A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Disulfidptosis-related LncRNAs forecast the prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive hematologic malignancy with a poor prognosis for patients. Disulfidptosis response-related long non-coding RNAs (DRLs) have been demonstrated to be closely associated with cancer development. Therefore, this study aims to construct a prognostic DRL signature and investigate the immune microenvironment for AML. RNA-seq and clinical data for AML patients were obtained from The Cancer Genome Atlas (TCGA) database. A total of 344 disulfidptosis-associated lncRNAs were identified, and a prognostic model consisting of seven lncRNAs was constructed and validated. Two risk groups, high-risk and low-risk, were identified. The model demonstrated a robust capacity to predict prognosis, with a worse overall survival for patients in the high-risk group. Additionally, differential expression of the seven lncRNAs were relatively higher in AML samples than in control samples via quantitative polymerase chain reaction(qPCR). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration analysis revealed a substantial infiltration of immune cells and enrichment of immune pathways in the high-risk group. The sensitivity of AML patients to drugs varied according to their risk grade. This study identified a DRL signature, which can effectively predict the prognosis of AML and better understand the mechanism of disulfidptosis in AML. This provides a basis for personalized immunotherapy in AML patients.

Long noncoding RNAs in acute myeloid leukemia: biomarkers, prognostic indicators, and treatment potential

Long noncoding RNAs (lncRNAs) have been recognized as significant modulators of gene expression and are essential for various biological functions, even though they don't appear to have the ability to encode proteins. Originally considered dark matter, lncRNAs have been recognized as being dysregulated and contributing to the onset, progression, and resistance to treatment of acute myeloid leukemia (AML). AML is a prevalent type of leukemia characterized by the disruption of myeloid cell differentiation, leading to an increased number of immature myeloid progenitor cells. Currently, the need for novel biomarkers and treatment targets to enhance therapeutic alternatives has led to a focus on lncRNAs as possible indicators for prognostic, therapeutic, and diagnostic systems in various human cancers, including AML. Recent research has recognized a limited set of lncRNAs as possible prognostic biomarkers or diagnoses in AML. This review evaluates the key research that highlights the significance of lncRNAs in AML and discusses their roles and impacts on the disease. Furthermore, we intend to underscore the importance of lncRNAs as new and trustworthy markers for the diagnosis, prediction, drug resistance, and targets for treatment in AML.

RAB39B: A novel biomarker for acute myeloid leukemia identified via multi-omics and functional validation

Background

The objective of this research was to investigate the involvement of RAB39B in acute myeloid leukemia (AML) using bioinformatics analysis and in vitro experiments for validation.

Methods

In this article, RNA sequencing data from The Cancer Genome Atlas and genotype-tissue expression were utilized to analyze the expression of RAB39BA and identify differentially expressed genes.

Results

AML exhibited elevated expression of RAB39B in diverse tumor types. In laboratory experiments, it has been demonstrated that RAB39B exhibits a significant expression level in AML cell lines when compared to normal peripheral blood monocytes. Moreover, RAB39B is closely linked to the growth and programmed cell death of AML cells.

Conclusion

In conclusion, RAB39B shows potential as a biomarker for the identification and prediction of AML, contributing to the growth and cell death processes in AML.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for rheumatoid arthritis by integrated bioinformatics analysis and single-cell RNA sequencing analysis

Introduction

Rheumatoid arthritis (RA) is a chronic autoimmune disorder intricately liked with inflammation. Cuproptosis, an emerging type of cell death, has been implicated in the initiation and development of RA. However, the exact alterations in the expression and biological function of cuproptosis-related genes (CRGs) in RA remain poorly understood. Therefore, our study aims to elucidate the potential association between CRGs and RA, with the goal of identifying novel biomarkers for the treatment and prognosis of RA.

Methods

In this study, we identified ten differentially expressed cuproptosis-related genes (DE-CRGs) between patients with RA and controls. Through comprehensive functional enrichment and protein-protein interaction (PPI) network analysis, we explored the functional roles of the DE-CRGs. Additionally, we investigated the correlation between DE-CRGs and immune infiltration, immune factors, diagnostic efficacy, and potential therapeutic drugs.

