Skip to main content
Download PDF

Construction of an immunogenic cell death-related LncRNA signature to predict the prognosis of patients with lung adenocarcinoma

BMC Medical Genomics volume 17, Article number: 277 (2024) Cite this article

Abstract

Background

Lung adenocarcinoma (LUAD) is one of the most common malignant diseases worldwide. This study aimed to construct an immunogenic cell death (ICD)-related long non-coding RNA (lncRNA) signature to effectively predict the prognosis of LUAD.

Methods

The RNA-sequencing and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox proportional hazard regression analysis were utilized to construct lncRNA signature. Then, the reliability of the signature was evaluated in the training, validation and whole cohorts. The differences in the immune landscape and drug sensitivity between the low- and high-risk groups were analyzed. Finally, the expression level of the selected ICD-related lncRNAs in LUAD cell lines via reverse transcription quantitative PCR (RT-qPCR). CCK-8 and transwell assays were performed to study biological function of AC245014.3.

Results

A signature consisting of 5 ICD-related lncRNAs was constructed. Kaplan Meier (K-M) survival analysis showed shorter overall survival (OS) in high-risk group. The receiver operating characteristic (ROC) curves and Multivariate Cox regression analysis showed the signature was good predictive and independent prognostic factor in LUAD. Moreover, the high-risk group had a lower level of antitumor immunity and was less sensitive to some chemotherapeutics and targeted drugs. Finally, the expression level of selected ICD-related lncRNAs was validated in LUAD cell lines by RT-qPCR. Knockdown of AC245014.3 significantly suppressed LUAD proliferation, migration and invasion.

Conclusions

In this study, an ICD-related lncRNA signature was constructed, which could accurately predict the prognosis of LUAD patients and guide clinical treatment.

Peer Review reports

Introduction

Lung cancer remains one of the most common malignancies and the leading cause of cancer-related death worldwide according to the report by GLOBOCAN 2020 [1]. Among them, lung adenocarcinoma (LUAD) is the most common pathological type, accounting for approximately 40% of all cases [2]. Due to the lack of obvious clinical symptoms, most LUAD patients are already in the advanced stage when diagnosed, and the 5-year survival rate of patients diagnosed with advanced LUAD is only 15% [3, 4]. In recent years, the emergence of targeted drugs and immune checkpoint inhibitors has largely improved the clinical outcomes of LUAD, but the prognosis of LUAD patients remains poor. Therefore, it is crucial to identify new effective therapeutic targets and reliable biomarkers that can contribute to the prognostic evaluation of LUAD.

Immunogenic cell death (ICD) has been identified as a type of regulated cell death that is enough to activate an adaptive immune response in immunocompetent syngeneic hosts [5, 6]. Upon the induction of ICD, dying tumor cells produce a series of signaling molecules called damage-associated molecular patterns (DAMPs), mainly including surface-exposed calreticulin (CRT), secreted adenosine triphosphate (ATP) and released high mobility group protein B1 (HMGB1) [7, 8]. It is believed that released DAMPs can promote the recruitment and maturation of antigen-presenting cells (APCs), such as dendritic cells (DCs), thereby mediating the presentation of tumor-associated antigens to effector CD8+ T cells [9, 10]. Consequently, via antigen presentation, DCs stimulate specific T cell response that kills more cancer cells [11]. Accumulating preclinical and clinical evidence suggests that various DAMPs and DAMP-related processes may have prognostic value in cancer patients [12].

Long non-coding RNAs (lncRNAs) are defined as a class of nonprotein-coding RNAs with more than 200 nucleotides in length [13, 14]. Although lncRNAs do not encode proteins, they have several specific functions, such as mRNA splicing, transcription regulation, and mRNA post-transcriptional regulation [15]. Previous studies have shown that lncRNAs play an important role in tumor development, and many lncRNAs can serve as prognostic biomarkers and novel therapeutic targets in various cancers [16, 17]. However, few studies have focused on ICD-related lncRNAs in LUAD, and numerous lncRNAs that regulate ICD have not been identified.

In this study, an ICD-related lncRNA signature was constructed that could effectively predict the prognosis of LUAD patients. The possible biological pathways involved in the signature were analyzed by gene set enrichment analysis (GSEA). The differences in the immune landscape and drug sensitivity between the low-risk and high-risk groups were also explored. The expression level of the selected ICD-related lncRNAs were validated in LUAD cell lines by reverse transcription quantitative PCR (RT-qPCR). The biological function of AC245014.3 was validated in vitro.

