Development of an immunogenic cell death-related lncRNAs signature for prognostic risk assessment in gastric cancer

Introduction

Gastric cancer (GC) is the fifth most common cancer worldwide and the third leading cause of cancer-related death (1,2). Helicobacter pylori is the leading cause of GC (3,4). The growth, spread, and metastasis of GC are influenced by genetic mutations, epigenetic alterations, and abnormal molecular signaling pathways (5). The prognosis for GC remains poor even though significant advances have been made in its treatment, and the 5-year survival rate is less than 30% (6-8). In addition, the onset of GC is relatively insidious, and it is often at an advanced stage when undergoing diagnosis, leading to a high mortality rate (9). Current studies are examining immunotherapeutic approaches, and they are approved as monotherapies as new treatment options.

Immune checkpoint therapy has been shown to be clinically more effective than conventional therapeutic modalities, with the most classical programmed death-ligand 1 (PD-L1) able to bind to programmed immune cells death 1, leading to T-cell suppression and apoptosis (10-12). It is believed that gallectin-9 and its receptor T-cell immunoglobulin and mucin-containing domain 3 (Tim3) act as co-suppressive receptors for immune cells. Tim3 is overexpressed on failing T cells in chronic viral infection or cancer, and in addition (13-15), Tim3 presents on dendritic cells (DCs) can be hyperfunctional (16-18). The two aforementioned receptors play a crucial role in the immune system’s resistance to tumors, including immunogenic cell death (ICD). This endoplasmic reticulum (ER) stress-induced apoptotic approach can activate the corresponding immune system by secreting or stimulating the release of various danger-associated molecular patterns (DAMPs). All cytotoxic therapies can lead to apoptosis, but three anthracyclines and oxaliplatin can induce ICD. The secretion of Association of Tennis Professionals and the release of the cell death-associated protein high mobility group box 1 are also considered to be two additional markers of ICD (19-21). Cytotoxic agents are able to be accurately predicted by the detection of these features to induce ICD in cancer cells. Among them are cytostatic agents, including oxaliplatin and anthracyclines, as well as treatments that cause ER stress, like radiotherapy. When DCs are stimulated, a cellular cascade is generated, at which point the ICD triggers an antigenic immune response against the dead cells. In addition, therapeutically driven ICD has been shown to produce an immune response against cancer. However, only a few ICD induction treatments are currently being utilized, so more in-depth exploration is needed to improve clinical care. It is also unclear how ICD induction can be used in clinical treatment protocols for GC. In two clinical case reports, ICD has been described after radiation therapy, and in one, sandalwood alcohol was described as an ICD-inducing agent (22-24). Additionally, ICD caused by treatment is a rare occurrence.

It has been shown that long non-coding RNAs (lncRNAs), which has a transcriptional length exceeding 200 nucleotides, plays an important role in growth control. The highly expressed PCED1B-AS1 in GC cell lines and tissues upregulates the expression of MAP2K7 by acting as a sponge for miR-3681-3p, thereby accelerating cell proliferation and inhibiting apoptosis, potentially serving as a therapeutic target for GC (25). Additionally, the methylation of PSMA3-AS1 and MIR22HG suppresses apoptosis in GC cells and promotes tumorigenesis through the miR-411-3p or MIR22HG-miR-24-3p-SERTAD1 axis (26). Similarly, upregulated SNHG26 interacts with nucleolin, regulating c-Myc expression, ultimately promoting the development of malignant tumors in GC by promoting energy metabolism through hexokinase 2 (27). Furthermore, research has found that hnRNPA2B1, as a methyladenosine reader, is upregulated in GC cells and tissues, interacting with lncRNA NEAT1 to enhance its stability, ultimately maintaining cell stemness and exacerbating chemoresistance in GC through the Wnt/β-catenin pathway (28). The study of lncRNAs prognosis models has also emerged alongside functional and mechanistic studies. Li et al. constructed a prognostic model based on 11 long noncoding RNAs involved in exosomes, which was found to be significantly associated with certain components of GC’s immune microenvironment and could be used in this context (29). The model the authors developed for prognosis of GC patients is based on four pyroptosis-related lncRNAs, has been validated with various samples, and has proved effective in guiding the immune microenvironment, demonstrating the feasibility of molecularly targeted therapies for prognostic indications (30). According to Luo et al., 12 necrosis-related long noncoding RNAs are used to determine the prognosis of GC cases (31). Although GC-related lncRNAs and their biological function have not yet been well studied, there is a lack of evidence regarding their clinical significance.

According to Figure 1, an lncRNAs signature significantly correlated to the ICD has been developed and validated in this study, and its clinical implications and associations with clinical characteristics of patients with GC have been evaluated. Additionally, we successfully established a nomogram, offering a more accurate prediction of GC patients’ prognoses. In addition, we also investigated the correlation between ICD-related lncRNAs signatures (ICDlncSig) and immune cell infiltration, as well as the response to immunotherapy, and found through in vitro experiments that proliferation, migration, and invasion of GC cells have been associated with AP002954.1 and AP000695.1. It may be possible to enhance prognosis assessment and treatment for GC patients through a better understanding of the pathophysiology of immunogenic death. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-344/rc).

