Tumor necrosis factor-related lncRNAs predict prognosis and immunotherapy response for patients with lung adenocarcinoma

Introduction

Lung cancer is the most common cause of cancer death with an increasing annual incidence rate (1). It mainly includes non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), with NSCLC accounting for about 80–85% all cases of lung cancer. Thereinto, more than 50% of NSCLC patients are classified as lung adenocarcinoma (LUAD) (2,3). Although drugs targeting driver genes of LUAD have improved clinical treatment, LUAD patients inevitably develop drug resistance after treatment with these targeted drugs (4). With the development of the immune check-point inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1), programmed death ligand-1 (PD-L1), and cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4), immunotherapy has revolutionized LUAD treatment in the recent decade and provided new approaches to improve the survival of LUAD patients (5,6). However, only a small number of LUAD patients respond to immunotherapy (7). Therefore, there is an urgent need to discover novel biomarkers to predict the treatment response and prognosis for LUAD patients.

The tumor necrosis factor (TNF) family consists of a 19 TNF ligands superfamily (TNFSF) and a 29 TNF receptor superfamily (TNFRSF). The TNF family plays an important role in tumorigenesis and tumor progression (8). Studies have reported that a TNF family-based signature can efficiently predict the prognosis and response to immunotherapy and chemotherapy in several cancers, including LUAD, SCLC, and colorectal cancer (9-11). Moreover, the TNFSF/TNFRSF system has been shown to mediate inflammation-related pathways that are involved in many steps of the immune response (12), highlighting that anti-TNF therapy can effectively relieve inflammation-related autoimmune diseases, such as rheumatoid arthritis and inflammatory bowel disease (8), and cancer pain (13). Therefore, the therapeutic approaches to intervene TNF-related signaling pathways may have a lot of potential.

Long non-coding RNAs (lncRNAs) are RNAs with little or no protein-coding function consisting of more than 200 nucleotides (14). They are involved in drug resistance, carcinogenesis, and inflammatory and immune responses by interacting with RNA-binding proteins and transcription factors (15-17). Studies identified the dual role of lncRNAs by interacting various signal pathways in LUAD. Some lncRNAs promoted the progression of LUAD, such as lncRNA PCAT6 by sponging miR-545-3p and lncRNA FOXD2-AS1 by regulating cell cycle (18,19). However, some lncRNAs inhibited the progression of LUAD, such as lncRNA SNHG5 by regulating epithelial-mesenchymal transition and lncRNA HITT by attenuating Rab5-mediated endocytosis (20,21). Various lncRNAs have been demonstrated to play their roles by mediating TNF family members in multiple cancers, including lung cancer (22-26). Therefore, TNF-related lncRNAs may be a promising research direction in understanding the treatment and prognosis of cancers. However, the expression patterns and clinical significance of TNF-related lncRNAs in LUAD are still not fully clear.

In this study, we systematically explored the expression patterns and clinical significance of TNF-related lncRNAs using RNA-seq data of LUAD patients from The Cancer Genome Atlas (TCGA). We constructed a TNF-related lncRNA prognostic signature by the univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO)-Cox proportional hazards regression method in the training dataset. Then, the signature was validated in the testing dataset and the entire patient dataset. Finally, we further explored the signature-related biological pathways and the correlation of the signature and immunotherapy response. We present the following article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-184/rc).

Methods

Data collection

We collected RNA-seq data of 539 LUAD patients and 59 healthy controls with the corresponding clinical data, including age, gender, and tumor-node-metastasis (TNM) stage, and overall survival (OS) from the TCGA database (http://www.ncbi.gov/repository). Patients who had incomplete clinical data and OS <30 days were excluded, thus 493 LUAD patients were eventually included in the study. The enrolled 493 LUAD patients were randomly assigned into a training dataset and a testing dataset in a 1:1 ratio. Thus, the entire patient dataset, training dataset, and testing dataset consisted of data from 493 patients, 247 patients, and 246 patients, respectively. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Establishment and validation of TNF family-related lncRNA prognostic signature

