The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma

doi:10.21203/rs.3.rs-1419868/v2

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Research Article

The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma

https://doi.org/10.21203/rs.3.rs-1419868/v2

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Purpose

Necroptosis played an important role in the tumorigenesis, development, metastasis, and drug resistance of malignant tumors. This study explored the new model for assessing stomach adenocarcinoma (STAD) prognosis and immunotherapy by combining long noncoding RNAs associated with necroptosis.

Methods

Clinical data and gene expression information of STAD patients were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the molecular marker database. Perl software and R software were used for data processing and analysis. The correlation test of the R language was used to obtain necrotizing apoptosis-related lncRNAs in STAD. Combined with clinical data, univariate Cox regression and multivariate regression analysis were used to screen out immune-associated lncRNAs associated with STAD prognosis and construct the risk score model. The Kaplan-Meier (K-M) survival analysis and independent prognostic factor assessment were used to evaluate the model, and the model was combined with other clinical factors to construct the nomogram to predict the survival rate of individuals. Immune-related and drug sensitivity analysis of necroptosis-related lncRNA tumor clusters using TIMER2.0, GSVA, GSEA, PCA, and GDSC.

Results

Cox regression analysis determined that 21 necroptotic-related lncRNAs were used to construct the risk score model. With the median risk score as the cut-off, patients can be divided into high-risk and low-risk groups. The survival curve of the two groups of patients indicated a better prognosis for low-risk patients. In addition, the risk scoring model can be used as an independent prognostic factor for STAD. Combined with other clinical factors and risk scores of STAD, the nomogram was established to predict the survival rate of individuals, and the calibration plot showed that the predicted values were in good agreement with the actual observations. Through the single sample GSEA (ssGSEA) difference analysis, it was found that most immune cells had differences in the high-risk group and the low-risk group, whereas patients in the high-risk group had higher immune infiltration. Tumor microenvironment (TME) analysis showed that cluster-C3 had the highest score in the analysis of the three TMEs. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters. Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters.

Conclusions

By quantitatively evaluating the risk score of tumor clusters, the accuracy of STAD immunotherapy can be better predicted as a single drug or combination therapy. This study provided a new idea and basis for necrotizing apoptosis-related lncRNAs to achieve more efficient clinical translation and accurately guide immunotherapy in STAD patients.

necroptosis

stomach adenocarcinoma

prognostic signature

immune infiltration

drug sensitivities

Stomach adenocarcinoma (STAD) was one of the most common malignancies worldwide and has become a global health problem. In the 2012 World Cancer Statistics, the number of new cases of STAD was 951,000, which was 6.8% of the number of malignant tumors in the same period, accounting for the fifth place for malignancies[1]. Surgical resection was the mainstay of treatment for advanced STAD, but the 5-year survival rate after surgery was still low. On the basis of adjuvant therapy, the 5-year survival rate was about 20–25%[2]. In recent years, immunotherapy has become one of the most promising strategies in cancer treatment, showing outstanding therapeutic efficacy in tumors such as melanoma, non-small cell lung cancer, and kidney cancer[3–5]. Immunotherapy research on STAD has also become a research hotspot, and a variety of immunotherapy strategies have been developed, including immune checkpoint inhibitors, cellular adoptive immunotherapy, cancer vaccines, etc[6]. These immunotherapy methods treated tumors by increasing the ability of the patient's immune system to respond to tumors or the immunogenicity of the tumors[7]. However, many new immunotherapies were still in the early stages of clinical research. Therefore, it was of great significance to study the mechanism of immunotherapy in STAD, as well as the immune, molecular and genetic characteristics of STAD patients.