Figure 1 Flowchart for the construction and validation of an lncRNAs signature associated with ICD in GC. TCGA, The Cancer Genome Atlas; GC, gastric cancer; lncRNA, long non-coding RNA; ICD, immunogenic cell death; FC, fold change; FDR, false discovery rate; LASSO, least absolute shrinkage, and selection operator; ROC, receiver operating characteristic; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSVA, analysis of gene set variation.

Methods

Data acquisition

On May 1, 2022, the relevant RNA sequencing data, and corresponding clinical characteristics of GC patient samples (including 375 GC tissues and 32 adjacent tissues) were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). By using the “limma” package in R, differentially expressed genes were identified utilizing transcript data from the TCGA_STAD dataset [conditions: false discovery rate (FDR) =0.05 and log₂(fold change) =1.0]. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Recognizing ICD-related lncRNAs in GC

Thirty-two genes associated with immunogenic death were obtained from recent studies and these genes are shown in Table S1 (32). Based on Pearson correlation analysis, 432 lncRNAs associated with ICD were identified from the expression levels of ICD-related genes (based on |Pearson correlation coefficient| >0.4 and P<0.05). Thereafter, 233 differentially expressed lncRNAs associated with ICD were screened according to FDR <0.05 and |log₂(fold change)| ≥1.0.

Development of a prognostic signature for GC based on ICD-related lncRNAs

By combining the expression matrix with clinical data, 337 GC patients with complete survival data were obtained, from which the missing clinical data were censored. There were 236 patients in the training cohort, and 101 patients in the testing cohort. Seventy percent of the sample went through training, and thirty percent went through testing. Identifiers of lncRNAs associated with survival were identified using univariate Cox regression analysis (P<0.05). Subsequently, ICDlncSig for GC were constructed by least absolute shrinkage and selection operator (LASSO) regression models and multivariate Cox regression analysis. Below are the risk scores.

RiskScore=∑i=1nexpi∗βi[1]

Therein, β denotes the coefficient value, and exp the level of lncRNAs expression.

Patients were divided into high-risk and low-risk groups based on the median risk score in the training cohort. Subsequently, using the same approach, patient risk scores for the other cohorts were calculated, and based on the median risk score of the training cohort, two subgroups were formed. Then patients in high- and low-risk cohorts were analyzed using Kaplan-Meier survival analysis. P<0.05 indicates statistical significance.

To finish the evaluation of the specificity and predictive accuracy of risk models based on clinicopathological characteristics, the “timeROC” software package was used to construct time-dependent receiver operating characteristic (ROC) curves.

Gene function enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)

Cellular components (CCs), molecular functions (MFs), and biological processes (BPs) are all included in the GO database. Systematic analyses of gene function in biological pathways are conducted using the KEGG database. In order to analyze functional and pathway enrichment clustering of the differential genes within the prognostic model, the clusterProfiler package in R was used. P>0.05 for FDR was considered a significant difference.

Analysis of gene set variation (GSVA)

In certain gene sets, GSVA scores were obtained for each sample of the TCGA dataset using the GSVA package and the single-sample gene set enrichment analysis (ssGSEA) method (http://www.bioconductor.org) (33). A GSVA score indicates the degree to which a genome is enriched in each sample.

Nomogram construction and calibration

Based on the predictive risk model, the Nomogram was drawn using RMS and a total score was calculated based on the risk score and clinical characteristics. The nomogram visualizes GC patients’ estimated survival probabilities at 1-, 3-, and 5-year using the R package “survival”. As a next step, model diagnostics were performed using the package “survminer”, including the Schoenfeld residuals. To evaluate the prediction accuracy and clinical validity of the model, the area under the ROC was calculated, the calibration analysis was conducted, and the decision curve analysis (DCA) was conducted.

Tumor-infiltrating immune cell analysis

We assessed the level of immune infiltration among two ICDlncSig subgroups using several algorithms (including MCPCOUNTER, TIMER, CIBERSORT, QUANTISEQ, EPIC algorithms, CIBERSORT-ABS, and XCELL) (34). Based on ssGSEA (33), a comparison was made between different immune cells and pathways. The ESTIMATE algorithm (which includes EstimateScores, ImmuneScores, and StromalScores) was used to compare tumor cells with normal cells (35). To provide more possibilities for clinical immunotherapy, we analyzed the relationship between immune checkpoint genes and ICDlncSig.

Forecasting immunotherapeutic responses

The utilization of the tumor immune dysfunction and rejection (TIDE) algorithm enables the assessment of the response of patients with GC to immunotherapy, aiding clinicians in determining the optimal treatment approach. This algorithm, through the analysis of immune dysfunction and rejection within tumors, provides crucial support for personalized medicine (36). The pRRophetic software package (37) is used to calculate the half-maximal inhibitory concentration (IC₅₀, µM) for each sample in order to predict drug sensitivity.