In training dataset, Pearson correlation analysis was used to identify the correlation of lncRNAs and TNF family genes. The univariate Cox proportional hazards regression analysis was used to identify TNF-related lncRNAs that were related with survival of LUAD patients. With the selected genes, the LASSO-Cox proportional hazards regression analysis was used to construct a TNF-related lncRNA prognostic signature. The risk scores were calculated based on the LASSO regression coefficients. According to the median risk score, these enrolled patients were divided into high- and low-risk subgroups. Then, the Kaplan-Meier (KM) survival analysis was used to evaluate the prognostic value of the signature. To identify the prognostic robustness of the signature from the training dataset, we further assessed its performance in the testing dataset and the entire patient dataset. Finally, univariate and multivariate Cox regression analyses were used to evaluate whether the signature can independently predict prognosis by analyzing the correlation of the signature and traditional clinical factors, including age, gender, and stage.

Functional enrichment analysis

To identify the signature related biological pathways, we used Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to explore the potential molecular mechanisms based on differentially expressed genes (DEGs) between the high- and low-risk groups in the clusterProfiler package in R (R Foundation for Statistical Computing, Vienna, Austria).

Tumor immune dysfunction and exclusion analysis

The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immune escape and clinical responses to immune checkpoint blockade. Patients with low TIDE prediction scores were deemed to have a good clinical effect to immunotherapy and no immune escape, whereas patients with high TIDE prediction scores tend to be non-responders and have immune escape.

Statistical analysis

R language (version 4.0.0) was the main software tool used for statistical analysis. Univariate, LASSO-Cox, and multivariate Cox regression analyses were used to explore the prognostic value of the signature and the correlation of the signature and traditional clinical factors. KM survival analysis was used to evaluate the survival status.

Time-dependent area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the predictive value of the signature to 1-, 2-, and 3-year OS. Student’s t-test was used to analyze significance among variables. P value <0.05 was considered statistically significant.

Results

Identification of the prognostic value of TNF family-related lncRNAs in LUAD patients

The flowchart of this study is shown in Figure 1. In this study, we enrolled 47 TNF family genes and 16,876 lncRNAs by retrieving the RNA-seq data of LUAD patients from TCGA (Figure 1). Pearson correlation analysis further demonstrated that the expression of 673 lncRNAs was closely related with TNF family genes (|Pearson R|>0.4, P<0.001). Then, the univariate Cox proportional hazards regression analysis was used to evaluate the prognostic value of the identified TNF-related lncRNAs in LUAD. A total of 63 TNF-related lncRNAs significantly associated with OS were identified (P<0.05). After LASSO analysis, 8 TNF-related lncRNAs obviously related with OS were acquired to construct the risk signature (Figure 2). Among these 8 genes, 4 genes (LINC01137, LINC02310, AC017100.1, and AC108676.1) were confirmed as risky factors with hazard ratios (HRs) >1, whereas 4 genes (AC118757.1, AL357873.1, AC104971.3, and HLA-DQB1-AS1) were confirmed as protective factors with HRs <1 (Figure 2).

Figure 1 The flowchart of this study. LUAD, lung adenocarcinoma; TCGA, The Cancer Genome Atlas; TNF, tumor necrosis factor; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; IncRNAs, long non-coding RNAs.

Figure 2 The prognostic value of TNF family-related lncRNAs in LUAD patients. TNF, tumor necrosis factor; lncRNA, long non-coding RNA; LUAD, lung adenocarcinoma.

Additionally, KM survival analysis showed that patients with high expression status of 4 genes (LINC01137, LINC02310, AC017100.1, and AC108676.1) had shorter survival time than those with low expression status, whereas patients with high expression of 4 genes (AC118757.1, AL357873.1, AC104971.3, and HLA-DQB1-AS1) had longer survival than those with low expression (P<0.05, Figure 3).

Figure 3 Kaplan-Meier survival curves were created to estimate survival value for LUAD patients with high or low expression of the identified eight TNF family-related lncRNAs. (A) AC118757.1; (B) LINC01137; (C) LINC02310; (D) AL357873.1; (E) AC017100.1; (F) AC104971.3; (G) AC108676.1; (H) HLA-DQB1-AS1.TNF, tumor necrosis factor; lncRNA, long non-coding RNA; LUAD, lung adenocarcinoma.

