Abstract
Background
Lung adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to its aggressive nature and metastatic potential. Mitochondrial permeability transition (MPT)-driven necrosis and long non-coding RNAs (lncRNAs) play crucial roles in cancer progression. This study aimed to build a MPT-driven necrosis-linked lncRNA signature for forecasting prognosis and treatment of LUAD.
Methods
The publicly available data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database for investigating the key MPT-driven necrosis-related lncRNAs in LUAD. Then, an MPT-driven necrosis-related lncRNA signature was constructed and its predictive performance for forecasting LUAD prognosis was examined. Moreover, nomogram construction, pathway, immune infiltration, immunotherapy response, and drug sensitivity analyses were performed to explore the clinical value and potential mechanisms of the signature in LUAD.
Results
Six MPT-driven necrosis-related lncRNAs that were linked with LUAD prognosis were identified, including LINC01150, SIGLEC17P, SNHG3, DRAIC, AC099850.4, and AC245041.1. An MPT-driven necrosis-linked lncRNA signature was then constructed by these six lncRNAs, which showed high performance in forecasting prognosis. Moreover, the signature was linked with the infiltration of multiple immune cells like macrophages, activated CD4 T cells, and neutrophils. Furthermore, low-risk patients displayed greater sensitivity to immunotherapy.
Conclusions
The six-MPT-driven necrosis-related lncRNA signature may be a powerful biomarker for forecasting prognosis and immunotherapy response of LUAD.
Keywords: Lung adenocarcinoma (LUAD), long non-coding RNAs (lncRNAs), mitochondrial permeability transition (MPT), necrosis, prognosis
Highlight box.
Key findings
• Six mitochondrial permeability transition (MPT)-driven necrosis-related long non-coding RNAs (lncRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) were identified: LINC01150, SIGLEC17P, SNHG3, DRAIC, AC099850.4, and AC245041.1. These lncRNAs were used to construct an MPT-driven necrosis-associated lncRNA signature, which demonstrated high predictive accuracy for LUAD prognosis.
• The signature was associated with immune cell infiltration, particularly macrophages, activated CD4 T cells, and neutrophils, and low-risk patients demonstrated enhanced sensitivity to immunotherapy.
What is known and what is new?
• Previous studies have linked MPT and necrosis to cancer progression, but their related lncRNAs in LUAD are largely unknown.
• This study is the first to develop a robust MPT-driven necrosis-related lncRNA signature for LUAD prognosis, highlighting its potential as a biomarker for clinical prediction and therapeutic response.
What is the implication, and what should change now?
• The identified lncRNA signature may serve as a promising tool for forecasting LUAD prognosis and guiding immunotherapy strategies.
• Future clinical studies are needed to validate this signature and explore its potential in personalized treatment plans for LUAD patients.
Introduction
Lung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer, known for its aggressive nature and rapid progression (1). Approximately 80% of LUAD comprise a mixture of multiple histological types (2,3). Despite therapeutic advancements, the 5-year survival rate for LUAD remains under 50%, plummeting to a mere 7% once metastasis occurs (3). Therefore, in order to improve patient outcomes, there is a pressing necessity to identify more effective predictive markers and therapeutic targets for LUAD.
Mitochondrial permeability transition (MPT) is a crucial aspect of cellular physiology. It is mediated by the mitochondrial permeability transition pore (mPTP), and alterations in MPT, resulting in the influx of excessive molecules into the cytoplasm, can evoke diverse cellular responses, including the physiological regulation of mitophagy and the activation of apoptosis or necrosis (4). MPT-driven necrosis has been considered as a programmed necrosis, featuring specific signaling cascades that present potential therapeutic targets for numerous diseases, including cancer (5).
Long non-coding RNAs (lncRNAs) are vital regulators in a broad spectrum of human diseases, including lung cancer (6). Accumulating evidence has revealed that lncRNAs are critical regulators in tumorigenesis and cancer progression (7). In addition, MPT-driven necrosis-linked lncRNAs have been shown to affect the signaling pathways of death and survival, and are engaged in important biological processes including cancer, apoptosis, cell cycle progression, and cell senescence (8). Moreover, MPT-driven necrosis-linked lncRNAs play a key role in regulating gene expression and cancer development (9,10). Notably, MPT-driven necrosis-linked lncRNAs are related to immunotherapy/chemotherapy outcomes in clear cell renal carcinoma patients (11). However, the link between MPT-driven necrosis-linked lncRNAs and the prognosis of LUAD has not been thoroughly explored.