Quantitative real-time PCR analysis (qRT-PCR) of total RNA

Total RNA was isolated from GC cells using FastPure^® Cell/Tissue Total RNA Isolation Kit V2 (Vazyme Biotech Co., Ltd., China), followed by reverse transcription using a reverse transcription kit (Thermo Fisher Scientific, MA, USA). After reversing transcribed RNA from GC cells into cDNA, the diluted cDNA was diluted 10-fold and used for further experiments. In this procedure, PCR was conducted with Q5 (Thermo Fisher Scientific, MA, USA), with denaturation at 95 ℃ for 10 seconds, annealing at 60 ℃ for 30 seconds, and extension at 72 ℃ for 30 seconds. Internal molecular references were all GAPDH rRNAs. Primer sequences are shown in Table S2.

Cell culture and transfection

Gastric epithelial cells (GES-1) along with GC cell lines (AGS, SGC-7901, HGC-27, MKN-45, and MKN-1) were obtained from the Chinese Academy of Sciences located in Shanghai, China. These cells were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium (acquired from Corning, NY, USA), which was supplemented with 10% fetal bovine serum (FBS, sourced from Gibco, Thermo Fisher Scientific) and 1% antibiotic-antimycotic solution (procured from New Cell & Molecular Biotech Co., Ltd., Suzhou, China). The cultures were incubated in a CO₂ incubator that was set to maintain a humid environment with 5% CO₂ at a temperature of 37 ℃. For the transfection of plasmids into the GC cells, Lipo 3000 reagent (sourced from Invitrogen, Carlsbad, CA, USA) was utilized following the manufacturer’s protocols.

Vitro functional assays

To evaluating cell proliferation, cells were seeded at densities of either 3×10³ or 1×10³ evenly across the wells of 96-well or 6-well plates respectively, following a 48-hour period post-transfection. To facilitate the creation of a growth curve, 5 µL of CCK8 solution (provided by Dojindo, Osaka, Japan) was dispensed into each well subsequent to cell adherence. The optical density (OD) was recorded at a wavelength of 450 nm daily over a span of 5 days. After a culture duration of 14 days, cell quantification was carried out utilizing crystal violet to fix and stain the cells situated within 6-well plates.

For the evaluation of cell migration and invasion capabilities, cells were allocated at densities of 5×10⁴ for migration assays and 8×10⁴ for invasion assays, positioning them within the respective chambers of a 24-well plate. The invasion chambers had been pre-coated with Matrigel to facilitate this process. A fixed and stained procedure was employed using crystal violet both 48 hours following cell seeding and again after an additional 48-hour period; subsequent to these steps, randomly chosen fields were captured for photographic documentation.

Statistical analysis

To determine whether the differences were significant, we used log-rank tests based on Kaplan-Meier survival rates. Univariate and multifactor analyses were conducted using the Cox proportional risk model with stepwise regression (LRForward). A nomogram was constructed and validated according to the Iasonos guidelines. As a way of measuring the accuracy of the prognostic model, we used Harrell’s consistency index (c-index) and a time-dependent ROC curve. Statistical significance was determined by P<0.05.

Results

ICDlncSig construction for GC

To identify lncRNAs associated with ICD in GC tissues, Pearson correlation analysis was performed (|Cor| >0.4, P<0.05) on the lncRNA and ICD-related mRNA expression profiles of 375 GC tissues and 32 adjacent tissues in the TCGA database and identified 432 ICD-related lncRNAs, 233 of which were considered to be differentially expressed (Figure 2A,2B). A training cohort (70%) and a testing cohort (30%) were randomly selected from GC samples to determine the clinical significance of ICDlncSig. A univariate Cox regression analysis identified 67 lncRNAs associated with survival (hazard ratio >1, P<0.05) in the training cohort (Table 1). Furthermore, a risk model with nine ICDs was obtained using LASSO Cox regression analysis and based on minimum systolic values. Nine of the lncRNAs were associated with patient survival except for AC116158.1 (Figure S1).

Figure 2 Prognostic risk model construction for GC patients based on ICD-related lncRNAs in the training cohort. (A) An illustration of the expression levels of ICD-related genes in GC tissues and adjacent normal tissues is shown. Red indicates upregulated genes, whereas blue indicates downregulated genes. (B) Volcano plot showing differential expression of ICD-related genes, with red genes indicating upregulation, green genes indicating downregulation, and black genes indicating non-significant expression. (C) Distribution plot of LASSO coefficients for ICD-related lncRNAs, indicating the strength and direction of the association with the outcome. (D) The plot of partial likelihood deviance against log(λ) in the LASSO regression model, showing the selection of the optimal λ value for the best model fit. FC, fold change; FDR, false discovery rate; GC, gastric cancer; ICD, immunogenic cell death; lncRNA, long non-coding RNA; LASSO, least absolute shrinkage, and selection operator.