Establishment of a TNF family-related lncRNA prognostic signature using the LUAD patients in the training dataset

After univariate Cox proportional hazards regression analysis, 8 TNF-related lncRNAs were selected to construct the signature by LASSO-Cox proportional hazards regression analysis in the training dataset (Figure 4). The risk formula was established with the enrolled 8 TNF-related lncRNAs: risk score = (−1.18× AC118757.1) + (0.28× LINC01137) + (0.55× LINC02310) + (−1.21× AL357873.1) + (0.38× AC017100.1) + (−0.45× AC104971.3) + (0.31× AC108676.1) + (−0.15× HLA-DQB1-AS1). The gene expression profiles, the corresponding risk scores, and survival status of the identified 8 TNF-related lncRNAs are displayed in Figure 5A. According to the median risk score, these enrolled patients were divided into high- and low-risk subgroups. Moreover, the KM survival analysis indicated that patients in the high-risk group showed significantly less favorable OS than that of low-risk group (Figure 5B). Subsequently, time-dependent AUC values were calculated to evaluate the ability of the TNF-related lncRNA prognostic signature to predict 1-, 2-, and 3-year OS in the training dataset. These values were 0.740, 0.738, and 0.758, respectively (Figure 5C).

Figure 4 LASSO-Cox regression with a 10-fold cross-validation for the prognostic value of the eight TNF family-related lncRNAs. (A) LASSO coefficient profiles of the prognostic lncRNAs; (B) plots of the cross-validation error rates. LASSO, least absolute shrinkage and selection operator; lncRNA, long non-coding RNA; TNF, tumor necrosis factor.

Figure 5 Establishment of the TNF family-related lncRNA prognostic signature in the training dataset. (A) Heatmap of eight gene expression, risk score and survival status. (B) Kaplan-Meier curves of OS in 50% of total LUAD patients are stratified by TNF family-related lncRNA prognostic signature in high and low risk groups based on the risk score. (C) Time-dependent AUC of the risk scores for 1-, 2-, and 3-year OS prediction. TNF, tumor necrosis factor; lncRNA, long non-coding RNA; LUAD, lung adenocarcinoma; OS, overall survival; AUC, area under the curve.

Validation of the TNF family-related lncRNA prognostic signature

To identify the prognostic robustness of the TNF-related lncRNA prognostic signature from the training dataset, we further assessed its performance in the testing dataset and the entire patient dataset. The gene expression profiles, the corresponding risk scores, and survival status in the testing dataset and entire patient dataset are displayed in Figure 6A. As expected, the KM survival analysis showed that patients in the high-risk group showed significantly less favorable OS than that of low-risk group in the testing dataset (P<0.001) and entire patient dataset (P<0.001) (Figure 6B). Time-dependent AUC values were also used to assess the predictive value the signature to 1-, 2-, and 3-year survival. These values were 0.706, 0.695, and 0.665 in the testing dataset, and 0.720, 0.720, and 0.715 in entire patient dataset, respectively (Figure 6C). Therefore, these results indicated that TNF-related lncRNA prognostic signature had a good prognostic predictive value for LUAD patients.

Figure 6 Validation of the TNF-related lncRNA prognostic signature in the testing dataset and entire patient dataset. (A) Heatmap of eight gene expression, risk score and survival status. (B) Kaplan-Meier curves of OS were stratified by TNF family-related lncRNA prognostic signature in high and low risk groups based on the risk score. (C) Time-dependent AUC of the risk scores for 1-, 2-, and 3-year OS prediction. TNF, tumor necrosis factor; lncRNA, long non-coding RNA; OS, overall survival; AUC, area under the curve.

Evaluation of the performance of TNF-related lncRNA prognostic signature in different clinical subgroups

To further verify the prognostic value of TNF family-related lncRNA prognostic signature in different clinical subgroups, the univariate and multivariate Cox regression analysis were performed. The results showed that stage and risk score were independent prognostic factors (Figure 7A,7B). Then, the nomogram was constructed to quantitatively evaluate the survival probability with traditional clinical variables and the risk score (Figure 7C). The calibration diagram showed that the prediction and observation values of nomogram model were consistent (Figure 7D). Moreover, the AUCs showed that the AUC value of risk score (AUC =0.720) was higher than that of age (AUC =0.503) and gender (AUC =0.591) (Figure 7E).