Herein, we investigated the key MPT-driven necrosis-related lncRNAs in LUAD and constructed a prognostic signature. Moreover, we performed nomogram construction, pathway, immune infiltration, immunotherapy response, and drug sensitivity analyses to clarify the clinical value and potential mechanisms of the prognostic signature in LUAD. Our work provides reference for further understanding of the pathogenesis, treatment and prognosis of LUAD. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2051/rc).
Methods
Data collection and preparation
The RNA expression [fragments per kilobase million (FPKM)] data and clinical data of The Cancer Genome Atlas (TCGA)-LUAD dataset were acquired from the TCGA database. The data were preprocessed as follows: (I) samples without survival time were removed, and only samples with prognostic information were retained; (II) genes with missing values or lack of expression exceeding 50% of the total sequencing count were removed; (III) samples with unexpressed genes over 50% are excluded; and (IV) all expression values were log2(X+1) transformed. Finally, 559 TCGA samples were retained, including 500 tumor and 59 normal samples. In addition, the gene expression dataset GSE31210 with clinical information was obtained from National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. After excluding samples without survival time, 226 LUAD samples were retained. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Identification of MPT-driven necrosis-related differentially expressed lncRNAs (MPTDNRDELs)
The MPT-driven necrosis-related genes (MPTDNRGs) were extracted from the M17902, M3873, and M16257 gene sets in the gene set enrichment analysis (GSEA) database (12). The differentially expressed lncRNAs (DELs) between LUAD and normal samples was identified using the R limma package (Version 3.34.7) (13). The cutoff value for DEL screening was adjusted P value <0.05 and |log2 fold change (FC)| ≥0.5. The Pearson correlation coefficient between DELs and MPTDNRGs were calculated by cor function. The significant MPTDNRDELs were obtained with P<0.001 and |R| ≥0.4.
Construction of a prognostic model
Based on MPTDNRDELs expression, univariate Cox regression analysis was conducted using the R survival package (version2.41-1) (14). The MPTDNRDELs that had prognostic value were selected with P<0.05. Subsequently, the key MPTDNRDELs were screened using the survival regression analysis in the least absolute shrinkage and selection operator (LASSO) Cox algorithm (15) in the R glmnet package (version 1.2). The model with the fewest number of variables with 1 standard error was chosen. Ten-fold cross-validation was used to adjust the penalty parameter. Then, a Risk score model was constructed using stepwise Cox regression analysis, as follows: Risk score = h0 (t) * exp (β1X1 + β2X2 + ... +βnXn), where exp indicates lncRNA expression, β refers to the regression coefficient of each lncRNA, and h0 (t) represents the base risk rate. Based on the TCGA training set and GEO verification set, the risk score of each patient was calculated. According the median risk score, patients in the two datasets were classified into high- or low-risk groups. The Kaplan-Meier (KM) curves were plotted to assess the prognostic value of risk score.
The prognostic independence of RiskGroup and nomogram construction
Univariate and multivariate Cox regression analyses were conducted to identify the independent prognostic factors in LUAD were selected with P<0.05. The nomogram was then constructed by these independent prognostic factors using the R rms package (version 5.1-2) (16). The 1-, 3- and 5-year calibration curves were plotted to prove the predictive accuracy of the nomogram.
Pathway analysis
Based on genome-wide expression data, the significantly differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between two risk groups were enriched using GSEA (17). The cutoff value was P<0.05 and |normalized enrichment score| >1.
Immune microenvironment analysis for two risk groups
Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) (18) and single-sample gene set enrichment analysis (ssGSEA) (19) analyzed the proportion of immune cell types in LUAD samples. The ESTIMATE, immune, and stromal scores were determined using the R ESTIMATE package (20). Subsequently, the link between risk score, model lncRNA and immune cells were calculated utilizing the R ggcor package. Moreover, the expression data of immune checkpoint genes and human leukocyte antigen (HLA) family genes were extracted from the TCGA LUAD dataset.