Apoptosis was the earliest discovery of programmed cell death, first proposed in 1972 by Kerr JF et al[8]. In 2005, American scholars Degterev found that receptor-interacting protein kinase 3 (RIPK3) activates mixed lineage kinase domain-like protein (MLKL) leading to cell membrane rupture of the cell membrane in the form of cell necrosis, called necroptosis[9]. Subsequently, a variety of programmed cell death forms such as pyroptosis, ferroptosis, and autophagy, were discovered. Necroptosis had a regular regulatory mechanism, which was a necrotic form of cell death that occurs when the apoptosis pathway was inhibited. With the deepening of basic research, it had been found that necroptosis not only involved the inflammatory pathological mechanism but was also closely related to the occurrence and development of tumors and the mechanism of drug resistance. In addition, necroptosis may play an important role in triggering immunogenicity and promoting natural anti-cancer immune surveillance[10–12]. It was precise because necroptosis was involved in the occurrence and development of many diseases, it was urgent for us to explore the role of necroptosis in the development of STAD, and provide new treatment strategies for STAD.

Some studies had shown that the occurrence and development of STAD were accompanied by a variety of dysregulation of the long non-coding RNAs (lncRNAs)[13, 14]. Abnormally expressed lncRNAs can eventually affect epigenetics leading to the formation of malignant tumor phenotypes. Early intervention in the proliferation, infiltration, and metastasis of tumor cells can provide a potential target for the treatment of STAD. Therefore, identifying the biological behavior of lncRNAs can provide a basis for the diagnosis and treatment of STAD. The lncRNAs can alter the activity of multiple signaling pathways by regulating target gene expression, such as Wnt/β-catenin[15, 16], PI3K-AKT/mTOR[17, 18], JAK/STAT[19], NF-κ B[20], thereby promoting or inhibiting tumor cell activity. LncRNAs can also regulate tumor cell apoptosis. MCL-1 belonged to the anti-apoptotic Bcl-2 gene family, and lncRNA MYOSLID promoted the expression of MCL-1 by adsorbing miR-29c-3p in STAD[21]. Abnormal expression of LncRNA MEG3 promoted apoptosis and regulated the expression of p53 in gastric cancer cell lines[22]. The role of lncRNA in tumors had only been preliminarily recognized in recent years, and the specific regulatory mechanism in STAD needed to be further studied. Therefore, fully understanding the lncRNAs regulatory network associated with necroptosis will help deepen the understanding of the mechanism of STAD and can provide new ideas and methods for the clinical immunotherapy of STAD.

The in-depth study of necroptosis was not only conducive to deepening the understanding and understanding of the way cells die but also helped to study the development and variation of different disease models. In particular, the relationship between necroptosis and the occurrence, development, and outcome of tumors will continue to become a research hotspot for a long time in the future. The in-depth study of the role of necroptosis in tumor pathogenesis will advance the development of new therapeutic targets. Currently, there was less research on necroptosis-related lncRNAs in STAD. In this study, we will study the expression and prognostic significance of necroptosis-related lncRNAs in STAD, laying a theoretical foundation for elucidating the pathogenesis of STAD, and providing valuable clues and means for developing drugs with related molecular targets.

Collection and processing of clinical data

STAD-related RNA sequences (RNA-seq) and clinical data were downloaded from the TCGA (https://portal.gdc.cancer.gov/) database. The downloaded dataset consisted of 375 tumor samples and 32 normal samples. Perl software was used for data integration, as well as the extraction of necrotizing apoptosis gene expression data, lncRNA expression data, and corresponding clinical data. To make gene expression comparable between samples, the data was converted into TPM values. This made the data more similar to the transcript samples produced by the microarray more comparable between samples. Among them, the gene expression data extraction project included gene name, sample number, and expression value. Clinical data included patient number, survival time, survival status, age, gender, and TNM staging.

Selection of Necroptosis-Related Genes and lncRNAs

According to necroptosis-related studies, 67 necroptosis-related genes were obtained, and the expression of necroptosis genes was extracted by using the "limma" package in R (Supplemental Table 1). Through | Log₂ fold change (FC)|> 1 and p < 0.05, abnormally expressed lncRNAs were screened. R software was used to correlate necroptosis-related genes and abnormally expressed lncRNAs to screen out necroptotic-related lncRNAs.