Results

Leveraging single-cell RNA sequencing data, we conducted a detailed analysis to elucidate alterations in various cell clusters associated with RA. Our study unveiled a significant association between DE-CRGs and diverse biological functions, as well as potential drug candidates.

Discussion

These findings provide crucial insights into the involvement of DE-CRGs in the pathogenesis of RA and shed light on potential therapeutic strategies.

A lncRNA signature associated with endoplasmic reticulum stress supports prognostication and prediction of drug resistance in acute myelogenous leukemia

BACKGROUND

Acute myelogenous leukemia (AML) is a type of blood cancer that is characterized by the accumulation of young and undeveloped myeloid cells in the bone marrow. It is considered a heterogeneous disease due to its diverse nature. Endoplasmic reticulum (ER) stress has emerged as a critical regulator of tumor development and drug resistance in various cancers. Long non-coding RNAs (lncRNAs) have been found to play a role in the development and prognosis of AML. Nonetheless, there is still limited understanding regarding the involvement of ER stress-related lncRNAs in AML prognosis and their predictive ability for drug resistance. The objective of this study was to examine the potential prognostic and predictive significance of an ER stress-related lncRNA signature in patients diagnosed with AML.

METHODS

Based on the bulk RNA sequence data, we constructed an ER stress-related lncRNA signature using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. We established nomograms and calibration curves to assess the clinical value of the signature by analyzing overall survival (OS) rates between different risk groups. We also conducted tumor mutation burden (TMB) analysis, predicted immune responses, performed functional and biological enrichment analysis, and evaluated drug sensitivity to investigate the impact of the prognostic signature. Additionally, we built a consensus cluster to explore the need for personalized immunotherapy approaches in treating patients with AML.

RESULTS

A prognostic signature was constructed using 227 ER stress-related lncRNAs that showed differential expression. Patients in the high-risk category demonstrated decreased OS rates in comparison to individuals in the low-risk category. The findings from the nomogram and receiver operating characteristic (ROC) curve analysis suggest a notable disparity in age between the different categories. Among the group at high risk, we noticed a considerably greater TMB in comparison to the low-risk group. Furthermore, individuals with both an elevated risk score and high TMB demonstrated the most unfavorable survival rates. Significant differences were observed in the immune responses between the groups classified as high- and low-risk. We then systematically evaluated three different clusters to assess immune responses and drug responses. Through analyzing the association between the risk score and various medications, we have discovered 18 potential drug contenders capable of effectively addressing AML. Furthermore, we conducted pathway analyses to determine the targeted pathways of these drugs.

CONCLUSIONS

Our data serve as a valuable resource for decoding the immune responses, somatic mutational landscape, drug resistance, and potential biological functions in AML patients. Additionally, our findings offer valuable insights into the association between related lncRNAs and the immune microenvironment of AML. It provides us with promising insights that can help in the development of precise therapeutic strategies.

MT1E in AML: a gateway to understanding regulatory cell death and immunotherapeutic responses

Regulated cell death (RCD) plays a crucial role in the initiation and progression of tumors, particularly in acute myeloid leukemia (AML). This study investigates the prognostic importance of RCD-related genes in AML and their correlation with immune infiltration. We combined TCGA and GTEx data, analyzing 1,488 RCD-related genes, to develop a predictive model using LASSO regression and survival analysis. The model's accuracy was validated against multiple databases, examining immune cell infiltration, therapy responses, and drug sensitivity among risk groups. RT-qPCR confirmed MT1E expression in AML patients and healthy bone marrow. CCK8 and Transwell assays measured cell proliferation, adhesion, migration, and invasion, while flow cytometry and Western blotting assessed apoptosis and protein expression. We developed a prognostic model using 10 RCD methods, which demonstrated strong predictive ability, showing an inverse correlation between age and risk scores with survival in AML patients. Functional enrichment analysis of the model is linked to immune modulation pathways. RT-qPCR revealed significantly lower MT1E expression in AML vs healthy bone marrow (P < 0.05). Consequently, experiments were designed to assess the function of MT1E overexpression. Findings indicated that MT1E overexpression showed it significantly reduced THP-1 cell proliferation and adhesion (P < 0.001), decreased migration (P < 0.001), and invasiveness (P < 0.05), and increased apoptosis (P < 0.05), with a notable rise in Caspase3 expression. A novel AML RCD risk model was developed, showing promise as a prognostic marker for evaluating outcomes and immune therapy effectiveness. Insights into MT1E's impact on AML cell proliferation and apoptosis open possibilities for improving patient outcomes and devising personalized treatment strategies.