Methods

Data collection

The RNA-sequencing (RNA-seq) and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA, http://portal.gdc.cancer.gov/) database. LUAD samples with no survival information or survival time < 30 days were excluded. Finally, 445 LUAD samples and 54 normal samples were included in this study. In addition, ICD-related genes were obtained from the published literatures, which had been previously summarized by Abhishek et al. [18].

Identification of differentially expressed ICD-related lncRNAs

Based on the annotated file of human genes downloaded from the Ensemble website (http://grch37.ensembl.org/index.html), the expression of 13,333 lncRNAs was extracted from TCGA-LUAD RNA-seq data. Pearson correlation analysis was performed to analyze the relevance between ICD-related genes and all lncRNAs. ICD-related lncRNAs were defined as lncRNAs that were significantly related to at least one ICD-related gene (|cor| ≥ 0.4, P < 0.05). Then, ICD-related lncRNAs that were differentially expressed between LUAD and normal samples were screened using the “limma” R package (version 3.56.2) (P < 0.05, |log2FC | > 1).

Construction and validation of an ICD-related lncRNA signature

First, all LUAD samples were randomly divided into a TCGA training cohort (n = 223) and a TCGA validation cohort (n = 222). ICD-related lncRNAs associated with prognosis were screened by univariate Cox regression analysis in the training cohort (P < 0.05). Candidate ICD-related lncRNAs were further selected through least absolute shrinkage and selection operator (LASSO) analysis. Finally, stepwise multivariate Cox proportional hazard regression analysis was performed to construct an ICD-related lncRNA signature. The risk score of each LUAD patient was calculated according to the following formula: Risk score = \(\:{\sum\:}_{i=1}^{n}Cofe\left(i\right)\times\:X\left(i\right)\),

where Cofe represents the regression coefficient, and X represents the expression level of corresponding lncRNA. Patients were divided into the low-risk and high-risk groups according to the median risk score, and Kaplan‒Meier (K-M) survival curves were plotted using the “survival” R package (version 3.5). The receiver operating characteristic (ROC) curves were plotted by the “survivalROC” R package (version 1.0.3.1), and the predictive ability of the signature for the prognosis of LUAD patients was evaluated by calculating the area under the curve (AUC). Risk curves and survival state diagrams were also plotted. The independent prognostic value of the signature was assessed by univariate and multivariate Cox regression analysis. The above methods were also applied to verify the performance of the signature in the validation and whole cohorts.

GSEA

GSEA was performed in the whole cohort using GSEA 4.1.0 software to explore the molecular mechanisms involved in the signature. The weighted enrichment method was used for enrichment analysis, and the number of random combinations was set as 1000 times. Gene sets with P < 0.05 and false discovery rate < 0.25 were considered significantly enriched.

Estimation of the immune landscape

The stromal score, immune score and ESTIMATE score were calculated in the whole cohort by the “ESTIMATE” R package (version 1.0.13). Single-sample gene set enrichment analysis was performed using the “GSVA” R package (version 1.48.3) to calculate the enrichment score of immune cells and evaluate the activity of immune-related pathways. The differences in the immune landscape between the low-risk and high-risk groups were compared.

Drug sensitivity analysis

The “pRRophetic” R package (version 0.5) was used to assess the half-maximal inhibitory concentration of the chemotherapeutics and targeted drugs that had been approved by the food and drug administration (FDA). The differences in drug sensitivity between the low-risk and high-risk groups were analyzed.

Cell culture

The human bronchial epithelial cell line BEAS-2B and the human LUAD cell lines A549, PC9 and H1975 were purchased from iCell Bioscience Inc (Shanghai, China). All of the cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37 °C in a humidified atmosphere with 5% CO2.

Cell transfection

First, PC9 cells were seeded into the six-well plates and cultured to 50-60% confluence. Then the cells were transfected with small interfering RNA (siRNA) targeting AC245014.3 (si-AC245014.3) or negative control (si-NC) according to the instructions of transfection reagent siRNA-Mate™ (GenePharma, Shanghai, China). The siRNA sequences were listed in Supplementary Table 1. RNA was extracted 48 h after cell transfection, and the knockdown efficiency was verified using RT-qPCR. The successfully transfected cells then were used for subsequent functional experiments.

Reverse transcription quantitative PCR

Total RNA was isolated from cells using FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, Nanjing, China), and then reverse-transcribed into cDNA using the HiScript® III RT SuperMix kit (Vazyme). ChamQ Universal SYBR qPCR Master Mix (Vazyme) and a CFX96 Real-Time PCR system (Bio-Rad, USA) were used for RT-qPCR. GAPDH was employed as an internal control. The relative expression level of lncRNAs were calculated utilizing the 2−ΔΔCt method. The primers sequences were listed in Supplementary Table 2.