Figure 7 Evaluation of the performance of TNF-related lncRNA prognostic signature in different clinical subgroups. (A) Univariate Cox regression analysis. (B) Multivariate Cox regression analysis. (C) A nomogram model was construct with traditional clinical variables and the risk score. (D) Calibration curves of nomogram model for predicting 1-, 2-, and 3-year OS. (E) AUC of traditional clinical variables and the risk score. *, P<0.05; ***, P<0.001. TNF, tumor necrosis factor; lncRNA, long non-coding RNA; OS, overall survival; AUC, area under the curve; MNA, mini nutritional assessment; T, tumor; N, node.

The treatment regimen and prognosis are significantly different between different cancer stages, so we further evaluated the prognostic value of the TNF-related lncRNA prognostic signature in different clinical stages. The KM survival analysis showed that the early-stage (stages I and II) and advanced-stage (stages III and IV) patients had less favorable OS in the high-risk group than that of the low-risk group (Figure 8, P<0.001). The results showed that the signature maintained a reliable predictive performance in different subgroups of LUAD patients.

Figure 8 The Kaplan-Meier survival analysis of the patients in the high and low-risk groups with early-stage LUAD (stages I and II) (A) and advanced-stage LUAD (stages III and IV) (B). LUAD, lung adenocarcinoma.

Identification of TNF-related lncRNA prognostic signature-related biological pathways

As the TNF-related lncRNA prognostic signature had a high prognostic predictive value for LUAD patients, we continued to explore the biological features. The GO and KEGG pathway analysis were used to explore the signature-related biological pathways. The results of GO analysis indicated that these genes were closely involved in the biological process of humoral immune response, immune response-related signaling pathway, B cell activation, and B cell receptor signaling pathway (Figure 9A). Meanwhile, KEGG analysis showed that primary immunodeficiency, cytokine-cytokine receptor interaction, B cell receptor signaling pathway, complement, and coagulation cascades were mainly enriched (Figure 9B).

Figure 9 The GO and KEGG pathway analysis were used to explore the signature-related biological pathways. (A) GO analysis; (B) KEGG analysis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; ECM, extracellular matrix.

Correlation of TNF-related lncRNA prognostic signature and immunotherapy response

As the results of GO and KEGG pathway analysis showed that it was closely relevant to the immune-related signaling pathway, we further evaluated the correlation of the TNF-related lncRNA prognostic signature and immunotherapy response. The results showed that many interleukin (IL) subtypes, including IL1A, IL1R1, IL1R2, IL2, IL2RB, IL9, and IL22, were closely associated with the risk scores (Figure 10A). Then, we further scored the TIDE, and found that high-risk patients had a lower TIDE score than that of low-risk patients, indicating that high-risk patients identified by TNF-related lncRNA prognostic signature may be appropriate candidates for immunotherapy (Figure 10B).

Figure 10 Correlation of TNF-related lncRNA prognostic signature and immunotherapy response. (A) An association analysis between TNF family-related lncRNA prognostic signature and immune checkpoints. (B) The distribution of TIDE score in high and low risk subgroups. **, P<0.01; ***, P<0.001. TNF, tumor necrosis factor; lncRNA, long non-coding RNA; TIDE, tumor immune dysfunction and exclusion.

Discussion

LUAD, as the primary subtype of NSCLC, has become one of the most lethal cancers. With increasing numbers of the identified biomarkers in recent years, we have a better understanding of the origin of LUAD to improve the treatment and prognosis, but the recurrence and the survival rates remain unfavorable (27,28). Therefore, looking for better predictive biomarkers of treatment response and prognosis would be helpful for optimization of personalized treatment and prognosis management of LUAD patients. The TNF family is expressed in almost all cells and is involved in many cellular processes, including cell survival and immunity (29). Studies have demonstrated that the TNF-related signature can predict the prognosis of various cancers, such as cervical cancer, bladder cancer, and SCLC (10,30,31). Moreover, various lncRNAs have been demonstrated to play important roles by mediating TNF family members in multiple cancers, including lung cancer (22-25). These TNF-related lncRNAs were involved in carcinogenesis, and inflammatory and immune responses (22-25). A number of studies have also demonstrated that some of TNF-related lncRNAs are closely associated with cancer proliferation and survival (32-38). Thus, we analyzed the expression patterns and clinical significance of TNF-related lncRNAs in LUAD. In this study, we identified 8 TNF-related lncRNAs that were closely associated with OS of LUAD patients by univariate Cox proportional hazards regression analysis. Then, we used the identified 8 lncRNAs to construct TNF-related lncRNA prognostic signature, and found that the signature had a high predictive capacity of prognosis for LUAD in 3 datasets. Furthermore, the univariate and multivariate Cox regression analysis showed that stage and risk score were independent prognostic factors in different clinical subgroups, and the KM survival analysis also showed that the early-stage (stages I and II) and advanced-stage (stages III and IV) patients had less favorable OS in the high-risk group than that of the low-risk group.