Prognostic models were constructed using nine lncRNAs associated with ICD and risk scores were obtained: Risk score =1.3530× expression (exp) of AC116158.1 + 1.1381 × exp of AC048382.2 + 0.9209 × exp of AC068790.7 - 0.5092 × exp of LINC00106 + 0.4768 × exp of AP002954.1 − 1.9875 × exp of AC144548.1 − 0.5983 × exp of AC116914.1 + 0.4029 × exp of AL049838.1 + 0.4345 × exp of AP000695.1. In the above equation, six genes (AC116158.1, AC048382.2, AC068790.7, AP002954.1, AL049838.1, AP000695.1) have positive coefficients and are risk genes; the other three genes (LINC00106, AC144548.1, AC116914.1) have negative coefficients and are protection genes (Figure 2C,2D).

Basic validation of ICDlncSig

High-risk and low-risk subgroups of GC patients were formed based on their median risk scores. Six upregulated ICD-lncRNAs and three downregulated ICD-RNAs were present in the high-risk group of GC patients, and their overall survival time was inversely proportional to the risk score (Figure 3A). In addition, Kaplan-Meier curve analysis revealed that low-risk group patients survived longer than high-risk group patients (Figure 3B). Subsequently, using ROC curves to analyze the area under the curve (AUC) values for the ICDlncSig in training cohorts, the 1-, 3-, and 5-year survival rates were 0.766, 0.732, and 0.813, respectively (Figure 3C). In both testing and entire cohorts, the same risk scoring formula is used, resulting in similar results. Similarly, the AUC values of the ICDlncSig in the testing cohort and the entire cohort were analyzed using ROC curves. The 1-, 3-, and 5-year survival rates in the testing cohort were 0.673, 0.604, and 0.504, respectively, and the 1-, 3-, and 5-year survival rates for the entire cohort were 0.741, 0.701, and 0.741, respectively (Figure 3D-3I). As indicated above, prognoses for GC can be predicted with good sensitivity and specificity using the risk model.

Figure 3 An evaluation and validation of ICDlncSig for overall survival in patients with GC. The training cohort (A), testing cohort (D), and entire cohort (G) are shown to illustrate the nine-lncRNA signature risk scores and expression profiles. Compared Kaplan-Meier survival curves of training cohort (B), testing cohort (E), and entire cohort (H) for high- and low-risk individuals. ROC curves of 1-, 3-, and 5-year survival for the training (C), testing (F), and entire cohorts (I). ICDlncSig, ICD-related lncRNAs signature; AUC, area under the curve; GC, gastric cancer; lncRNA, long non-coding RNA; ROC, receiver operating characteristic.

ICDlncSig and clinicopathologic characteristics in GC: correlation analysis

Using the entire cohort, we performed univariate and multivariate Cox regression analyses to determine if ICDlncSig is an independent predictor of GC. Based on univariate Cox regression analysis, the overall survival of GC patients was strongly influenced by the patient’s age, tumor stage, and ICDlncSig (Table 2). Using multivariate Cox regression analysis, ICDlncSig was identified as an independent prognostic indicator for GC patients (Table 2). Afterwards, we analyzed the correlation between ICDlncSig and clinicopathological characteristics, as shown in Figure 4A, with significant differences in age, histological grade, and tumor T-stage between patients in the high- and low-risk groups. In addition, the AUC values of ICDlncSig risk scores were found to be 0.741, 0.701, and 0.741 for 1-, 3-, and 5-year, respectively, as assessed by ROC curves (Figure 4B). Prognostic analysis of the entire cohort by clinical stratification based on different clinical-pathological correlation factors revealed that the prognosis of GC patients with high-risk label was worse in all clinical stratification subgroups except the M1 stage (Figure 4C). Based on the clinical trait composition ratios of each subgroup. Stage M1 patients with high-risk and low-risk labels showed no significant difference in prognosis. The above-mentioned reasons may be due to the low number of patients with stage M1 GC (Figure 4D). These findings indicate that ICDlncSig is independent of other clinicopathological factors in predicting prognosis in patients with GC.

Figure 4 The association between GC risk score and clinicopathological characteristics in the entire cohort. (A) The strip chart shows the relationship between clinical characteristics (age, gender, histological grade, tumor stage, T stage, N stage, and M stage) and the ICDlncSig risk score. (B) Based on various prognostic indicators in GC samples, including age, gender, grade, and stage, ROC analysis was conducted for 1-, 3-, and 5-year overall survival predictions. (C) The calculation of clinical stratification of overall survival for high- and low-risk groups based on age (>65), gender, histological grade (G1–2 and G3), and tumor stage (I–II, III–IV, T1–2, T3–4, N0–1, N2–3, M0 and M1). (D) Based on age, gender, histology grade, and stage of tumor, proportional analysis is performed of high- and low-risk groups. *, P<0.05; **, P<0.01. AUC, area under the curve; GC, gastric cancer; ROC, receiver operating characteristic.