Establishment and verification of risk prognostic model

Prognostic models were established using necroptosis-associated lncRNAs and corresponding STAD clinical data. First, the relationship between necroptosis-related lncRNAs expression levels and overall survival (OS) was assessed using univariate Cox regression analysis. Based on P < 0.01, necroptosis-related lncRNAs related to STAD prognosis were screened. Subsequently, the screened necroptosis-related lncRNAs were incorporated into the multivariate Cox regression analysis, and the necroptosis-related lncRNAs that ultimately constituted the riskScore model were screened out according to the optimal Akaike information criterion (AIC) simulation criteria. The model formula was as follows[23]:

Risk score =\({\sum }_{i=1}^{N}（\text{E}\text{i}\text{*}\text{C}\text{i}）\)

The N was the number of necroptosis-related lncRNAs in which the riskScore model was constructed, the Ci was the necroptosis-related lncRNA coefficient, and the Ei was the expression level of necroptosis-related lncRNAs.

Based on the risk scoring model, each patient received a corresponding risk score. With the median risk score as the cut-off, patients can be divided into high-risk and low-risk groups. The Kaplan-Meier (K-M) method was used to analyze the survival of high- and low-risk groups, and the difference in total survival time was analyzed by log-rank test. To further evaluate this model, risk scores and other clinical features were analyzed for univariate and multifactor Cox regression to examine whether they were independent prognostic factors.

Construction of nomogram

The variables used to construct the nomogram of STAD include age, gender, TNM staging, and risk score. Plot calibration curves to assess consistency between actual and predicted survival. In the nomogram, the corresponding point was found on the corresponding axis according to the patient's variables. A vertical line was drawn over this point, and the value of the point that intersects the score axis was the fraction of the variable. The sum of the fractions for each variable was the total score. In the same way, the value of the total score was read on the survival rate axis, which was the probability that the patient will survive in the corresponding time[24].

Gene set enrichment analysis (GSEA)

GSEA 4.0.3 was used to analyze the effects of genome-wide expression changes on various biological functions and pathways in patients in the high and low-risk groups. GSEA was used as reference "c5: gene ontology (GO) gene sets" (c5.all.v7.0.sym-bols.gmt) and "c2: curated gene sets" (c2.cp.kegg.v7.0.symbols.gmt) from the molecular signatures database (MSigDB). The number of permutations was set to repeat 1,000 times. P < 0.05 and false discovery rate (FDR) less than 0.05 gene set as significantly enriched gene sets.

Immune-related and drug-sensitivity analysis

TIMER2.0 was used to assess the correlation between the expression of necroptosis-lncRNAs and the richness of tumor⁃infiltrating immune cells (TIIC), including CD4 + T cells, CD8 + T cells, macrophages, etc. In addition, CIBERSORT, a deconvolution algorithm based on gene expression, was used to assess relative changes in gene expression between groups[25]. Data on tumor immune-related characteristics of STAD samples were obtained from the National Cancer Institute Genomic Data Commons. The 375 tumor samples were divided into high and low-risk groups with the median risk score as the cut-off value to analyze and assess the effect of necroptosis-related lncRNAs on the immune system microenvironment. The "ggpubr" in the R package analyzed differences in tumor microenvironment (TME) scores and immune checkpoints between the low- and high-risk groups.

Gene set variation analysis (GSVA) was a non-parametric unsupervised [9] analysis method for assessing gene collection enrichment in the transcriptome. GSVA determined the biological function of the sample by comprehensively scoring the gene set of interest, turning the gene level change into a pathway level change. This study was taken from the Molecular Signatures Database (v7.0) (http://software.broad-institute. org/gsea/msigdb) downloads the gene set and used the GSVA algorithm to comprehensively score each gene set to assess potential biological functional changes in different samples.

For the calculation of the TME score, there were currently methods of mean-weighted coefficient GSEA and principal component analysis (PCA). To comprehensively assess the merits and demerits of the various scores, we integrated the three most commonly used methods for single-sample gene collection enrichment analysis GSEA, principal component analysis PCA, and T-distributed stochastic neighbor embedding (t-SNE). The K-M method was used to plot the survival curves of each group.