Cell proliferation assay

Cell Counting Kit-8 (CCK-8) assay was used to assess cell proliferation. In brief, PC9 cells were seeded into 96-well plates at a density of 1000 cells per well. At the different timepoint, 10ul CCK-8 (FUDE, Hangzhou, China) reagent was added to each well. After 2.5 h incubation, the absorbance was measured at 450 nm wave length.

Transwell assay

The migration and invasion ability of cells was evaluated by transwell assay. Briefly, 4 × 105 PC9 cells with 200ul FBS-free medium were placed into the upper chamber coated with or without Matrigel (ABW, Shanghai, China), and 800ul medium containing 10% FBS was added to the lower chamber. After incubation for 48 h at 37℃, the cells that passed through the membrane were fixed with 4% methanol and stained with 5% crystal violet. The stained cells were counted using a microscope.

Statistical analysis

R 4.1.2 and GraphPad Prism 8.0 software were utilized to analyze and visualize data. Univariate Cox regression analysis was used to screen ICD-related lncRNAs associated with prognosis. K-M analysis was performed to evaluate the differences in survival time between the low-risk and high-risk groups. The predictive ability of the signature was assessed by plotting ROC curves. Multivariate Cox regression analysis was used to evaluate the independent prognostic value of the signature. Wilcoxon test, Kruskal-Wallis test and logistic regression were used to analyzed the relationship between risk score and clinicopathological features.

Results

Identification of differentially expressed ICD-related lncRNAs

Figure 1 depicted the overall framework of the study. We obtained 33 ICD-related genes from the published literatures, which had been previously summarized by Abhishek et al. [18]. The expression of 33 ICD-related genes and 13,333 lncRNAs was extracted from TCGA-LUAD RNA-seq data. Through Pearson correlation analysis, we identified 733 ICD-related lncRNAs, among which 244 were upregulated and 61 were downregulated in LUAD. The expression profiles of differentially expressed lncRNAs were visualized in the form of a heatmap and a volcano plot (Fig. 2A and B).

Fig. 1
figure 1

The flow chart of the study. ICD: immunogenic cell death; LUAD: lung adenocarcinoma; LASSO: least absolute shrinkage and selection operator; GESA: gene set enrichment analysis

Full size image
Fig. 2
figure 2

Differentially expressed ICD-related lncRNAs associated with prognosis (A) The heatmap of differentially expressed ICD-related lncRNAs between LUAD and normal samples. (B) The volcano plot of differentially expressed ICD-related lncRNAs between LUAD and normal samples. The red dots represent up-regulated and green for down-regulated. (C) Differentially expressed ICD-related lncRNAs associated with the prognosis of LUAD patients. ICD: immunogenic cell death; LUAD: lung adenocarcinoma

Full size image

Construction of an ICD-related lncRNA signature in the training cohort

A total of 7 ICD-related lncRNAs were found to be significantly associated with overall survival (OS) in the training cohort (Fig. 2C). Then, LASSO analysis and stepwise multivariate Cox proportional hazard regression analysis were utilized to construct a risk model composed of 5 ICD-related lncRNAs to predict the prognosis of LUAD patients. The risk score for each LUAD patient in the training cohort was calculated according to the following formula: Risk score = (0.655730531649772 × expression of AC245014.3) + (0.462817768020813 × expression of LINC00941) + (0.877653684548024 × expression of AL358115.1) + (-1.51062067434958 × expression of AC243960.3) + (-1.35918746869745 × expression of AC005180.1). LUAD patients in the training cohort were divided into the low-risk and high-risk groups according to the median risk score (0.994682147). K-M survival analysis showed that the OS of patients in the high-risk group was significantly shorter than that of patients in the low-risk group (P = 0.003, Fig. 3A). The ROC curve was used to assess the prognostic ability of the signature, and the AUC was 0.725 at 1 year, 0.717 at 3 years and 0.742 at 5 years (Fig. 3B). The risk curve and survival state diagram showed that the risk of death in LUAD patients increased with increasing risk score (Fig. 3C and D). Univariate and multivariate Cox regression analysis showed that the ICD-related lncRNA signature was associated with the poor prognosis of LUAD patients and had independent prognostic value (P < 0.001, Fig. 3E and F).