Prediction of immunotherapy response
Tumor immune dysfunction and exclusion (TIDE)score was used to predict the response of each LUAD sample to immunotherapy. Cytolytic activity (CYT) scores were expressed as the average of granzyme A (GZMA) and recombinant perforin 1 (PRF1) expression values. The tertiary lymphoid structure (TLS) score was calculated using the R GSVA algorithm.
Analysis of genetic mutations of two risk groups
On basis of the genetic mutation information, the number of mutations of each gene in the LUAD sample was ranked from high to low. The mutation frequency of the top 20 genes was calculated using the R maftools package (version 2.8.0) (21). Moreover, the tumor mutation burden (TMB) of all LUAD samples was calculated.
Drug sensitivity analysis
Based on the Genomic Data Science Cancer (GDSC) database, the sensitivity [half maximal inhibitory concentration (IC50) value] of 138 kinds of chemotherapy drugs was estimated through the R pRRophetic package (22).
Statistical analysis
The differences in the expression of immune checkpoint genes and HLA family genes, immunotherapy biomarkers, TMB value, and IC50 value of chemotherapy drugs between different risk groups were analyzed using the Wilcoxon test. The value of P<0.05 was considered statistically significant.
Results
Identification of MPTDNRDELs
First, the difference analysis was conducted on the extracted 39 MPTDNRGs. The results showed that most of the MPTDNRGs exhibited significant differences between the LUAD samples and normal samples (Figure 1A). Through differential expression analysis, 594 DELs were screened between tumor samples and control samples (Figure 1B). By further correlation analysis, 127 MPTDNRDELs were obtained.
Figure 1.
Identification of MPTDNRDELs. (A) Difference analysis of 39 MPTDNRGs (tumor: n=500; normal: n=59). (B) Volcano map for DELs, blue represents down-regulated lncRNAs, red represents up-regulated lncRNAs. **, P<0.01; ***, P<0.001; “-” indicated no significance. DELs, differentially expressed lncRNAs; lncRNAs, long non-coding RNAs; MPT, mitochondrial permeability transition; MPTDNRDELs, MPT-driven necrosis-related differentially expressed lncRNAs; MPTDNRGs, MPT-driven necrosis-related genes.
Construction and validation of a prognostic model
Based on the expression levels of MPTDNRDELs, 58 MPTDNRDELs were significantly linked to prognosis (Figure 2A). Subsequently, LASSO analysis revealed 17 key MPTDNRDELs (Figure 2B,2C). Then, the optimal gene combination was obtained using the stepwise Cox regression algorithm. As results six MPTDNRDELs were identified, including LINC01150, SIGLEC17P, SNHG3, DRAIC, AC099850.4, and AC245041.1 (Figure 2D). The Riskscore model was then constructed by these MPTDNRDELs. The risk score and survival time distribution of patients in two datasets were shown in Figure 2E. The KM curve demonstrated better prognosis of patients in the low-risk group (Figure 2F). Receiver operating characteristic (ROC) curves showed higher area under the curve (AUC) value of this prognosis model in forecasting 1-, 3-, and 5-year survival (all AUC value >0.65) (Figure 2G). Consistent results were obtained based on GEO validation set (Figure 2H-2J).
Figure 2.
Construction and validation of a prognostic model. (A) Univariate Cox regression analysis of 58 MPTDNRDELs significantly linked to prognosis of patients with LUAD (n=500). (B) LASSO coefficient distributions of 17 key MPTDNRDELs; (C) LASSO coefficient profiles of MPTDNRDELs. (D) Multivariate Cox regression forest maps of 6 signature lncRNAs. (E) The distribution of the risk scores (up) and survival time state (down) of the TCGA validation set (high-risk group, n=250; low-risk group, n=250). (F) KM curve based on TCGA dataset (high-risk group, n=250; low-risk group, n=250). (G) The 1-, 3-, and 5-year ROC curves based on TCGA dataset. (H) The distribution of the risk scores (up) and survival time state (down) of the GEO validation set (high-risk group, n=113; low-risk group, n=113). (I) KM curve based on GEO validation set (high-risk group, n=113; low-risk group, n=113). (J) The 1-, 3-, and 5-year ROC curves based on GEO validation set. AUC, area under the curve; CI, confidence interval; GEO, Gene Expression Omnibus; KM, Kaplan-Meier; LASSO, least absolute shrinkage and selection operator; lncRNAs, long non-coding RNAs; LUAD, lung adenocarcinoma; MPT, mitochondrial permeability transition; MPTDNRDELs, MPT-driven necrosis-related differentially expressed lncRNAs; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas.