Pathways and functions associated with the ICDlncSig

To further explore the functions performed by ICDlncSig and the pathways involved, enrichment analyses were performed. GO enrichment analysis showed that results indicated that these genes were most significantly enriched in collagen-containing extracellular matrix, contractile fiber (CC processes), muscle system process, muscle contraction (BP processes), and actin binding, glycosaminoglycan binding (MF processes) (Figure 5A and available online: https://cdn.amegroups.cn/static/public/tcr-24-344-1.docx). Additionally, KEGG pathway enrichment demonstrated these genes were significantly enriched in Wnt, cGMP-PKG, and Hedgehog pathways (Figure 5B and Table S3). The above results demonstrated that the screened differential genes might be correlated to the promoting progress of GC. In addition, further GSVA pathway enrichment analysis of nine lncRNAs revealed that 26 pathways, including UV_RESPONSE_DN, TNFA_SIGNALING_VIA_NFK, TGF_BETA_SIGNALING, PANCREAS_BETA_CELLS, were positively correlated with risk score. UNFOLDED_PROTEIN_RESPONSE, SPERMATOGENESIS, OXIDATIVE_PHOSPHORYLATION, MYC_TARGETS_V2, and 10 other pathways were negatively correlated with risk score (Figure 5C). Activation of these markers may be involved in the process of tumor progression. This may lead to poor survival rates for GC patients.

Figure 5 ICDlncSig pathway enrichment analysis. (A) The GO enrichment analysis of ICDlncSig provides information about its biological processes, cellular components, and molecular functions. (B) KEGG pathway enrichment analysis of ICDlncSig, illustrating the signaling and metabolic pathways significantly enriched in the ICDlncSig genes. (C) GSVA enrichment analysis of ICDlncSig, showing the correlation of ICDlncSig genes with hallmark gene sets. The heatmap displays significant correlations, with positive correlations in red and negative correlations in green. Statistical significance is indicated by asterisks (*, P<0.05; **, P<0.01; ***, P<0.001). GO, Gene Ontology; ICDlncSig, ICD-related lncRNAs signature; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSVA, analysis of gene set variation.

Prognostic nomogram construction and evaluation

This risk model was applied clinically by developing a nomogram that included several clinicopathological factors, including the ICDlncSig risk score, to predict overall survival for all patients with GC over 1, 3, and 5 years (Figure 6A). According to Schoenfeld’s residuals test, our Cox model meets proportional hazards (Figure 6B). In the ROC curve analysis, the model predicted the 1-, 3-, and 5-year survival of GC patients with AUC values of 0.726, 0.754, and 0.727, respectively (Figure 6C), and its c-index =0.7032, indicating that The nomogram has better discrimination ability. Subsequently, we similarly established calibration curves for nomogram and found that the survival predicted by nomogram for GC patients correlated closely with the actual survival outcomes (Figure 6D). Finally, DCA was used to evaluate the clinical effectiveness of the model. The DCA curve of the nomogram is shown in Figure 6E. The nomogram is generally associated with high clinical net benefit rates, according to our data. Based on these studies, the nomogram is more accurate than individual diagnostic features in predicting survival among GC patients.

Figure 6 The development and evaluation of a nomogram that incorporates clinicopathological characteristics and risk signatures. (A) An integrated nomogram integrating ICDlncSigs and clinicopathological traits for the prognosis of patients with GC. Each predictor variable (age, stage, and risk score) is assigned a point value, which are summed to obtain a total score indicating the probability of overall survival at different time points (1, 3, and 5 years). (B) Results of Schoenfeld residual tests showing that age, stage, and risk score all satisfy the proportional hazards assumption. (C) ROC curves, with respective AUC values, for the nomogram predicting 1-, 3-, and 5-year overall survival in GC patients. (D) Comparison of predicted vs. observed overall survival probabilities at 1, 3, and 5 years for the nomogram. (E) DCA comparing the net benefit of the nomogram with clinical parameters alone, the risk signature alone, and all predictors combined. **, P<0.01; ***, P<0.001. ICDlncSig, ICD-related lncRNAs signature; AUC, area under the curve; OS, overall survival; GC, gastric cancer; ROC, receiver operating characteristic; DCA, decision curve analysis.