Fig. 3
figure 3

Performance verification of the signature in the training cohort (A) Kaplan–Meier curve of patients in the low-risk (blue) and high-risk (red) groups. (B) The receiver operating characteristic curve analysis of the signature. (C, D) The risk curve, survival state diagram of LUAD patients. (E, F) Univariate and multivariate cox regression analysis in the training cohort. LUAD: lung adenocarcinoma

Full size image

Testing in the validation cohort and the whole cohort

The risk score of LUAD patients in the validation cohort was calculated according to the above formula. The median risk score in the training cohort was adopted to divide the validation cohort into a low-risk group (n = 108) and a high-risk group (n = 114). The K-M survival curve showed that the OS of the high-risk group was significantly shorter than that of the low-risk group (P = 0.013, Fig S1A). The AUC values of the signature for predicting 1-year, 3-year and 5-year OS were 0.650, 0.616 and 0.670, respectively (Fig S1B). The results of the risk curve and survival state diagram in the validation cohort were consistent with those in the training cohort (Fig S1C, 1D). Univariate Cox regression analysis showed that the ICD-related lncRNA signature was associated with the poor prognosis of LUAD patients (P < 0.001, Fig S1E). However, multivariate Cox regression analysis showed that the ICD-related lncRNA signature was not an independent prognostic factor in the validation cohort (P = 0.123, Fig S1F).

We then further verified the performance of the ICD-related lncRNA signature in the whole cohort and achieved similar results. According to the median risk score in the training cohort, LUAD patients in the whole cohort were divided into a low-risk group (n = 220) and a high-risk group (n = 225). K-M survival analysis showed that the OS of patients in the high-risk group was significantly shorter than that of patients in the low-risk group (P < 0.001, Fig S2A). The AUC values corresponding to 1, 3, and 5 years were 0.689, 0.661 and 0.701, respectively (Fig S2B). The risk curve and survival state diagram were similar to those in the other two cohorts (Fig S2C, 2D). Univariate and multivariate Cox regression analysis showed that the ICD-related lncRNA signature was an independent prognostic factor for LUAD (P < 0.001, Fig S2E, 2F).

Relationship between risk score and clinicopathological features

The relationship between risk score and clinicopathological characteristics of LUAD patients was analyzed. The results showed that the risk score of male patients was significantly higher than that of female patients (P = 0.001), and the risk score increased with the increase of clinical stage (P = 0.01), T stage (P = 0.005) and M stage (P = 0.03). However, there was no statistical difference in risk score among different age groups and different N stages (P > 0.05) (Fig. 4A and F). Similarly, the results of logistic regression showed that the risk score was significantly associated with gender, clinical stage and distant metastasis, but not with age and lymph node metastasis (Table 1).

Fig. 4
figure 4

The relationship between risk score and clinicopathological features, including age (A), gender (B), clinical stage (C), T stage (D), N stage (E), M stage (F)

Full size image
Table 1 The association between risk score and clinicopathological features (logistic regression)
Full size table

GSEA

The possible biological pathways involved in the ICD-related lncRNA signature were analyzed by GSEA. The results showed that in the high-risk group, pathways closely related to tumorigenesis, such as cell cycle, DNA replication, RNA degradation, nucleotide excision repair, mismatch repair and the p53 pathway, were significantly enriched (P < 0.05, false discovery rate < 0.25, Fig. 5).

Fig. 5
figure 5

Gene set enrichment analysis in the whole cohort

Full size image

Immune landscape analysis

The differences in the immune landscape between the low-risk and high-risk groups were analyzed. The results showed that the stromal score, immune score and ESTIMATE score was significantly lower in the high-risk group than in the low-risk group (P < 0.05, Fig. 6A and C). In addition, the high-risk group had a lower level of immune cell infiltration than the low-risk group (P < 0.05, Fig. 6D). Except for cytolytic activity and the MHC class I pathway, the immune pathways were significantly less active in the high-risk group than in the low-risk group (P < 0.05, Fig. 6E).

Fig. 6
figure 6

The differences in immune landscape and drug sensitivity between the low-risk and high-risk groups (A-C) The differences in stromal score, immune score and ESTIMATE score between the low-risk and high-risk groups. (D) The differences in the infiltration level of immune cells between the low-risk and high-risk groups. (E) The differences in the activity of immune-related pathways between the low-risk and high-risk groups. (F-I) The differences in drug sensitivity (Doxorubicin, Etoposide, Paclitaxel and Erlotinib) between the low-risk and high-risk groups. *, P < 0.05; **, P < 0.01; ***, P < 0.001

Full size image

Drug sensitivity analysis

In order to enhance the relationship of the signature and clinical application, the differences in drug sensitivity were compared between the low-risk and high-risk groups. The results showed that LUAD patients in the high-risk group were less sensitive to some chemotherapeutics and targeted drugs commonly used in LUAD, such as doxorubicin, etoposide, paclitaxel and erlotinib (P < 0.05, Fig. 6F and I).