The prognostic independence of RiskGroup and nomogram construction
Univariate and multivariate Cox regression analyses showed that pathologic_T, pathologic_N, and RiskGroup were independent factors affecting disease prognosis (P<0.001, Figure 3). A nomogram was constructed by incorporating pathologic_T, pathologic_N, and RiskGroup (Figure 4A) and its high predictive accuracy for survival was proved by the calibration curves (Figure 4B), KM curve (Figure 4C) and ROC curves (1-, 3-, and 5-year AUC =0.691, 0.723, and 0.765, respectively, Figure 4D).
Figure 3.
The prognostic independence of RiskGroup. (A) Univariate Cox regression analysis result. (B) Multivariate Cox regression analysis result. CI, confidence interval; M, metastasis; N, node; T, tumor.
Figure 4.
Nomogram construction. (A) A nomogram was constructed by incorporating pathologic_T, pathologic_N, and RiskGroup. (B) The calibration curves. (C) KM curves (high-risk group, n=241; low-risk group, n=241). (D) 1-, 3- and 5-year ROC curves. AUC, area under the curve; KM, Kaplan-Meier; N, node; ROC, receiver operating characteristic; T, tumor.
Differential signaling pathways between two risk groups
To explore the potential mechanism, GSEA demonstrated 32 differential KEGG pathways between two risk groups (Figure 5). The most prominent up-regulated pathway was PROTEASOME, and the most prominent down-regulated pathway was VASCULAR_SMOOTH_MUSCLE_CONTRACTION.
Figure 5.
GSEA demonstrated differential signaling pathways between two risk groups. Left: down-regulated pathways; right: up-regulated pathways. High-risk group, n=250; low-risk group, n=250. GSEA, gene set enrichment analysis; NES, normalized enrichment score; NP, nominal P value.
Immune microenvironment analysis of two risk groups
For immune microenvironment, the stromal, immune, and ESTIMATE scores were higher in the low-risk group, whereas the tumor purity was higher in the high-risk group (Figure 6A). We then calculated the proportion of 22 immune cell types utilizing the CIBERSORT algorithm. Among them, 10 immune cells such as macrophages, activated CD4 T cells, and neutrophils were significantly different between the two risk groups (Figure 6B). Utilizing the ssGSEA algorithm, 17 differential immune cells such as macrophages, activated CD4 T cells, and neutrophils were also obtained (Figure 6C). Additionally, seven differentially expressed immune checkpoint genes were obtained, including BTLA, CD47, CTLA-4, TIM3, CD278, MYD1, and TIGIT (Figure 7A). Furthermore, most HLA family genes exhibited differential expression between the two risk groups (Figure 7B).
Figure 6.
Immune microenvironment analysis of two risk groups. (A) The stromal, immune, ESTIMATE scores and tumor purity of two risk groups. (B) CIBERSORT analysis results. (C) ssGSEA analysis results. High-risk group, n=250; low-risk group, n=250. *, P<0.05; **, P<0.01; ***, P<0.001; “-” indicated no significance. CIBERSORT, cell-type identification by estimating relative subsets of RNA transcripts; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; ssGSEA, single-sample gene set enrichment analysis.
Figure 7.
Comparison of immune checkpoint genes and HLA family genes between two risk groups. (A) Immune checkpoint genes of different risk groups. (B) HLA family genes of different risk groups. High-risk group, n=250; low-risk group, n=250. *, P<0.05; **, P<0.01; ***, P<0.001; “-” indicated no significance. HLA, human leukocyte antigen.
