A unique regulated cell death-related classification regarding prognosis and immune landscapes in non-small cell lung cancer

Excavating regulated cell death signatures to predict prognosis, tumor microenvironment and therapeutic response in HR+/HER2- breast cancer

Regulated cell death (RCD) has been documented to have great potentials for discovering novel biomarkers and therapeutic targets in malignancies. But its role and clinical value in HR+/HER2- breast cancer, the most common subtype of breast cancer, are obscure. In this study, we comprehensively explored 12 types of RCD patterns and found extensive mutations and dysregulations of RCD genes in HR+/HER2- breast cancer. A prognostic RCD scoring system (CDScore) based on six critical genes (LEF1, SLC7A11, SFRP1, IGFBP6, CXCL2, STXBP1) was constructed, in which a high CDScore predicts poor prognosis. The expressions and prognostic value of LEF1 and SFRP1were also validated in our tissue microarrays. The nomogram established basing on CDScore, age and TNM stage performed satisfactory in predicting overall survival, with an area under the ROC curve of 0.89, 0.82 and 0.8 in predicting 1-year, 3-year and 5-year overall survival rates, respectively. Furthermore, CDScore was identified to be correlated with tumor microenvironments and immune checkpoints by excavation of bulk and single-cell sequencing data. Patients in CDScore high group might be resistant to standard chemotherapy and target therapy. Our results underlined the potential effects and importance of RCD in HR+/HER2- breast cancer and provided novel biomarkers and therapeutic targets for HR+/HER2- breast cancer patients.

Leveraging cell death patterns to predict metastasis in prostate adenocarcinoma and targeting PTGDS for tumor suppression

Metastasis is the major cause of treatment failure in patients with prostate adenocarcinoma (PRAD). Diverse programmed cell death (PCD) patterns play an important role in tumor metastasis and hold promise as predictive indicators for PRAD metastasis. Using the LASSO Cox regression method, we developed PCD score (PCDS) based on differentially expressed genes (DEGs) associated with PCD. Clinical correlation, external validation, functional enrichment analysis, mutation landscape analysis, tumor immune environment analysis, and immunotherapy analysis were conducted. The role of Prostaglandin D2 Synthase (PTGDS) in PRAD was examined through in vitro experiments, single-cell, and Mendelian randomization (MR) analysis. PCDS is elevated in patients with higher Gleason scores, higher T stage, biochemical recurrence (BCR), and higher prostate-specific antigen (PSA) levels. Individuals with higher PCDS are prone to metastasis, metastasis after BCR, BCR, and castration resistance. Moreover, PRAD patients with low PCDS responded positively to immunotherapy. Random forest analysis and Mendelian randomization analysis identified PTGDS as the top gene associated with PRAD metastasis and in vitro experiments revealed that PTGDS was considerably downregulated in PRAD cells against normal prostate cells. Furthermore, the overexpression of PTGDS was found to suppress the migration, invasion, proliferationof DU145 and LNCaP cells. To sum up, PCDS may be a useful biomarker for forecasting the possibility of metastasis, recurrence, castration resistance, and the efficacy of immunotherapy in PRAD patients. Additionally, PTGDS was identified as a viable therapeutic target for the management of PRAD.

Molecular map of disulfidptosis-related genes in lung adenocarcinoma: the perspective toward immune microenvironment and prognosis

BACKGROUND

Disulfidptosis is a recently discovered form of programmed cell death that could impact cancer development. Nevertheless, the prognostic significance of disulfidptosis-related genes (DRGs) in lung adenocarcinoma (LUAD) requires further clarification.

METHODS

This study systematically explores the genetic and transcriptional variability, prognostic relevance, and expression profiles of DRGs. Clusters related to disulfidptosis were identified through consensus clustering. We used single-sample gene set enrichment analysis and ESTIMATE to assess the tumor microenvironment (TME) in different subgroups. We conducted a functional analysis of differentially expressed genes between subgroups, which involved gene ontology, the Kyoto encyclopedia of genes and genomes, and gene set variation analysis, in order to elucidate their functional status. Prognostic risk models were developed using univariate Cox regression and the least absolute shrinkage and selection operator regression. Additionally, single-cell clustering and cell communication analysis were conducted to enhance the understanding of the importance of signature genes. Lastly, qRT-PCR was employed to validate the prognostic model.

RESULTS

Two clearly defined DRG clusters were identified through a consensus-based, unsupervised clustering analysis. Observations were made concerning the correlation between changes in multilayer DRG and various clinical characteristics, prognosis, and the infiltration of TME cells. A well-executed risk assessment model, known as the DRG score, was developed to predict the prognosis of LUAD patients. A high DRG score indicates increased TME cell infiltration, a higher mutation burden, elevated TME scores, and a poorer prognosis. Additionally, the DRG score showed a significant correlation with the tumor mutation burden score and the tumor immune dysfunction and exclusion score. Subsequently, a nomogram was established for facilitating the clinical application of the DRG score, showing good predictive ability and calibration. Additionally, crucial DRGs were further validated by single-cell sequencing data. Finally, crucial DRGs were further validated by qRT-PCR and immunohistochemistry.

CONCLUSION

Our new DRG signature risk score can predict the immune landscape and prognosis of LUAD. It also serves as a reference for LUAD's immunotherapy and chemotherapy.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma

BACKGROUND

Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis.

METHODS

Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression.

RESULTS

Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR.

CONCLUSIONS

Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.

Targeted therapy for non-small-cell lung cancer: New insights into regulated cell death combined with immunotherapy

Non-small-cell lung cancer (NSCLC), which has a high rate of metastatic spread and drug resistance, is the most common subtype of lung cancer. Therefore, NSCLC patients have a very poor prognosis and a very low chance of survival. Human cancers are closely linked to regulated cell death (RCD), such as apoptosis, autophagy, ferroptosis, pyroptosis, and necroptosis. Currently, small-molecule compounds targeting various types of RCD have shown potential as anticancer treatments. Moreover, RCD appears to be a specific part of the antitumor immune response; hence, the combination of RCD and immunotherapy might increase the inhibitory effect of therapy on tumor growth. In this review, we summarize small-molecule compounds used for the treatment of NSCLC by focusing on RCD and pharmacological systems. In addition, we describe the current research status of an immunotherapy combined with an RCD-based regimen for NSCLC, providing new ideas for targeting RCD pathways in combination with immunotherapy for patients with NSCLC in the future.

Diverse regulated cell death modes predict the immune microenvironment and drug sensitivity in lung adenocarcinoma

Integrated action modes of regulated cell death (RCD) in lung adenocarcinoma (LUAD) have not been comprehensively dissected. Here, we adopted 15 RCD modes, including 1350 related genes, and established RCD signature scores. We found that LUAD patients with high RCD scores had a significantly worse prognosis in all four different cohorts (TCGA, KM-plotter, GSE31210, and GSE30219). Our nomogram established based on the RCD score and clinical characteristics performed well in both the discovery and validation sets. There was a close correlation between the RCD scores and LUAD molecular subtypes identified by unsupervised consensus clustering. Furthermore, we profiled the tumor microenvironment via deconvolution and found significant differences in immune activity, transcription factor activity and molecular pathway enrichment between the RCD-high and RCD-low groups. More importantly, we revealed that the regulation of antigen presentation is the crucial mechanism underlying RCD. In addition, higher RCD scores predict poorer sensitivity to multiple therapeutic drugs, which indicates that RCD scores may serve as a promising predictor of chemotherapy and immunotherapy outcomes. In summary, this work is the first to reveal the internal links between RCD modes, LUAD, and cancer immunity and highlights the necessity of RCD scores in personalizing treatment plans.