Validating the expression level of ICD-related lncRNAs in cell lines

The expression level of the selected ICD-related lncRNAs in LUAD cell lines was further validated by RT-qPCR. The results showed that the expression level of AC245014.3, LINC00941 and AL358115.1 was upregulated in LUAD cells lines compared with the human bronchial epithelial cell line, while AC243960.3 and AC005180.1 showed the opposite trend (P < 0.05, Fig. 7A and E).

Fig. 7
figure 7

Verification of the expression level and biological function of ICD-related lncRNAs in LUAD cells The expression level of AC245014.3 (A), LINC00941 (B), AL358115.1 (C), AC243960.3 (D) and AC005180.1 (E) in LUAD cells. (F) Validation of knockdown efficiency of AC245014.3 in PC9 cells. (G) Cell growth curves of PC9 cells transfected with si-AC245014.3 or si-NC. (H, I) The migration and invasion ability of PC9 cells transfected with si-AC245014.3 or si-NC. ICD: immunogenic cell death; NC: negative control. **, P < 0.01; ***, P < 0.001

Full size image

Knockdown of AC245014.3 inhibits LUAD cells proliferation and migration

Among the 5 lncRNAs, AC245014.3, LINC00941 and AL358115.1 were risk factors. The function and mechanism of LINC00941 in LUAD have been reported in previous study [19]. Therefore, we selected AC245014.3, which has a relatively higher expression level in LUAD cell lines, for further functional analysis. First, we knocked down AC245014.3 in PC9 cells with siRNA and detected the knockdown efficiency by RT-qPCR (Fig. 7F). The results of CCK-8 assay showed that knockdown of AC245014.3 significantly inhibited the proliferation ability of PC9 cells (Fig. 7G). In addition, scratch test and transwell assay revealed that the migration and invasion ability of PC9 cells was significantly decreased after AC245014.3 silencing (Fig. 7H and I).

Discussion

LUAD is one of the most common malignancies with high mortality worldwide. Identifying reliable prognostic biomarkers remains critical for LUAD patients. In this study, we constructed a signature consisting of 5 ICD-related lncRNAs (AC245014.3, LINC00941, AL358115.1, AC243960.3, AC005180.1) based on the TCGA database. According to the median risk score, LUAD patients were divided into the low-risk and high-risk groups. K-M survival analysis showed that the OS of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The ROC curves showed that the signature had good predictive ability for the prognosis of LUAD patients. Univariate and multivariate Cox regression analysis showed that the signature was an independent prognostic factor in LUAD. In addition, we analyzed the relationship between risk score and clinicopathological features, and the results showed that the risk score of LUAD patients increased with the increase of clinical stage, T stage and M stage. The results of GSEA showed that pathways closely related to tumorigenesis, such as cell cycle, DNA replication, RNA degradation, nucleotide excision repair, mismatch repair and the p53 pathway, were significantly enriched in the high-risk group. The above results suggested that the signature constructed in this study could be effective for the prognostic evaluation of LUAD. In addition, compared to the model constructed in previous study, our signature had a similar AUC, but utilized fewer ICD-related LncRNAs [20].

ICD is a type of cancer cell death induced by certain chemotherapeutic drugs, ionizing radiation, photodynamic therapy, and high hydrostatic pressure [21]. It initiates CD8+ T cell-mediated tumor-specific immune response through the emission of DAMPs, mainly including CRT, ATP and HMGB1, thereby inducing long-term efficacy of anticancer drugs through direct killing of cancer cells and antitumor immunity [7, 21]. CRT, which originally localizes on the endoplasmic reticulum, is exposed on the cell surface as an “eat me” signal during ICD and potently stimulates the phagocytosis of tumor antigens by DCs [22]. During the course of ICD, dying tumor cells release ATP into the extracellular space, and the extracellular ATP acts as a “find me” signal to recruit and activate APCs [23]. HMGB1 is a non-histone chromatin-binding protein localized in the nucleus. When released from dying cells, extracellular HMGB1 has potent immunostimulatory effect by interacting with distinct pattern recognition receptors, such as Toll-like receptor 4 and the receptor of advanced glycation end products [24]. In recent years, an increasing number of studies have focused on the application of ICD inducers in cancer therapy [25,26,27]. ICD is expected to provide new ideas and strategies for cancer treatment due to its ability to activate antitumor immunity and achieve the release of multiple tumor antigens.

LncRNAs can function as oncogenes or tumor suppressors in many types of cancer, including LUAD [28,29,30,31]. Moreover, in recent years, with the advancements in bioinformatics, there has been a growing number of studies utilizing public databases to construct lncRNA models aimed at predicting the prognosis of cancer patients [32,33,34,35,36]. For example, a novel immune-related four-lncRNA signature was reported to effectively predict the prognosis of LUAD patients [33]. Another metastasis-associated lncRNA signature was developed and shown to effectively predict the risk of LUAD recurrence [34]. However, few ICD-related lncRNAs have been identified, and the prognostic value of ICD-related lncRNAs in LUAD is unclear. In the present study, we constructed the ICD-related lncRNA signature in LUAD, which could provide a promising strategy for guiding individualized treatment and improving prognosis prediction.