Studies have reported that the enrolled 8 TNF-related lncRNAs (LINC01137, LINC02310, AC017100.1, AC108676.1, AC118757.1, AL357873.1, AC104971.3, and HLA-DQB1-AS1) are closely related with cancer progression. LINC01137 has been shown to play an oncogenic role in NSCLC and oral squamous cell carcinoma, which is similar with our results that NSCLC patients with low expression of LINC01137 were likely to exhibit better outcomes (32,33). Zhao et al. demonstrated that LINC02310, as an enhancer in LUAD, obviously promoted the growth and proliferation of LUAD cells, and its overexpression caused low survival probability (34). In addition, AC017100.1 was shown to be a protective factor for the prognosis of prostate cancer (35). HLA-DQB1-AS1 has been shown to interact with zinc finger RANBP2-type containing 2 (ZRANB2) protein to promote proliferation and inhibit apoptosis in hepatocellular carcinoma (36), suggest that higher expression of HLA-DQB1-AS1 might have a negative impact on survival in hepatocellular carcinoma, while it was demonstrated to be beneficial for the survival of patients with LUAD and melanoma (37,38). Therefore, the role of HLA-DQB1-AS1 in cancer was related to the type of cancers, and the mechanism needs to explore further. Moreover, AC108676.1, AC118757.1, AL357873.1, and AC104971.3 have not been previously reported. This is the first time that they have been reported to be considered as the prognostic biomarkers of LUAD.

The GO and KEGG pathway analysis provided a further understanding of TNF family-related lncRNA prognostic signature-related biological pathways, and found that these genes were closely involved in immune-related signaling pathways. Immunotherapy has emerged as a novel treatment modality and become the standard treatment for many kinds of cancers, including LUAD (39). However, some LUAD patients still develop disease progression (40). Many factors have been revealed to affect the response to ICIs, such as the pro-inflammatory cytokines and the expression level of PD-L1 and others (41,42). In this study, we found that many IL subtypes, including IL1A, IL1R1, IL1R2, IL2, IL2RB, IL9, and IL22, were closely associated with the risk scores, suggesting that these pro-inflammatory cytokines are involved in immunotherapy response of LUAD patients. Moreover, studies have revealed 2 distinct mechanisms of tumor immune evasion: T cell dysfunction and T cell exclusion (43,44). From this, Jiang et al. developed TIDE to identify factors that underlie these 2 mechanisms (45). Compared with widely used ICI response biomarkers, such as PD-L1 level and tumor mutation load, TIDE signature showed better predictor for both anti-PD1 and anti-CTLA4 therapies (45). Thus, we further scored the TIDE, and found that high-risk patients had a lower TIDE score than that of low-risk patients, indicating that high-risk patients identified by TNF-related lncRNA prognostic signature may be appropriate candidates for immunotherapy.

However, this study has some limitations. We only used the data from the public dataset TCGA to construct and validate TNF-related lncRNA prognostic signature. More in vitro and/or in vivo experiments are needed to verify the clinical predictive ability of the signature.

Conclusions

In conclusion, this study is the first to construct and validate a prognostic predictive signature of LUAD patients based on TNF-related lncRNAs. The signature showed good performance to predict immunotherapy response in LUAD. Therefore, this signature may provide new strategies for individualized treatment of LUAD patients.

Acknowledgments

Funding: This study was supported by the Tackling-plan Project of Henan Department of Science and Technology (No. 212102310325) and the Tackling-plan Project of Henan Medical Science and Technology (No. LHGJ20200164).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-184/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-184/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-184/coif). The authors have no conflicts of interest to declare.

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.The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)