Immunotherapy response of two risk groups
The correlation analysis showed that the model genes like LINC01150, SIGLEC17P, and AC245041.1 were positively correlated with most of immune cells, whereas risk score, SNHG3, and DRAIC were negatively related to most of immune cells (Figure 8A). Moreover, the immunotherapy response of each patient was assessed. As results, the TIDE scores were obviously lower in the low-risk group (Figure 8B), whereas the TLS scores were dramatically higher in the low-risk group (Figure 8C). These data indicated that low-risk patients were more sensitive to immunotherapy.
Figure 8.
Immunotherapy response of two risk groups. (A) The correlation analysis of model genes, risk score, and immune cells. (B) TIDE scores of different risk groups. (C) CD8A/PD-L1 expression ratio, CYT score and TLS scores of different risk groups. High-risk group, n=250; low-risk group, n=250. CYT, cytolytic activity; PD-L1, programmed cell death-ligand 1; TIDE, tumor immune dysfunction and exclusion; TLS, tertiary lymphoid structure.
Gene mutation analysis
The top 20 genes with higher mutation frequencies in LUAD samples are shown in Figure 9A. Moreover, the TMB value was higher in the high-risk group relative to the low-risk group (Figure 9B). Furthermore, the TMB was positive related to risk score (Figure 9C).
Figure 9.
Gene mutation analysis. (A) Mutations of the top 20 genes in different risk groups. (B) TMB between two risk groups. (C) Differences in the distribution of TMB among different risk groups. High-risk group, n=250; low-risk group, n=250. TMB, tumor mutation burden.
Sensitivity to chemotherapy drugs
By analysis of IC50 values of 138 chemotherapy drugs, the top three chemotherapy drugs that exhibited the most significant disparity between two risk groups were A.443654, BI.2536, and GW843682X (Figure 10).
Figure 10.
Comparison of IC50 levels of three chemotherapy drugs in different risk groups. High-risk group, n=250; low-risk group, n=250. ***, P<0.001. IC50, half maximal inhibitory concentration.
Discussion
LUAD is a common malignancy with gradually increasing incidence (23). There is a pressing need to explore key mechanisms underlying LUAD. This study identified six MPTDNRDELs related to LUAD prognosis and built a prognostic signature that had high performance for forecasting LUAD prognosis. Moreover, this signature was linked with the infiltration of multiple immune cells like macrophages, activated CD4 T cells, and neutrophils. Furthermore, the low-risk patients displayed greater sensitivity to immunotherapy.
LncRNAs are implicated in various cell functions and MPT-driven necrosis-linked lncRNAs may affect cancer cell growth and metastasis by regulating cell death pathways (24). Cancer occurrence and prognosis are intricately linked to the MPT-driven necrosis-linked lncRNA, and exploring MPT-driven necrosis lncRNAs presents promising avenues for uncovering innovative prognostic methodologies and understanding the immune microenvironment in cancer patients (11). In this study, six MPTDNRDELs were obtained, including LINC01150, SIGLEC17P, SNHG3, DRAIC, AC099850.4, and AC245041.1. Accumulating studies have shown that LINC01150 is crucial in aortic valve disease and stomach cancer (25,26) and has a certain prognostic value for non-small cell lung cancer (27). Overexpression of SIGLEC17P has shown to suppress the malignant progression of LUAD cells (28). SNHG3 is a potential oncogene in cancers, including lung cancer (29). AC099850.4 is a prognostic marker for liver cancer (30). AC245041.1 has also been revealed to have prognostic value in stomach adenocarcinoma (31). Given the role of these lncRNAs in cancer, we believe that these lncRNAs may contribute to LUAD development via involving in MPT-driven necrosis. Moreover, we developed a prognostic model by the six MPTDNRDELs, which demonstrated high performance for predicting survival in both training and validation datasets. Therefore, this prognostic model may be used for guiding clinical prognosis of patients with LUAD. Furthermore, we identified three independent clinical prognostic factors, namely pathologic_T, pathologic_N, and RiskGroup, and then constructed a nomogram model. Some studies have shown the independent prognostic impact of pathologic_T, which has a significant impact on treatment selection and long-term survival of cancer patients (32,33). Pathologic evaluation is a crucial factor in determining treatment strategies and predicting disease outcomes. Moreover, clinical stage and RiskGroup are usually used to guide clinical decisions and treatment plans (3). In this study, the nomogram constructed by pathologic_T, pathologic_N, and RiskGroup had a high predictive accuracy on LUAD prognosis, which could serve as a valuable tool for clinicians in predicting patient outcomes and making informed treatment decisions, ultimately improving patient care and management strategies for LUAD.