Copper homeostasis and copper-induced cell death in tumor immunity: implications for therapeutic strategies in cancer immunotherapy

Copper is an important trace element for maintaining key biological functions such as cellular respiration, nerve conduction, and antioxidant defense. Maintaining copper homeostasis is critical for human health, and its imbalance has been linked to various diseases, especially cancer. Cuproptosis, a novel mechanism of copper-induced cell death, provides new therapeutic opportunities for metal ion regulation to interact with cell fate. This review provides insights into the complex mechanisms of copper metabolism, the molecular basis of cuproptosis, and its association with cancer development. We assess the role of cuproptosis-related genes (CRGs) associated with tumorigenesis, their importance as prognostic indicators and therapeutic targets, and the impact of copper homeostasis on the tumor microenvironment (TME) and immune response. Ultimately, this review highlights the complex interplay between copper, cuproptosis, and cancer immunotherapy.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

MICAL1 promotes the proliferation in acute myeloid leukemia and is associated with clinical prognosis and immune infiltration

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has a poor prognosis and a high rate of relapse. Dysregulated metabolism plays an important role in AML progression. This study aimed to conduct a comprehensive analysis of MRGs using TCGA and GEO datasets and further explore the potential function of critical MRGs in AML progression. In this study, we identified 17 survival-related differentially expressed MRGs in AML using TCGA and GEO datasets. The 150 AML samples were divided into three molecular subtypes using 17 MRGs, and we found that three molecular subtypes exhibited a different association with ferroptosis, cuproptosis and m6A related genes. Moreover, a prognostic signature that comprised nine MRGs and had good predictive capacity was established by LASSO-Cox stepwise regression analysis. Among the 17 MRGs, our attention focused on MICAL1 which was highly expressed in many types of tumors, including AML and its overexpression was also confirmed in several AML cell lines. We also found that the expression of MICAL1 was associated with several immune cells. Moreover, functional experiments revealed that knockdown of MICAL1 distinctly suppressed the proliferation of AML cells. Overall, this study not only contributes to a deeper understanding of the molecular mechanisms underlying AML but also provides potential targets and prognostic markers for AML treatment. These findings offer robust support for further research into therapeutic strategies and mechanisms related to AML, with the potential to improve the prognosis and quality of life for AML patients. Nevertheless, further research is needed to validate these findings and explore more in-depth molecular mechanisms.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma

Background

Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group.

Methods

Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups.

Results

The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors.

Conclusions

The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.

Identification of a novel cuproptosis-related gene signature for multiple myeloma diagnosis

BACKGROUND

Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis-related genes (CRGs) in the prediction of MM diagnosis.

METHODS

Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in MM cell samples.

RESULTS

The optimal gene signature was identified using LASSO and SVM-RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature-based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune-related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings.

CONCLUSION

We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune-related pathways.

Construction and validation of a cuproptosis-related lncRNA prognosis signature in bladder carcinoma

BACKGROUND

Bladder cancer (BLCA) is a prevalent urological tumor with high morbidity and mortality. However, BLCA treatment remains challenging due to a lack of effective biomarkers. Long non-coding RNAs (lncRNAs), as active participants in tumor progression are involved in multiple biological regulatory mechanisms, and cuproptosis-related genes participate in the development of cancer. It is important to discover cuproptosis- related lncRNAs for BLCA diagnosis and treatment.

METHODS

A predictive signature was constructed based on least absolute shrinkage and selection operator regression (LASSO) and Cox regression analyses of the 9 cuproptosis-related lncRNAs. Samples were divided into high-risk group and low-risk group based on their median risk scores to explore their prognosis.

RESULTS

This signature is well predictive, as evidenced by the receiver operating characteristic curves (ROC curves) and K-M curves. Based on the nomogram, we were able to visually forecast the survival rates of patients with BLCA at 1-, 3-, and 5-year, and the calibration plots displayed that the actual results were well matched with the predicted 1-, 3-, and 5-year survival rates. Furthermore, BLCA patients in the high-risk group had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and lower TMB. Finally, we investigated the response of antitumor drugs for BLCA patients in different risk groups, and a statistically significant difference was observed in the sensitivity of those drugs between low- and the high-risk groups.