The K-Medoide was performed using the consensus clustering method of the "ConsensusClusterPlus" package. The cluster value k was 2 to 9, and the k value with higher cluster stability was selected according to the clustering effect[26]. The half-maximal inhibitory concentration (IC50) was used by Genomics of Drug Sensitivity in Cancer (GDSC) to detect drug reactions. The "ggpubr" in the GSVA and R software packages performed immunoassays and drug sensitivity testing for tumor typing.

Statistical analysis

R 3.6.2 Software was used for statistical analysis. The survival receiver operating characteristic (ROC) area under the curve (AUC) was calculated through the R software package, and clinical parameter differences were examined using an independent t-test. Qualitative data were compared using the Kruskal-Wallis H test. Differences in the proportion of infiltrated immune cells in the samples were analyzed using the log-rank test. The Pearson analyzed the correlation between the degrees of infiltration of various immune cells. P < 0.05 was statistically significant.

Necroptosis-related lncRNAs in STAD

We obtained gene expression data from STAD patients from TCGA, including 375 tumor samples and 32 normal samples. Figure 1A shows all lncRNAs differential expressions associated with necroptosis in STAD. Based on the analysis of the expression levels of 67 necroptosis-related genes, a total of 472 abnormally expressed lncRNAs were analyzed, of which 379 lncRNAs were up-regulated and 93 lncRNAs were down-regulated. Figure 1B showed the analytical relationship network diagram between necroptosis-related genes and lncRNA. The heat maps were plotted based on the differential gene expression of the first 50 lncRNAs with the most significant upregulation and downregulation (Fig. 1C).

Construct the risk prognostic model

Based on the expression of lncRNAs, we constructed a patient risk model, namely the train group and the test group. Through univariate Cox regression analysis, a total of 21 lncRNAs associated with necroptosis were found to have a significant OS correlation (P < 0.05). The relevant results were plotted as forest maps and heat maps (Figs. 2A, 2B). The Lasso regression was performed on two groups of patients, and 9 lncRNAs associated with necroptosis in STAD were obtained after selecting the lambda (λ) values with the smallest cross-validation error (Figs. 2C, 2D). As can be seen from the 21 prognostically correlated lncRNAs Sankey diagram, 19 lncRNAs were positively correlated with necroptosis and 2 were negatively correlated with necroptosis (Fig. 2E). After risk assessment of both groups of patients, they were divided into high-risk groups and low-risk groups according to the median risk score as a cut-off (Supplemental Fig. 1). The survival curve of the two groups of patients indicated a better prognosis for low-risk patients (Figs. 2F-H).

Assessment of the risk prognostic model associated with necroptosis-lncRNAs

Based on patient clinical characteristics and risk levels, we performed univariate and multifactorial Cox regression analyses of necrotizing necroptosis-related lncRNAs and OS. It was found that age, Stage, and risk score were independent prognostically relevant parameters (Figs. 3A, 3B). This suggested that the riseScore constructed was reliable as a prognostic model for STAD patients. Using the ROC curve, we analyzed the sensitivity and specificity of the prognostic model in patient survival time as well as prognosis-related parameters (Figs. 3C, 3D). The results showed that the 1-year, 3-year, and 5-year AUC was greater than 0.7, indicating that the constructed model had higher accuracy in predicting the survival of patients at 1, 3, and 5 years. The risk score in the prognosis-related parameters of the patient had the maximum AUC value. This suggested that risk score was used to predict patient prognosis better than other clinical features with higher accuracy. Patients were divided into early stages (stage I, II) and advanced stages (stage III, IV) according to the pathological stage of the patient. Model validation for clinical groupings showed that risk scores can be applied to STAD patients with different tumor stages (Figs. 3E, 3F). Based on independent prognostic factors including age, gender, risk score, and TNM staging, we built a nomogram. As shown in Fig. 3G, the patient (female, stage I, T₂N₀M₀, G₃, and low-risk) received a predictive score of 424. The probability of survival of this patient greater than 1 year can be seen by total points as 0.89 (Fig. 3G). It can be seen from the calibration curve that the nomogram predicts the survival time of patients with high accuracy (Fig. 3H).