In recent years, the tumor microenvironment has become an area of research focus. Increasing evidence indicates that tumor-invasive immune cells play a key role in cancer development [37, 38]. In the present study, we found that the enrichment score of key antitumor infiltrating immune cells was significantly lower in the high-risk group than in the low-risk group. As an integral component of the tumor microenvironment, tumor-infiltrating B lymphocytes exist in all stages of cancer and play an important role in tumor development [39]. Multiple studies have reported that tumor-infiltrating B lymphocytes are associated with a good prognosis in non-small cell lung cancer patients [40, 41]. As the most important professional APCs in vivo, DCs can present tumor antigens to antigen-specific helper cells and cytotoxic T cells [42]. Studies have shown that DC infiltration is related to protective immunity in LUAD [43]. Neutrophils that infiltrate tumor tissue are termed tumor-associated neutrophils [44]. Studies have shown that tumor-associated neutrophils can kill tumor cells in a variety of ways [45, 46]. In addition, the results of this study showed that except for cytolytic activity and the MHC class I pathway, other immune pathways were poorly activated in the high-risk group. The stromal score, immune score and ESTIMATE score were significantly lower in the high-risk group than in the low-risk group. Based on these findings, the poor survival outcome of the high-risk group may be caused by decreased antitumor immunity.

In order to improve the clinical application value of the signature constructed in this study, the differences in drug sensitivity were compared between the low-risk and high-risk groups. The results showed that LUAD patients in the high-risk group were less sensitive to some chemotherapeutics and targeted drugs commonly used in LUAD, such as doxorubicin, etoposide, paclitaxel and erlotinib. Hyposensitivity to the commonly used drugs may also be associated with the poor prognosis of LUAD patients in the high-risk group.

Finally, we validated the expression level of the selected ICD-related lncRNAs in LUAD cell lines by RT-qPCR. The results showed that AC245014.3, LINC00941 and.

AL358115.1 were upregulated, while AC243960.3 and AC005180.1 were downregulated in LUAD cell lines. By searching published literatures, we found that the biological function of the selected ICD-related lncRNAs in LUAD has not been studied, except for LINC00941. Considering the relatively higher expression level in LUAD cells, we selected AC245014.3 for further functional analysis. The results showed that knockdown of AC245014.3 significantly inhibited the proliferation, migration and invasion ability of PC9 cells.

However, there are some limitations in our study. First of all, our study data were mainly from TCGA database, and the prognostic value of the signature needs to be further validated in an independent real-world cohort. In addition, we did not verify the expression level of the 5 ICD-related lncRNAs in clinical samples. Finally, the possible molecular mechanisms involved in the signature need to be further verified by basic experiments.

Conclusion

In this study, a signature consisting of 5 ICD-related lncRNAs was established based on TCGA database. This signature can accurately predict the prognosis of LUAD patients and has the potential to help guide clinical treatment.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. Denisenko TV, Budkevich IN, Zhivotovsky B. Cell death-based treatment of lung adenocarcinoma [J]. Cell Death Dis. 2018;9(2):117.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015 [J]. CA Cancer J Clin. 2016;66(2):115–32.

    Article  PubMed  Google Scholar 

  4. Duma N, Santana-Davila R, Molina JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment [J]. Mayo Clin Proc. 2019;94(8):1623–40.

    Article  CAS  PubMed  Google Scholar 

  5. Galluzzi L, Vitale I, Warren S et al. Consensus guidelines for the definition, detection and interpretation of immunogenic cell death [J]. J Immunother Cancer. 2020;8(1).

  6. Galassi C, Klapp V, Yamazaki T, et al. Molecular determinants of immunogenic cell death elicited by radiation therapy [J]. Immunol Rev. 2024;321(1):20–32.

    Article  CAS  PubMed  Google Scholar 

  7. Ahmed A, Tait SWG. Targeting immunogenic cell death in cancer [J]. Mol Oncol. 2020;14(12):2994–3006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Galluzzi L, Kepp O, Hett E, et al. Immunogenic cell death in cancer: concept and therapeutic implications [J]. J Transl Med. 2023;21(1):162.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Alzeibak R, Mishchenko TA, Shilyagina NY, et al. Targeting immunogenic cancer cell death by photodynamic therapy: past, present and future [J]. J Immunother Cancer. 2021;9(1):e001926.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tang D, Kepp O, Kroemer G. Ferroptosis becomes immunogenic: implications for anticancer treatments [J]. Oncoimmunology. 2020;10(1):1862949.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhou J, Wang G, Chen Y, et al. Immunogenic cell death in cancer therapy: present and emerging inducers [J]. J Cell Mol Med. 2019;23(8):4854–65.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Han X, Song D, Cui Y et al. Pan-cancer analyses of immunogenic cell death-derived gene signatures: potential biomarkers for prognosis and immunotherapy [J]. Cancer Rep (Hoboken). 2024;7(4):e2073.

