumor development and are associated with longer progression-free survival in patients with non-small cell lung cancer [34, 35]. Tumor-infiltrating B lymphocytes are involved in the immune response of LUAD, and high levels of B cell infiltration also predict a longer survival in LUAD patients [36]. Furthermore, tumor-associated mast cells may shape the TME through crosstalk with other tumor-infiltrating cells [37]. Patients with increased mast cell infiltration in LUAD had a better prognosis [38]. These are consistent with our results. Therefore, various cells play instrumental roles in the TME, and the high infiltration of these immune cells in the LR group may contribute to the longer survival in this risk group. This could provide a solution for the individualized treatment of LUAD patients.

To further probe into the potential mechanisms underlying survival differences, we compared the gene mutation frequencies of stromal cells in LUAD, finding that HR group exhibited an elevated mutation rate than the LR group, with TP53, TTN, MUC16, CSMD3, and RYR2 having the highest mutation rates. Missense mutations in TP53 are the main cause of most TP53 mutations in LUAD, and the accumulation of mutant p53 may occur due to increased protein stability [39]. TP53 mutations may affect the treatment response of LUAD patients to some extent, and TP53 mutations lead to inferior OS in both the general patient population and KRAS-wild-type LUAD patients [40]. TTN is one of the most common HLA II-class-presenting mutant peptides, and its mutation is associated with a significant extension of overall survival in LUAD patients. Additionally, TTN mutations are linked to high immunogenicity and inflammatory tumor immune microenvironment, suggesting that TTN mutations may serve as a potential predictive marker for LUAD patients receiving immune checkpoint inhibitor therapy [41, 42]. Furthermore, it is worth noting that the OS of TTN/TP53 double-mutant tumors is significantly better than that of TTN/TP53 single-mutant LUAD patients [43]. The mutation frequency of MUC16 ranked third, second only to TP53 and TTN. MUC16 was frequently mutated in LUAD and was tightly linked to higher tumor mutation burden and better clinical prognosis [44]. CSMD3 mutations may lead to dysfunction of CSMD3 tumor suppressor ability, resulting in enhanced metastasis, tumor cell proliferation, and adverse clinical manifestations in patients [45]. However, the current research on CSMD3 in LUAD is relatively limited. RYR2 functions as a tumor suppressor in LUAD by inducing mitochondrial dysfunction and promoting immune cell infiltration [46]. Moreover, RYR2 mutation prolonged the survival of non-small cell lung cancer patients by downregulating DKK1 and upregulating GS1-115G20.1 [47]. Therefore, gene mutations are one of the essential causes of LUAD, and mutations in related genes may lead to the deterioration of LUAD. In addition, based on the analysis of drug sensitivity of characteristic genes in LUAD using the CellMiner database, ENPP3 and SLC22A8 were found to be more sensitive to Nilotinib, ARRY-614, and Megestrol acetate in the low expression group, while resistance was low. The drugs may be used for targeted therapy, which, however, needs further research to confirm.

In conclusion, this project identified 9 SRGs in LUAD, constructed a risk assessment model, and analyzed the relationship between LUAD patients based on riskscores and immune landscape, gene mutations, and drug sensitivity. This work shed new insights for survival prediction and clinical application for LUAD patients. However, certain limitations persist in this project. The prognostic model establishment and validation are finished based on public databases, awaiting further validation with effective clinical samples. Additionally, the regulatory mechanisms and biological functions of SRGs in LUAD also need further investigation.

Supplementary Information

12014_2025_9570_MOESM1_ESM.xlsx (43.1KB, xlsx)

Supplementary Material 1. Table S1: Clinical Data Table of LUAD in TCGA Database 

12014_2025_9570_MOESM2_ESM.xlsx (359.5KB, xlsx)

Supplementary Material 2. Table S2: DEGs in LUAD

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Supplementary Material 3. Table S3: 26 Survival-related SRGs_DEGs Selected by Univariate Cox Regression Analysis

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Supplementary Material 4. Table S4: DEGs in the HR Group and LR Group

Author contributions

(I) Conception and design: Zongyu Li, Tiantian Zhang(II) Administrative support: Qingqing Liu, Yan Zhu(III) Collection and assembly of data: Erdan Lu, Yan Zhu(IV) Data analysis and interpretation: Tiantian Zhang, Qingqing Liu(V) Manuscript writing: Zongyu Li, Erdan Lu(VI). Final approval of manuscript: All authors.

Funding

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

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

12014_2025_9570_MOESM1_ESM.xlsx (43.1KB, xlsx)

Supplementary Material 1. Table S1: Clinical Data Table of LUAD in TCGA Database 

12014_2025_9570_MOESM2_ESM.xlsx (359.5KB, xlsx)

Supplementary Material 2. Table S2: DEGs in LUAD

12014_2025_9570_MOESM3_ESM.xlsx (11.1KB, xlsx)

Supplementary Material 3. Table S3: 26 Survival-related SRGs_DEGs Selected by Univariate Cox Regression Analysis

12014_2025_9570_MOESM4_ESM.xlsx (113.7KB, xlsx)

Supplementary Material 4. Table S4: DEGs in the HR Group and LR Group

Data Availability Statement

No datasets were generated or analysed during the current study.

Articles from Clinical Proteomics are provided here courtesy of BMC

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