Infiltration of immune cells by ICDlncSig models

Several algorithms were used to compare immune infiltration between high-risk and low-risk groups. Figure 7A shows the bubbles of all apparently different immune response graphs. Comparative analysis of immune cell subsets showed that immune cells [including immature DCs (IDCs), Mast cells, activated DCs (aDCs), neutrophils, B cells, plasmacytoid DCs (pDCs), CD8⁺ T cells, DCs, macrophages, natural killer (NK) cells, T helper cells, Treg, Tfh, and tumor-infiltrating lymphocyte (TIL)] and immune function [chemokine receptor (CCR), check-point, antigen presenting cell (APC) co-inhibition, T cell co-stimulation, APC co-stimulation, human leukocyte antigen (HLA), parainflammation, inflammation-promoting, type II IFN response, T cell co-inhibition, type I IFN response] had significantly different infiltration levels (Figure 7B,7C). As a result of further analysis, all immune cells with differential immune infiltration and function scored higher in the high-risk subgroup. Both groups were also evaluated for their tumor microenvironment and characteristics such as EstimateScore, ImmuneScore, and StromalScore. Then the results showed patients who were at high risk for GC had higher immune, stromal, and estimate scores (Figure 7D). Based on these results, ICDlncSig may be associated to some extent with tumor-infiltrating immune cells in GC.

Figure 7 Analyses of immune infiltration. (A) A correlation plot shows significant differences among high- and low-risk groups in immune cell populations using algorithms such as TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, EPIC, and XCELL. (B) Box plots comparing immune cell scores between high- and low-risk groups using the ssGSEA algorithm. (C) Using ssGSEA algorithms, box plots showing the difference between high- and low-risk groups for immune function scores. (D) A box plot comparing the stromal scores, immune scores, and estimated scores between high- and low-risk groups of patients with GC. *, P<0.05; **, P<0.01; ***, P<0.001. GC, gastric cancer; TIL, tumor-infiltrating lymphocyte; aDCs, activated DCs; NK, natural killer; APC, antigen presenting cell; CCR, chemokine receptor; HLA, human leukocyte antigen.

The therapeutic effect of the ICDlncSig model

It was found that immune checkpoints play a crucial role in immunotherapy. Therefore, according to our analysis of expression profiles in both groups, high-risk individuals expressed highly expressed immune checkpoint genes (TNFSF14, CD28, CD27, CD276, IDO2, TNFSF18, CD40LG, BTLA, LAIR1, TNFRSF4, NRP1, CD86, TIGIT, TNFRSF8, TNFSF4, CD200, CD48, PDCD1LG2, CD200R1, HAVCR2), except for TNFRSF25 (Figure 8A). Following that, a relationship between ICDlncSig and immune checkpoint genes was examined. A positive correlation exists between the ICDlncSig risk score and 14 immune checkpoint genes (TNFSF4, TNFSF14, PDCD1LG2, NRP1, LAIR1, HAVCR2, CD86, CD48, CD40LG, CD28, CD27, CD200R1, CD200, BTLA) (P<0.001, Figure 8B). Additionally, the TIDE algorithm was used to evaluate the ability of ICDlncSig to predict the benefit of immunotherapy. TIDE scores differed significantly between patients with different risk labels, indicating that low-risk patients would benefit more from immunotherapy than high-risk patients (Figure 8C). As a result, we assessed the sensitivity of different groups of patients to six commonly used chemotherapy drugs (dasatinib, clobazam, methotrexate, bortezomib, sunitinib, and imatinib), and by comparing the IC₅₀ values of the drugs, we found that patients with high-risk GC had lower IC₅₀ (µM) for dasatinib (P=2×10⁻⁹), clobazam (P=1.8×10⁻⁴), bortezomib (P=1.8×10⁻⁴), sunitinib (P=6.9×10⁻⁹) and imatinib (P=7.6×10⁻⁶) had lower IC₅₀ values (Figure 8D). There may be a greater sensitivity to these five drugs in high-risk patients. For methotrexate (P=3×10⁻⁴), the IC₅₀ value was higher, indicating that patients with high risk were less sensitive to methotrexate drugs (Figure 8D).

Figure 8 Prediction of immunotherapy and chemotherapy response. (A) A box plot showing the expression of immune checkpoint genes in high-risk versus low-risk individuals. (B) A heatmap illustrating the correlation between ICDlncSig score and ICD-related lncRNAs and immune checkpoint genes, with red indicating positive correlations and blue indicating negative correlations. (C) A violin plot comparing TIDE prediction scores (left) and predicted immunotherapy responses (right) between high- and low-risk groups in the TCGA_STAD dataset. (D) Box plots comparing the sensitivity to various chemotherapeutic agents (dasatinib, cyclopamine, methotrexate, bortezomib, sunitinib, and imatinib) between high- and low-risk groups. *, P<0.05; **, P<0.01; ***, P<0.001; ns, no significance; ICD, immunogenic cell death; ICDlncSig, ICD-related lncRNAs signature; lncRNA, long non-coding RNA; TIDE, tumor immune dysfunction and rejection; TCGA, The Cancer Genome Atlas; STAD, stomach adenocarcinoma.