  13. Gil N, Ulitsky I. Regulation of gene expression by cis-acting long non-coding RNAs [J]. Nat Rev Genet. 2020;21(2):102–17.

    Article  CAS  PubMed  Google Scholar 

  14. Mattick JS, Amaral PP, Carninci P, et al. Long non-coding RNAs: definitions, functions, challenges and recommendations [J]. Nat Rev Mol Cell Biol. 2023;24(6):430–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nandwani A, Rathore S, Datta M. LncRNAs in cancer: regulatory and therapeutic implications [J]. Cancer Lett. 2021;501:162–71.

    Article  CAS  PubMed  Google Scholar 

  16. Wu M, Zhang X, Han X, et al. The potential of long noncoding RNAs for precision medicine in human cancer [J]. Cancer Lett. 2021;501:12–9.

    Article  CAS  PubMed  Google Scholar 

  17. Bhattacharjee R, Prabhakar N, Kumar L, et al. Crosstalk between long noncoding RNA and microRNA in cancer [J]. Cell Oncol (Dordr). 2023;46(4):885–908.

    Article  CAS  PubMed  Google Scholar 

  18. Garg AD, De Ruysscher D, Agostinis P. Immunological metagene signatures derived from immunogenic cancer cell death associate with improved survival of patients with lung, breast or ovarian malignancies: a large-scale meta-analysis [J]. Oncoimmunology. 2016;5(2):e1069938.

    Article  PubMed  Google Scholar 

  19. Ren MH, Chen S, Wang LG, et al. LINC00941 promotes progression of non-small cell lung cancer by sponging mir-877-3p to regulate VEGFA expression [J]. Front Oncol. 2021;11:650037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sun D, Zhang C. Immunogenic cell death-related long noncoding RNA influences immunotherapy against lung adenocarcinoma [J]. Oncol Res. 2023;31(5):753–67.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhu M, Yang M, Zhang J, et al. Immunogenic cell death induction ionizing radiation [J]. Front Immunol. 2021;12:705361.

    CAS  PubMed  Google Scholar 

  22. Fucikova J, Kepp O, Kasikova L, et al. Detection of immunogenic cell death and its relevance for cancer therapy [J]. Cell Death Dis. 2020;11(11):1013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Krysko DV, Garg AD, Kaczmarek A, et al. Immunogenic cell death and DAMPs in cancer therapy [J]. Nat Rev Cancer. 2012;12(12):860–75.

    Article  CAS  PubMed  Google Scholar 

  24. Vaes RDW, Hendriks LEL, Vooijs M, et al. Biomarkers radiotherapy-induced immunogenic cell death [J]. Cells. 2021;10(4):930.

    CAS  PubMed  Google Scholar 

  25. Heshmati Aghda N, Abdulsahib SM, Severson C, et al. Induction of immunogenic cell death of cancer cells through nanoparticle-mediated dual chemotherapy and photothermal therapy [J]. Int J Pharm. 2020;589:119787.

    Article  CAS  PubMed  Google Scholar 

  26. Ma J, Zhang C, Shi G, et al. High-dose VitC plus oncolytic adenoviruses enhance immunogenic tumor cell death and reprogram tumor immune microenvironment [J]. Mol Ther. 2022;30(2):644–61.

    Article  CAS  PubMed  Google Scholar 

  27. Yu Z, Guo J, Hu M, et al. Icaritin exacerbates mitophagy and synergizes with doxorubicin to induce immunogenic cell death in hepatocellular carcinoma [J]. ACS Nano. 2020;14(4):4816–28.

    Article  CAS  PubMed  Google Scholar 

  28. Goodall GJ, Wickramasinghe VO. RNA in cancer [J]. Nat Rev Cancer. 2021;21(1):22–36.

    Article  CAS  PubMed  Google Scholar 

  29. Chandra Gupta S, Nandan Tripathi Y. Potential of long non-coding RNAs in cancer patients: from biomarkers to therapeutic targets [J]. Int J Cancer. 2017;140(9):1955–67.