CONCLUSION

According to the 9 cuproptosis-related lncRNAs, we constructed a signature which can be served as a promising prognostic biomarker for BLCA patients.

Long non-coding RNAs regulate treatment outcome in leukemia: What have we learnt recently?

Leukemia is a group of highly heterogeneous and life-threatening blood cancers that originate from abnormal hematopoietic stem cells. Multiple treatments are approved for leukemia, including chemotherapy, targeted therapy, hematopoietic stem cell transplantation, radiation therapy, and immunotherapy. Unfortunately, therapeutic resistance occurs in a substantial proportion of patients and greatly compromises the treatment efficacy of leukemia, resulting in relapse and mortality. The abnormal activity of receptor tyrosine kinases, cell membrane transporters, intracellular signal transducers, transcription factors, and anti-apoptotic proteins have been shown to contribute to the emergence of therapeutic resistance. Despite these findings, the exact mechanisms of treatment resistance are still not fully understood, which limits the development of effective measures to overcome it. Long non-coding RNAs (lncRNA) are a class of regulatory molecules that are gaining increasing attention, and lncRNA-mediated regulation of therapeutic resistance against multiple drugs for leukemia is being revealed. These dysregulated lncRNAs not only serve as potential targets to reduce resistance but also might improve treatment response prediction and individualized treatment decision. Here, we summarize the recent findings on lncRNA-mediated regulation of therapeutic resistance in leukemia and discuss future perspectives on how to make use of the dysregulated lncRNAs in leukemia to improve treatment outcome.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia

BACKGROUND

Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood.

METHODS

The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples.

RESULTS

A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples.

CONCLUSION

Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Cuproptosis-Related lncRNA Gene Signature Establishes a Prognostic Model of Gastric Adenocarcinoma and Evaluate the Effect of Antineoplastic Drugs

BACKGROUND

One of the most frequent malignancies of the digestive system is stomach adenocarcinoma (STAD). Recent research has demonstrated how cuproptosis (copper-dependent cell death) differs from other cell death mechanisms that were previously understood. Cuproptosis regulation in tumor cells could be a brand-new treatment strategy. Our goal was to create a cuproptosis-related lncRNA signature. Additionally, in order to evaluate the possible immunotherapeutic advantages and drug sensitivity, we attempted to study the association between these lncRNAs and the tumor immune microenvironment of STAD tumors.

METHODS

The TCGA database was accessed to download the RNA sequencing data, genetic mutations, and clinical profiles for TCGA STAD. To locate lncRNAs related to cuproptosis and build risk-prognosis models, three techniques were used: co-expression network analysis, Cox-regression techniques, and LASSO techniques. Additionally, an integrated methodology was used to validate the models' predictive capabilities. Then, using GO and KEGG analysis, we discovered the variations in biological functions between each group. The link between the risk score and various medications for STAD treatment was estimated using the tumor mutational load (TMB) and tumor immune dysfunction and rejection (TIDE) scores.

RESULT

We gathered 22 genes linked to cuproptosis based on the prior literature. Six lncRNAs related to cuproptosis were used to create a prognostic marker (AC016394.2, AC023511.1, AC147067.2, AL590705.3, HAGLR, and LINC01094). After that, the patients were split into high-risk and low-risk groups. A statistically significant difference in overall survival between the two groups was visible in the survival curves. The risk score was demonstrated to be an independent factor affecting the prognosis by both univariate and multivariate Cox regression analysis. Different risk scores were substantially related to the various immunological states of STAD patients, as further evidenced by immune cell infiltration and ssGSEA analysis. The two groups had differing burdens of tumor mutations. In addition, immunotherapy was more effective for STAD patients in the high-risk group than in the low-risk group, and risk scores for STAD were substantially connected with medication sensitivity.

CONCLUSIONS

We discovered a marker for six cuproptosis-associated lncRNAs linked to STAD as prognostic predictors, which may be useful biomarkers for risk stratification, evaluation of possible immunotherapy, and assessment of treatment sensitivity for STAD.