Correlation of ICDlncSig with tumor mutation burden (TMB)

Several studies have found an association between TMB and immunotherapy response (38,39). Thus, we assessed how somatic genomic mutations differed among different groups of ICDlncSig (Figure 9A, Figure S2). The top twenty genes with mutation rates exceeding 10% were identified based on both scoring subgroups. The quantitative TMB analysis revealed significant differences between groups with high and low risks (P=2.1×10⁻⁴, Figure 9B). ICDlncSig scores and TMB showed a negative correlation (R=−0.28, P=1.5×10⁻⁷, Figure 9C). After determining the optimal cutoff value for TMB (2.394), we divided the patients into groups based on their TMB levels. Compared with patients with high TMB levels, patients with low TMB levels had significantly worse survival prospects (Figure 9D). Prognostic stratification was also evaluated by combining TMB and ICDlncSig subgroups. Subsequently, we stratified the data and performed survival analysis and found that the high or low TMB did not affect the assessment of prognosis based on the ICDlncSig group (Figure 9E). In summary, ICDlncSig can make some predictions of response to immunotherapy, and this ability correlates with TMB.

Figure 9 The relationship between ICDlncSig and somatic mutations. (A) A waterfall plot showing the top 20 most frequently mutated genes within high- and low-risk groups. (B) Box plot illustrating the differences in TMB between high- and low-risk groups. (C) The scatter plot shows the positive relationship between the ICDlncSig score and the TMB score. (D) A comparison of Kaplan-Meier survival curves for the entire cohort between high- and low-TMB groups. (E) A Kaplan-Meier survival curve displaying patient survival stratified by TMB and ICDlncSig scores. ICDlncSig, ICD-related lncRNAs signature; TMB, tumor mutation burden; H, high; L, low.

An evaluation of lncRNAs in GC samples

A comparison of the expression levels of nine lncRNAs in GC tissues and cell lines is shown in Figure S3 and Figure 10A. We found that although the nine genes showed different upregulation or downregulation in GC tissues and cells, only AP002954.1 and AP000695.1 exhibited consistent upregulation in both GC tissues and all GC cells. Therefore, we explored the effects of AP002954.1 and AP000695.1 on GC cells. We found that successful knockdown of AP002954.1 and AP000695.1 resulted in varying degrees of decrease in the proliferation rate and quantity of GC cells (Figure 10B-10D). Additionally, transwell experiments also demonstrated a corresponding reduction in the number of migrated and invasive GC cells after knockdown of AP002954.1 and AP000695.1 (Figure 10E,10F). AP002954.1 and AP000695.1, two cancer genes found in the model, are believed to promote GC cell proliferation, migration, and invasion.

Figure 10 AP002954.1 and AP000695.1 can serve as key oncogenes for GC cells. (A) The expression levels of nine lncRNAs in GC tissues. (B) After knocking down AP002954.1 or AP000695.1, the expression levels of both in GC cells. (C) CCK8 experiment to assess the impact of AP002954.1 and AP000695.1 on the proliferation rate of GC cells. (D) Colony formation assay to assess the impact of AP002954.1 and AP000695.1 on the proliferation quantity of GC cells. Fix the cell colonies with paraformaldehyde for 30 minutes, then stain the cell colonies with crystal violet for 10 minutes and take photographs. Magnification: 1×. (E,F) Transwell migration and invasion assays to investigate the impact of AP002954.1 and AP000695.1 on the metastasis of GC cells, magnification is 100×. Stain the cells with crystal violet for 10 minutes and take photographs of different fields of view under the microscope. **, P<0.01; ***, P<0.001; ****, P<0.0001. NC, negative control; OD, optical density; CCK8, Cell Counting Kit 8; GC, gastric cancer; ns, no significance; lncRNA, long noncoding RNAs.

Discussion

In recent research, tumor immunotherapy, which activates the immune system to combat tumor cells, has demonstrated promising capabilities in fostering specific anti-tumor immunity and long-term immune memory (40,41). However, the immunosuppressive nature of the tumor microenvironment renders most patients insensitive to such treatments, thus limiting the effectiveness of immunotherapy. To overcome this obstacle, the induction of ICD has been proposed as an effective strategy. ICD can activate the immune system by releasing DAMPs and tumor-associated antigens, transforming immunosuppressive “cold” tumors into immune-active “hot” tumors. This conversion enhances the immunological microenvironment and promotes adaptive anti-tumor immune responses (42,43).

In therapeutic approaches that achieve ICD, modalities such as chemotherapy, targeted therapy, oncolytic viruses, photodynamic therapy (PDT), photothermal therapy (PTT), and radiation therapy have all been shown to possess the capability to induce ICD (44-46). Notably, when these treatments are integrated with nanotechnology-based drug delivery systems, they can effectively enhance therapeutic efficacy and exhibit remarkable outcomes in tumor treatment (40,47-49). Moreover, lncRNAs play intricate roles in the development and progression of tumors, influencing tumor behavior through the regulation of cellular proliferation, apoptosis, and necroptosis (50,51). Recent studies suggest that certain lncRNAs may play a pivotal role in tumor immune evasion by affecting ICD regulation. Furthermore, prognostic models based on lncRNAs signatures for patients with GC have been reported (52-54). However, due to GC’s high heterogeneity and the variability in analytical strategies employed in these studies, the predictive accuracy of these models requires improvement. Hence, researchers are tasked with further exploring lncRNAs features associated with ICD to enhance the accuracy of prognostic predictions and devise more personalized therapeutic strategies. This effort could not only provide new perspectives for the treatment of GC but could also serve as a reference for the therapy of other tumor types, especially in scenarios involving the combined use of ICD-inducing treatments and immunotherapy. Such integrative research endeavors promise to unveil critical molecular and cellular mechanisms underlying tumor immunology, thereby propelling the innovation of cancer treatment modalities.