Immune-related analysis of risk score of necroptosis-related lncRNAs

The GSEA was used to analyze the KEGG pathway in the high-risk and low-risk groups to explore differences in biological function. As shown in Fig. 4A, 5 pathways were related to the high-risk group and 5 pathways were related to the low-risk group. Most of the pathways of enrichment were highly correlated with tumor invasion and immunity. It can be seen from the bubble plots analyzed on different software platforms that most of the immune cells were positively correlated with the high-risk group (Fig. 4B). Through the single sample GSEA (ssGSEA) difference analysis, it was found that most immune cells had differences in the high-risk group and the low-risk group, whereas patients in the high-risk group had higher immune infiltration (Fig. 4C). The same results can be obtained in immune-related functions (Fig. 4D). Through the analysis of TME differences, we found that StromalScore, ImmuneScore, and ESTIMATEScore were higher in the high-risk group than those in the low-risk group (Figs. 4E, 4F, and 4G). Immune checkpoint inhibitors differential analysis found that immune-related genes were expressed higher in the high-risk group relative to the low-risk group (Fig. 4H). This also suggested that the lncRNA-related risk prognostic model can be used to select appropriate immune checkpoint inhibitors for STAD patients to improve therapeutic efficacy. Through drug sensitivity analysis, we screened a total of 25 drugs related to STAD treatment (p < 0.005, Supplemental Fig. 2).

The role of tumor clusters of necroptosis-related lncRNAs in immunotherapy

According to the consensus clustering analysis, the necroptosis-related lncRNAs were divided into three subtypes: cluster-C1 (C1), cluster-C2 (C2), and cluster-C3 (C3) (Fig. 5A). Through K-M analysis, it was found that there was a difference in survival between the clusters, where C2 had an optimal survival time (Fig. 5B). To explore the correspondence between STAD clusters and risk scores, we use the Sandel diagram for demonstration. The results showed that most of the C2 were low-risk patients (Fig. 5C). Through PCA analysis, the results showed that patients in the high-risk and low-risk groups and different tumor clusters could be distinguished based on the expression of model lncRNAs (Figs. 5D, 5E). The results of the t-SNE analysis also provided good validation of tumor clusters (Fig. 5F, 5G). TME analysis showed differences between tumor clusters between StromalScore, ImmuneScore, and ESTIMATEScore (Figs. 5H-J). Among them, the C3 had the highest score in the analysis of the three TMEs. Figure 5K showed a heat map of immune cells with tumor clusters by different analytical methods. Through the differential analysis of immune checkpoints, it was found that almost all immune checkpoint-related genes were expressed differently in various tumor clusters (Fig. 5L). Among them, CD44 expression was the highest. By comparing all drug sensitivities, we screened out 29 drugs with differences in drug sensitivity across different clusters (p < 0.005, Supplemental Fig. 3).

Necroptosis, as a newly discovered mode of regulated cell death, played an important role in maintaining a stable environment and embryonic development in the body. It was also a determinant of the pathological etiology of a variety of human diseases[27]. However, the regulatory mechanism of necroptosis and its correlation with tumor pathological mechanisms still faced many problems and challenges. Under different conditions, necroptosis can not only directly remove tumor cells, but also release damage-associated molecular patterns (DAMPs) to recruit immune cells, build a tumor microenvironment immune signaling system, and indirectly clear tumor cells[28]. It was precise because necroptosis that it played a dual role in the development of tumorigenesis and anti-tumor therapy. We needed to further explore the key molecules of necroptosis and the exact molecular mechanisms that interact with other genes. Especially in recent years, some lncRNAs studies have shown their important position in STAD, which can be used as a new biomarker for the early diagnosis and a new target for prevention in STAD. Therefore, the role of necroptosis-related lncRNAs in STAD and the anti-tumor immune response can be further elucidated to maximize the anti-tumor effect of necroptosis.