LncRNA plays a crucial role in immune regulation, and the immune microenvironment influences the prognosis of LUAD patients (27). Herein, the infiltration of immune cells like macrophages, activated CD4 T cells, and neutrophils exhibited significantly differences between two risk groups. Previous studies have shown that macrophages_M0 was involved in clearing infections and repairing tissue, and may potentially affect the overall function of the immune system (34,35). CD4 T cells are essential in the immune response, activating other immune cells and promoting antibody production (36). Neutrophils are one of the main inflammatory cells in the immune system, which can rapidly accumulate and participate in killing microorganisms and controlling inflammation (36). In addition, immune checkpoints participate in immunoregulation through cell surface or intracellular receptors, allowing the immune system to better adapt to the body’s environment and prevent excessive immune reactions. In LUAD, immune checkpoints participate in the immune evasion process of tumor cells, affecting the killing effect of immune cells on tumors (37-40). Furthermore, high TMB is associated with poor prognosis in several malignant diseases (41,42). It has also been reported that high TMB is related to the efficacy of immunotherapy (43,44). In this study, we found that the TMB value was higher in the high-risk group compared to the low-risk group, suggesting that TMB could serve as a potential predictor for prognosis and immunotherapy response. Moreover, this study revealed that the TIDE scores were significantly lower in the low-risk group, whereas the TLS scores were dramatically higher in the low-risk group. These findings suggest that low-risk patients may display greater sensitive to immunotherapy. Notably, tumor-infiltrating CD8+ T lymphocytes play a key role in killing tumor cells and are associated with the effectiveness of immune checkpoint inhibitors (45). However, we did observe differences in CD8+ T lymphocytes between the low-risk and high-risk groups. Given the different immunotherapy responses observed between two risk groups, we speculate that tumor-infiltrating CD8+ T lymphocytes may not be the sole factor influencing sensitivity to immunotherapy. Collectively, the infiltration of immune cells such as macrophages, activated CD4 T cells, and neutrophils, immune checkpoint activation, and TMB value may affect the immune response of the immune system in LUAD, which may be potential mechanisms by which MPT-driven necrosis-linked lncRNAs affect the LUAD prognosis and immunotherapy response.
To explore the potential mechanisms, GSEA demonstrated that the most significant upregulated pathway between two risk groups was PROTEASOME. Protein kinases play a crucial role in cell signaling, regulating various processes such as cell growth, differentiation, and death, and PROTEASOME is revealed a popular tumor signaling pathway and treatment (46,47). The most significant down-regulated pathway was VASCULAR_SMOOTH_MUSCLE_CONTRACTION, which played a crucial role in regulating vascular smooth muscle contraction in the body. Decreased activity of this pathway may affect the contractile function of vascular smooth muscle, which could have implications for certain diseases (48). These findings have important implications for understanding the pathogenesis of LUAD. Besides, we found significant differences in the sensitivity of chemotherapy agents, like A.443654, B.2536, and GW843682X for LUAD between two risk groups. These results have a reference value in the clinical use of chemotherapy drugs.
There are some limitations to this study. First, the expression levels of six MPTDNRDELs were not investigated in clinical samples. Second, the predictive value of the MPT-driven necrosis-linked lncRNA signature was not verified in the clinical cohorts. Third, the link between these MPTDNRDELs and pathway and immune regulatory mechanisms were not experimentally explored. Collectively, more studies are needed to refine our findings.
Conclusions
In conclusion, six MPTDNRDELs including LINC01150, SIGLEC17P, SNHG3, DRAIC, AC099850.4, and AC245041.1 may play important roles in LUAD development via involving in MPT-driven necrosis. The MPT-driven necrosis-linked lncRNA signature may be a powerful biomarker for forecasting prognosis and immunotherapy response of LUAD. This study provides a new reference for LUAD prognosis and treatment.
Supplementary
The article’s supplementary files as
Acknowledgments
None.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
Footnotes
Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2051/rc
Funding: None.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2051/coif). The authors have no conflicts of interest to declare.
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