Using nine ICD-related lncRNAs, we derived a prognostic signature for GC that could serve as an independent predictive factor. The ICDlncSig can thus be used to predict the survival of GC patients as a prognostic model. In three datasets the ICDlncSig model showed moderate predictive power. Furthermore, the newly developed nomogram can be used to intuitively predict the GC patients’ survival and improve clinical decision-making. Nine lncRNAs were identified as being associated with poor prognosis, including AC116158.1, AC048382.2, AC068790.7, AP0029541, AL049838.1, AP000695.1, LINC00106, AC144548.1 and AC116914.1. These nine lncRNAs have been included in previous studies to predict the prognosis of GC patients. For instance, Wang et al. constructed an lncRNA signature related to autophagy, which includes AC068790.7 and LINC00106, as effective prognostic indicators. This signature effectively guides immunotherapy and precision treatment for patients with GC (55). Zhang et al. constructed an inflammation-related lncRNAs signature, which includes ten lncRNAs such as AP000695.1. With an AUC of 0.788, this signature substantiates its utility in predicting the prognosis of GC (56). Wu et al. constructed a risk score model for lncRNAs that predicted the prognosis of GC patients, which contained LINC00106 in the lncRNA and was more accurate for patients with stage II–IV (57). All these studies suggest that it is more likely that the above molecules have a role in regulating the biological function of GC.

In this study, we investigated the molecular mechanisms by which ICDlncSig participates in GC by GO, KEGG, and GSVA pathway enrichment analysis and showed that TGF_BETA_SIGNALING, PANCREAS_BETA_CELLS and pathways, which was common in cancer, might play a vital role in the poor prognoses of GC patients who were labeled with high risk. Previous study has shown that LINC00106 is lowly expressed in thyroid patients, and it attenuates the migration and invasive ability of TC by inhibiting EMT (58). A comprehensive understanding of the important interactions between ICDlncSig and tumor immunomodulation is therefore crucial for the development of clinical immunotherapy. ImmuneScore, StromalScore, immune cell subpopulations, and immune checkpoints are suitable tools for characterizing the immune microenvironment of GC. As a result of immunosuppression, tumor cells can evade immune surveillance, promoting their survival and progression (59,60). According to the results of our analysis, GC patients with high-risk status had higher levels of immune cell infiltration than those with low-risk status. The findings also suggest that ICDlncSig can be viewed as a novel immune indicator of GC in the high-risk group. The clinical decision-making surrounding cancer treatment has also been improved by immune checkpoint inhibitors (61,62). An extensive list of immune checkpoint genes was upregulated in patients with renal clear cell carcinoma with a high risk, according to Bai et al. (63). Wang et al. also found that high-risk GC patients expressed higher levels of PD-L1, PD-1, and PDCD1LG2 (55). As in previous studies, ICDlncSig scores were positively correlated with the levels of immunoglobulin-like growth factor receptor genes (TNFSF4, TNFSF14, PDCD1LG2, CD28, CD27). The key finding of our study is that high-risk GC patients exhibited increased sensitivity to dasatinib, clobazam, bortezomib, sunitinib, and imatinib, which demonstrated significant anti-tumor activity in our research. This provides crucial insights for further exploration of their potential application in GC treatment. Our ICDlncSig model may assist clinicians in predicting the likelihood of patients responding to immunotherapy, thereby allowing for more personalized treatments.

In spite of the fact that ICDlncSig shows promise, the current study has a number of limitations. As noted, we validated our signature mainly with the TCGA database, and external validation of ICDlncSig needs to be done with a different database and large multicenter cohort. In addition, we need to explore the key roles of these nine ICD-related lncRNAs in GC to further improve the later clinical treatment modalities.

Conclusions

In summary, the ICD-related lncRNAs signature we constructed can effectively predict the prognosis of GC patients and may become a clinical guidance tool in the future. Moreover, the key lncRNAs in the model, AP002954.1 and AP000695.1, can regulate the proliferation, migration, and invasion of GC cells, and may serve as therapeutic targets for GC patients.

Acknowledgments

Funding: This project was supported by the Project of Nantong Science and Technology Bureau (No. MS20222047) and Zhenjiang City Science and Technology Bureau project (No. FZ2023005).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-344/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-344/dss

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-344/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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