Necroptosis of tumor cells may promote tumorigenesis and metastasis by modulating the TME. In pancreatic ductal carcinoma, RIPK1 and RIPK3 expressions were elevated. The deletion of RIPK3 or inhibition of RIPK1 in vivo experiments delayed the progression of pancreatic ductal carcinoma in mice. This phenomenon was associated with an enhanced antitumor immune response, as increased lymphocyte infiltration and decreased immunosuppressive medullary cell infiltration can be observed in RIPK3-deficient pancreatic ductal carcinoma[6]. A recent study used phosphorylated MLKL-specific antibodies to detect necrotizing apoptosis of tumor cells occurring in mouse models of breast cancer MMVT-PyMT[29]. These studies suggest that tumor necroptosis occurred in vivo and showed a pro-tumor effect by stimulating the immune microenvironment that promoted tumor progression. The pro-tumor effects of necroptosis may also be caused by necroptosis of non-tumor cells. Necroptosis of intestinal epithelial cells had been reported to promote cancer by inducing colonic inflammation. In addition, the application of necroptosis drug inhibitors such as necrostatin⁃1 (Nec-1) in a dextran sulfate sodium-induced model of acute colitis significantly inhibits the occurrence of associated tumors due to colitis in mice[30]. In our study, we analyzed the expression of 67 necroptosis-related genes in STAD and found that necroptosis existed in STAD. Therefore, the development of therapeutic strategies that induce necroptosis of tumor cells showed great potential in STAD treatment.

LncRNA HOTAIR, one of the earliest representative LncRNAs, had shown that the higher the expression level of HOTAIR, the greater the risk of STAD and the worse the prognosis. Li et al had shown that BRD4 was a transcriptional regulator of MAGI2-AS3, which can promote the epithelial-mesenchymal transition (EMT) of MAGI2-AS3, thereby promoting the invasion and metastasis of STAD[31]. Numerous studies showed that its role as a molecular marker of tumors related to STAD had been affirmed by researchers. LncRNAs were widely distributed in peripheral plasma/blood, saliva, gastric juice, urine, and other fluids, and were stable and easy to detect. Zhao et al detected serum lncRNA HOTTIP in 126 patients with STAD and 120 healthy people (control group)[32]. After evaluating and comparing the diagnostic capacity of HOTTIP with other serum biomarkers, it was found that the AUC of HOTTIP's ability to diagnose STAD was significantly higher than that of carcinoembryonic antigen (CEA), CA19-9, CA72-4. This suggested that HOTTIP was superior to both in the diagnosis of STAD. Cao et al found that the serum lncRNA SPRY4-IT1 in patients with STAD was highly enriched with normal people[33]. And in patients with large tumor volume, deep invasion depth, positive lymph node metastasis, and advanced gastric cancer, high expression of SPRY4-IT1 were more common. This suggested that SPRY4-IT1 can be used as an early diagnostic marker and clinical staging indicator of STAD. In recent years, a large number of studies had shown that lncRNA was closely related to the prognosis of patients with STAD, and its mechanism had gradually been confirmed. He et al detected the expression of lncRNA UCA1 in 60 cases of STAD tissue and normal tissues, and the median survival of the low expression group of UCA1 was significantly longer than that of the high expression group of UCA1[34]. This indicated that UCA1 overexpression predicts poor prognosis in patients with STAD. Chen et al. detected the expression levels of lncRNA VPS9D1-AS1 in 126 cases of STAD and normal tissues, and the results showed that their expression in tumor tissues was significantly downregulated[35]. The univariate and multifactor survival analysis showed that VPS9D1-AS1 expression was an independent prognostic indicator in patients with STAD. In our study, a total of 472 necroptotic-related lncRNAs were found. Through co-expression analysis, 21 lncRNAs associated with necroptosis have significant OS correlations. We believed that with the extensive and in-depth development of research, the biological effects and molecular mechanisms of necroptosis-related lncRNAs will be further elucidated, and provided new ideas and targets for the diagnosis and treatment of STAD.