    Article  CAS  PubMed  Google Scholar 

  30. Deng X, Xiong W, Jiang X, et al. LncRNA LINC00472 regulates cell stiffness and inhibits the migration and invasion of lung adenocarcinoma by binding to YBX1 [J]. Cell Death Dis. 2020;11(11):945.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hou M, Wu N, Yao L. LncRNA CBR3-AS1 potentiates Wnt/β-catenin signaling to regulate lung adenocarcinoma cells proliferation, migration and invasion [J]. Cancer Cell Int. 2021;21(1):36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bian G, Cao J, Li W, et al. Identification and validation of a cancer-testis antigen-related signature to predict the prognosis in stomach adenocarcinoma [J]. J Cancer. 2024;15(11):3596–611.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wang J, Yin X, Zhang YQ, et al. Identification and validation of a novel immune-related four-lncRNA signature for lung adenocarcinoma [J]. Front Genet. 2021;12:639254.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zhang X, Han J, Du L, et al. Unique metastasis-associated lncRNA signature optimizes prediction of tumor relapse in lung adenocarcinoma [J]. Thorac Cancer. 2020;11(3):728–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gui Z, Ye Y, Li Y, et al. Construction of a novel cancer-associated fibroblast-related signature to predict clinical outcome and immune response in cervical cancer [J]. Transl Oncol. 2024;46:102001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zhao Q, Ye Y, Zhang Q, et al. PANoptosis-related long non-coding RNA signature to predict the prognosis and immune landscapes of pancreatic adenocarcinoma [J]. Biochem Biophys Rep. 2024;37:101600.

    CAS  PubMed  Google Scholar 

  37. Liu X, Wu S, Yang Y, et al. The prognostic landscape of tumor-infiltrating immune cell and immunomodulators in lung cancer [J]. Biomed Pharmacother. 2017;95:55–61.

    Article  CAS  PubMed  Google Scholar 

  38. Ribatti D. The concept of immune surveillance against tumors. First Theor [J] Oncotarget. 2017;8(4):7175–80.

    Google Scholar 

  39. Wang SS, Liu W, Ly D, et al. Tumor-infiltrating B cells: their role and application in anti-tumor immunity in lung cancer [J]. Cell Mol Immunol. 2019;16(1):6–18.

    Article  CAS  PubMed  Google Scholar 

  40. Kinoshita T, Muramatsu R, Fujita T, et al. Prognostic value of tumor-infiltrating lymphocytes differs depending on histological type and smoking habit in completely resected non-small-cell lung cancer [J]. Ann Oncol. 2016;27(11):2117–23.

    Article  CAS  PubMed  Google Scholar 

  41. Ku BM, Kim Y, Lee KY, et al. Tumor infiltrated immune cell types support distinct immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer [J]. Eur J Immunol. 2021;51(4):956–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Catacchio I, Scattone A, Silvestris N, et al. Immune prophets of lung cancer: the prognostic and predictive landscape of cellular and molecular immune markers [J]. Transl Oncol. 2018;11(3):825–35.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang JB, Huang X, Li FR. Impaired dendritic cell functions in lung cancer: a review of recent advances and future perspectives [J]. Cancer Commun (Lond). 2019;39(1):43.

    PubMed  Google Scholar 

  44. Lecot P, Sarabi M, Pereira Abrantes M, et al. Neutrophil heterogeneity in cancer: from biology to therapies [J]. Front Immunol. 2019;10:2155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gershkovitz M, Fainsod-Levi T, Zelter T, et al. TRPM2 modulates neutrophil attraction to murine tumor cells by regulating CXCL2 expression [J]. Cancer Immunol Immunother. 2019;68(1):33–43.

    Article  PubMed  Google Scholar 

  46. Takeshima T, Pop LM, Laine A, et al. Key role for neutrophils in radiation-induced antitumor immune responses: potentiation with G-CSF [J]. Proc Natl Acad Sci U S A. 2016;113(40):11300–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are very grateful for the data provided by the TCGA database.

Funding

No fundings.

Author information

Author notes

  1. Shuaishuai Wang and Yi Zhang contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China

    Shuaishuai Wang

  2. Department of Orthopedic, Jinan Third People’s Hospital, Jinan, Shandong, China

    Yi Zhang

Authors
  1. Shuaishuai Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yi Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

YZ and SW designed the study. YZ and SW drafted the manuscript. SW collected and analyzed the data. YZ interpreted the results. YZ and SW revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shuaishuai Wang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, S., Zhang, Y. Construction of an immunogenic cell death-related LncRNA signature to predict the prognosis of patients with lung adenocarcinoma. BMC Med Genomics 17, 277 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02042-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02042-y

Keywords

BMC Medical Genomics

ISSN: 1755-8794

Contact us