Metal-dependent programmed cell death-related lncRNA prognostic signatures and natural drug sensitivity prediction for gastric cancer

Song, Xuesong; Hou, Lin; Zhao, Yuanyuan; Guan, Qingtian; Li, Zhiwen

doi:10.3389/fphar.2022.1039499

Cited by 7 publications

(6 citation statements)

References 60 publications

(51 reference statements)

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“…Our study also indicated that CDI score could potentially function as a predictor of drug sensitivity for certain FDA-approved drugs, such as Olaparib and Oxaliplatin. This aligns with prior research suggesting that PCD-related genes are associated with drug sensitivity in diverse cancer types, including ovarian cancer and colorectal cancer [ 26 – 28 ]. However, our study had its basis on in silico analysis, and additional experimental validation is essential to assess the clinical applicability of our findings.…”

Section: Discussionsupporting

confidence: 89%

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

LIANG,

LI,

et al. 2024

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Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as “edgeR” and “maftools” and visualized the characteristics of these genes using the “circlize” R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the “ConsensusClusterPlus” tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructe...

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Section: Discussionsupporting

confidence: 89%

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

LIANG,

LI,

et al. 2024

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“…Excitingly, a range of gene models centered around programmed cell death (such as ferroptosis and cuprotosis) have been developed. [21][22][23][24][31][32][33] These models hold great promise in identifying potential targets for therapeutic interventions in GC.…”

Section: Discussionmentioning

confidence: 99%

“…This area represents a new research direction in GC. [21][22][23][24] One novel and less explored mechanism of programmed cell death is the discovery of disulfidptosis this year. Researchers have observed that during glucose starvation, an abnormal accumulation of intracellular disulfide bonds disturbs the normal disulfide bonds between cytoskeletal proteins, ultimately leading to cell death.…”

Section: Introductionmentioning

confidence: 99%

A disulfidptosis-related classification and risk signature identifies immunotherapy biomarkers and predicts prognosis in gastric cancer: An observational study

Chen,

Jiang

2024

Medicine

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Gastric cancer (GC) is one of the most prevalent types of cancer globally, often detected at advanced stages. However, its prognosis remains poor, necessitating the exploration of new biomarkers. Disulfidptosis, a recently identified form of programmed cell death, has not yet been investigated in relation to GC and its associated mechanisms. We analyzed and identified potential associations between disulfidptosis genes and GC clinical risk using TCGA (The Cancer Genome Atlas)-STAD (stomach adenocarcinoma) as the training set and GSE84433 as the validation set. In addition, we explored the prognostic value and potential biological mechanisms of disulfide genes in GC by consensus clustering, enrichment analysis, mutation histology analysis and immune infiltration analysis. Finally, we constructed a disulfidptosis-related risk signature (DRRS) to assess the association between risk class, survival prognosis, and immune infiltration. By utilizing data from 19 disulfidptosis-related genes, we successfully identified subgroups of C1 and C2 patients through consensus clustering. Notably, the 2 groups exhibited significant variations in terms of survival rates, immune scores, and immune cell infiltration. Subsequently, we developed a DRRS via LASSO (least absolute shrinkage and selection operator) regression analysis, incorporating PRICKLE1, NRP1, APOD, MISP3, and SERPINE1. This scoring system effectively distinguished individuals with high and low risks, as verified with a validation set. These findings strongly indicate a close association between disulfidptosis and the immune microenvironment of GC tumors. Moreover, the DRRS demonstrated commendable predictive capabilities for the survival outcomes of GC patients. In this study, we have identified the association between different subtypes of disulfidptosis and alterations in the GC immunotumour microenvironment. Furthermore, we have developed and verified the accuracy of the DRRS, a valuable tool for predicting survival, biological function, and immune infiltration in patients with GC. These findings contribute to a better comprehension of disulfidptosis and offer potential opportunities for innovative approaches in GC treatment.

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“…It can circumvent the limitations of individual lncRNAs in predicting prognosis, but also avoid the defects of interactions among lncRNAs. Currently, studies on the prognostic signature of Cuproptosis-associated lncRNAs have been conducted in tumors such as bladder cancer ( 53 ), head and neck squamous cell carcinoma ( 54 ), and gastric cancer ( 55 ). However, most studies have used co-expression analysis for screening of lncRNAs, but few studies have systematically explored the genes from which lncRNAs originate, as well as their distribution and expression differences at the single-cell level.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

Sun,

Yao,

Man

et al. 2023

Front. Immunol.

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BackgroundCuproptosis, a novel mode of cell death associated with the tricarboxylic acid (TCA) cycle, is relevant to the development of cancer. However, the impact of single-cell-based Cuproptosis-associated lncRNAs on the Tumor immune microenvironment (TIME) of Pancreatic adenocarcinoma (PAAD) and its potential value for individualized immunotherapy has not been clarified.Methods14 immune-related CRGs were screened by exploring the interaction between differentially expressed Immune-Related Genes (IRGs) and Cuproptosis-Related Genes (CRGs) in PAAD. Next, the expression amount and expression distribution of CRGs in single-cell samples were analyzed by focusing on 7-CRGs with significant expressions. On the one hand, MAP2K2, SOD1, and VEGFA, which were significantly differentially expressed between PAAD sites and normal tissues adjacent to them, were subjected to immunohistochemical validation and immune landscape analysis. On the other hand, from these 7-CRGs, prognostic signatures of lncRNAs were established by co-expression and LASSO-COX regression analysis, and their prognostic value and immune relevance were assessed. In addition, this study not only validated the hub CRGs and the lncRNAs constituting the signature in a PAAD animal model treated with immunotherapy-based combination therapy using immunohistochemistry and qRT-PCR but also explored the potential value of the combination of targeted, chemotherapy and immunotherapy.ResultsBased on the screening of 7-CRGs significantly expressed in a PAAD single-cell cohort and their co-expressed Cuproptosis-Related lncRNAs (CRIs), this study constructed a prognostic signature of 4-CRIs named CIR-score. A Nomogram integrating the CIR-score and clinical risk factors was constructed on this basis to predict the individualized survival of patients. Moreover, high and low-risk groups classified according to the median of signatures exhibited significant differences in clinical prognosis, immune landscape, bioenrichment, tumor burden, and drug sensitivity. And the immunohistochemical and qRT-PCR results of different mouse PAAD treatment strategies were consistent with the trend of inter-group variability in drug sensitivity of hub CRGs and CIR-score. The combination of immunotherapy, targeted therapy, and chemotherapy exhibited a better tumor suppression effect.ConclusionCIR-score, as a Cuproptosis-related TIME-specific prognostic signature based on PAAD single cells, not only predicts the prognosis and immune landscape of PAAD patients but also provides a new strategy for individualized immunotherapy-based combination therapy.

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Metal-dependent programmed cell death-related lncRNA prognostic signatures and natural drug sensitivity prediction for gastric cancer

Cited by 7 publications

References 60 publications

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay

A disulfidptosis-related classification and risk signature identifies immunotherapy biomarkers and predicts prognosis in gastric cancer: An observational study

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

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