Immunogenic cell death signatures derived from on-treatment tumor specimens for predicting immune checkpoint blockade therapy response and prognosis in metastatic melanoma DOI Creative Commons
Huancheng Zeng,

Qiongzhi Jiang,

Rendong Zhang

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 17, 2024

Abstract Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, substantial number patients do not respond to this treatment and face risks recurrence metastasis. This study collected data multiple datasets, including cohorts Riaz et al., Gide MGH, Abril-Rodriguez focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method calculate immunogenic cell death scores (ICDS) employed an elastic network algorithm construct model predicting efficacy. By analyzing 18 ICD signatures, we identified 9 key signatures effectively predict response for specimens. Results showed high had significantly higher rates therapy compared those low scores. ROC demonstrated AUC values both training validation sets were around 0.8, indicating good predictive performance. Additionally, survival revealed longer progression-free (PFS) overall (OS). identify related melanoma. These features can only efficacy but also provide references clinical decision-making. The results indicate plays important role immunotherapy expressing more accurately prognosis specimens, thus providing basis personalized treatment.

Язык: Английский

ITCH inhibits alkaliptosis in human pancreatic cancer cells through YAP1-dependent SLC16A1 activation DOI

Xiutao Cai,

Fangquan Chen, Hu Tang

и другие.

The International Journal of Biochemistry & Cell Biology, Год журнала: 2024, Номер 175, С. 106646 - 106646

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

1
Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer DOI Creative Commons

Ming Wang,

Bangshun Dai, Qiushi Liu

и другие.

Cancer Cell International, Год журнала: 2024, Номер 24(1)

Опубликована: Авг. 24, 2024

Prostate cancer is one of the most common cancers in men with a significant proportion patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles tumor progression and can potentially serve as prognostic therapeutic biomarkers PCa. This study aimed develop signature for BCR PCa using PCD-related genes. We conducted an analysis 19 different modes PCD comprehensive model. Bulk transcriptomic, single-cell genomic, clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, GSE193337. analyzed expression mutations constructed, evaluated, validated Ten found be associated PCa, specific patterns exhibited by various components within microenvironment. Through Lasso Cox regression analysis, we established Cell Death Index (PCDI) utilizing 11-gene signature. High PCDI values five independent datasets increased risk patients. Notably, older age advanced T N staging higher values. By combining staging, constructed nomogram enhanced predictive performance. Additionally, high significantly correlated decreased drug sensitivity, drugs such Docetaxel Methotrexate. Patients lower demonstrated immunophenoscores (IPS), suggesting response rate immune therapy. Furthermore, was checkpoint genes key microenvironment, macrophages, cells, NK cells. Finally, specimens differential PCDI-related PCDRGs at both gene protein levels. In conclusion, developed novel PCD-based feature that successfully predicted provided insights into sensitivity potential These findings have implications treatment

Язык: Английский

Процитировано

1
Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways DOI Creative Commons
QingKun Chen,

ChenGuang Zhang,

Tao Meng

и другие.

Cell Biology International, Год журнала: 2024, Номер 48(12), С. 1816 - 1835

Опубликована: Авг. 27, 2024

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumor, highlighting a significant need for reliable predictive models to assess clinical prognosis, disease progression, and drug sensitivity. Recent studies have highlighted critical role of various programmed cell death pathways, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic death, NETotic parthanatos, lysosome-dependent autophagy-dependent alkaliptosis, oxeiptosis, disulfidptosis, in tumor development. Therefore, by investigating these we aimed develop model HCC prognosis We analyzed transcriptome, single-cell genomic, information using data from TCGA-LIHC, GSE14520, GSE45436, GSE166635 datasets. Machine learning algorithms were used establish index (CDI) with seven gene signatures, which was validated across three independent datasets, showing that high CDI correlates poorer prognosis. Unsupervised clustering revealed molecular subtypes distinct biological processes. Furthermore, nomogram integrating demonstrated good performance. associated immune checkpoint genes microenvironment components transcriptome analysis. Drug sensitivity analysis indicated patients may be resistant oxaliplatin cisplatin but sensitive axitinib sorafenib. In summary, our offers precise prediction outcomes HCC, providing valuable insights personalized treatment strategies.

Язык: Английский

Процитировано

1
Navigating the complexities of cell death: Insights into accidental and programmed cell death DOI

Mohammad-Sadegh Lotfi,

Fatemeh Behnam Rassouli

Tissue and Cell, Год журнала: 2024, Номер 91, С. 102586 - 102586

Опубликована: Окт. 16, 2024

Язык: Английский

Процитировано

1
Immunogenic cell death signatures derived from on-treatment tumor specimens for predicting immune checkpoint blockade therapy response and prognosis in metastatic melanoma DOI Creative Commons
Huancheng Zeng,

Qiongzhi Jiang,

Rendong Zhang

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Авг. 17, 2024

Abstract Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, substantial number patients do not respond to this treatment and face risks recurrence metastasis. This study collected data multiple datasets, including cohorts Riaz et al., Gide MGH, Abril-Rodriguez focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method calculate immunogenic cell death scores (ICDS) employed an elastic network algorithm construct model predicting efficacy. By analyzing 18 ICD signatures, we identified 9 key signatures effectively predict response for specimens. Results showed high had significantly higher rates therapy compared those low scores. ROC demonstrated AUC values both training validation sets were around 0.8, indicating good predictive performance. Additionally, survival revealed longer progression-free (PFS) overall (OS). identify related melanoma. These features can only efficacy but also provide references clinical decision-making. The results indicate plays important role immunotherapy expressing more accurately prognosis specimens, thus providing basis personalized treatment.

Язык: Английский

Процитировано

0