In our study, a risk prognostic model consisting of 21 necroptosis-related lncRNAs was constructed to better assess the prognosis of STAD patients. There was already evidence that lncRNAs were key regulators of the immune response, involved in the immune response and gene activation, and promoted immunophenotyping[36, 37]. With the remarkable results of immunotherapy in cancer treatment, immune-related lncRNAs had gradually become a new hot spot. It was a prognostic factor for a variety of tumors, including pancreatic cancer[38], malignant glioma[39], and breast cancer[40]. Considering that the prognosis of tumors was affected by multiple factors, we further combined other clinical features and constructed a nomogram to predict the individual survival rate of STAD patients, which provided a basis for the clinical decision on STAD. Studies in tumor immunology had also found that necroptosis had anti-tumor effects. Necroptosis may play an important role in stimulating immunogenicity and promoting anti-tumor immune surveillance[41]. Some studies had shown that tumor cells undergo necroptosis after releasing interleukin-1α (IL-1α) to activate dendritic cells (DCs). Activated DCs induced an anti-tumor immune response by producing cytotoxic IL-12 or activating CD8 + T cells to eliminate tumor cells[10, 11]. Similarly, the DAMPs released by tumor cells undergoing necroptosis can excite tumor antigen presentation in CD8 + T cells[12]. In addition, the natural killer T (NKT) cells had been reported to be involved in RIPK3-mediated anti-tumor immune responses. Because the deletion of RIPK3 impaired the antitumor activity of NKT cells[42]. In our study, we found that CD44 expression was the highest among all clusters. CD44 was a recognized marker of tumor stem cells and was a key regulator of the EMT, involved in tumorigenesis, progression, and metastasis. Immunotherapy targets and sensitivity of chemotherapy-related drugs were screened according to necroptosis-related lncRNAs. Therefore, fully elucidating the mechanism of necroptosis-related lncRNAs in STAD will deepen the understanding of necroptosis-related lncRNAs enhancing tumor immune response, which will help to explore the mechanism of immunotherapy resistance in STAD.

In summary, this study comprehensively depicted the TME landscape of necroptosis-related lncRNAs in STAD through a large number of biological omics data from STAD patients. After repeated verification in multiple dimensions, we found that there are 3 stable tumor clusters in STAD. These clusters were not only related to the prognosis of patients with STAD but also significantly related to the patient's subsequent treatment response and molecular typing. By quantitatively evaluating the risk score of tumor clusters, the accuracy of STAD immunotherapy can be better predicted as a single drug or combination therapy. This study provided a new idea and basis for necrotizing apoptosis-related lncRNAs to achieve more efficient clinical translation and accurately guide immunotherapy in STAD patients.

Funding

There is no funding.

Conflict of Interest Disclosures

The authors have no conflict of interest.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Consent for publication

Not applicable.

Ethical Statement

Approval of the research protocol by an Institutional Reviewer Board.

Not applicable.

Informed Consent.

Not applicable.

Registry and the Registration No. of the study/trial.

Not applicable.

Animal Studies.

Not applicable.

Acknowledgments

The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.

Author Contribution Statement

Yang Biao and Zhang Meijing designed the work; Luo Tianhang and Zhang Meijing wrote the main manuscript text; Luo Tianhang prepared the acquisition, analysis, and interpretation of data; Yang Biao and Liu Tao prepared figures and tables. All authors reviewed the manuscript.

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No competing interests reported.

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The necroptosis-related signature and tumor microenvironment immune characteristics associated with clinical prognosis and drug sensitivity analysis in stomach adenocarcinoma

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Abstract

Purpose

Methods

Results

Conclusions

Figures

Introduction

Methods

Collection and processing of clinical data

Selection of Necroptosis-Related Genes and lncRNAs

Establishment and verification of risk prognostic model

Construction of nomogram

Gene set enrichment analysis (GSEA)

Immune-related and drug-sensitivity analysis

Statistical analysis

Results

Necroptosis-related lncRNAs in STAD

Construct the risk prognostic model

Assessment of the risk prognostic model associated with necroptosis-lncRNAs

Immune-related analysis of risk score of necroptosis-related lncRNAs

The role of tumor clusters of necroptosis-related lncRNAs in immunotherapy

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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