Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery

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.

Identification and validation of an immune-derived multiple programmed cell death index for predicting clinical outcomes, molecular subtyping, and drug sensitivity in lung adenocarcinoma

OBJECTIVES

Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.

METHODS

Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed an immune-derived multiple programmed cell death index (MPCDI) based on machine learning methods.

RESULTS

Using the median MPCDI scores, we categorized the LUAD patients into two groups: low-MPCDI and high-MPCDI. Our analysis of the TCGA-LUAD training cohort and three external GEO cohorts (GSE37745, GSE30219, and GSE68465) revealed that patients with high-MPCDI experienced a more unfavorable prognosis, whereas those with low-MPCDI had a better prognosis. Furthermore, the results of both univariate and multivariate Cox regression analyses further confirmed that MPCDI serves as a novel independent risk factor. By combining clinical characteristics with the MPCDI, we constructed a nomogram that provides an accurate and reliable quantitative tool for personalized clinical management of LUAD patients. The findings obtained from the analysis of C-index and the decision curve revealed that the nomogram outperformed various clinical variables in terms of net clinical benefit. Encouragingly, the low-MPCDI patients are more sensitive to commonly used chemotherapy drugs, which suggests that MPCDI scores have a guiding role in chemotherapy for LUAD patients.

CONCLUSION

Therefore, MPCDI can be used as a novel clinical diagnostic classifier, providing valuable insights into the clinical management and clinical decision-making for LUAD patients.

Screening of genes related to programmed cell death in esophageal squamous cell carcinoma and construction of prognostic model based on transcriptome analysis

OBJECTIVES

To screen programmed cell death (PCD)-related genes in esophageal squamous cell carcinoma (ESCC) based on transcriptomic data and to explore its clinical value.

METHODS

Differentially expressed PCD genes (DEPCDGs) were screened from ESCC transcriptome and clinical data in TCGA database. Univariate COX and LASSO COX were performed on prognostically DEPCDGs in ESCC to develop prognostic model. Differences in immune cell infiltration in different RiskScore groups were determined by ssGSEA and CIBERSORT. The role of RiskScore in immunotherapy response was explored using Tumor Immune Dysfunction and Exclusion (TIDE) and IMvigor210 cohorts.

RESULTS

Fourteen DEPCDGs associated with prognosis were tapped in ESCC. These DEPCDGs form a RiskScore with good predictive performance for prognosis. RiskScore demonstrated excellent prediction accuracy in three data sets. The abundance of M2 macrophages and Tregs was higher in the high RiskScore group, and the abundance of M1 macrophages was higher in the low RiskScore group. The RiskScore also showed good immunotherapy sensitivity. RT-qPCR analysis showed that AUP1, BCAP31, DYRK2, TAF9 and UBQLN2 were higher expression in KYSE-150 cells. Knockdown BCAP31 inhibited migration and invasion.

CONCLUSION

A prognostic risk model can predict prognosis of ESCC and may be a useful biomarker for risk stratification and immunotherapy assessment.

Prediction of clinical prognosis and drug sensitivity in hepatocellular carcinoma through the combination of multiple cell death pathways

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

Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning

Lumbar disc herniation (LDH) is a common clinical spinal disorder, yet its etiology remains unclear. We aimed to explore the role of cuproptosis-related genes (CRGs) and identify potential diagnostic biomarkers. Our analysis involved interrogating the GSE124272 and GSE150408 datasets for differential gene expression profiles associated with CRGs and immune characteristics. Molecular clustering was performed on LDH samples, followed by expression and immune infiltration analyses. Using the WGCNA algorithm, specific genes within CRG clusters were identified. After selecting the most predictive genes from the optimal model, four machine learning models were constructed and validated. This study identified nine CRGs associated with copper-regulated cell death. Two copper-containing molecular clusters linked to death were detected in LDH samples. Elevated expression and immune infiltration levels were found in LDH patients, particularly in CRG cluster C2. Utilizing XGB, five genes were identified for constructing a diagnostic model, achieving an area under the curve values of 0.715. In conclusion, this research provides valuable insights into the association between LDH and copper-regulated cell death, alongside proposing a promising predictive model.

Revisiting the potential of regulated cell death in glioma treatment: a focus on autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis, immunogenic cell death, and the crosstalk between them

Gliomas are primary tumors that originate in the central nervous system. The conventional treatment options for gliomas typically encompass surgical resection and temozolomide (TMZ) chemotherapy. However, despite aggressive interventions, the median survival for glioma patients is merely about 14.6 months. Consequently, there is an urgent necessity to explore innovative therapeutic strategies for treating glioma. The foundational study of regulated cell death (RCD) can be traced back to Karl Vogt's seminal observations of cellular demise in toads, which were documented in 1842. In the past decade, the Nomenclature Committee on Cell Death (NCCD) has systematically classified and delineated various forms and mechanisms of cell death, synthesizing morphological, biochemical, and functional characteristics. Cell death primarily manifests in two forms: accidental cell death (ACD), which is caused by external factors such as physical, chemical, or mechanical disruptions; and RCD, a gene-directed intrinsic process that coordinates an orderly cellular demise in response to both physiological and pathological cues. Advancements in our understanding of RCD have shed light on the manipulation of cell death modulation - either through induction or suppression - as a potentially groundbreaking approach in oncology, holding significant promise. However, obstacles persist at the interface of research and clinical application, with significant impediments encountered in translating to therapeutic modalities. It is increasingly apparent that an integrative examination of the molecular underpinnings of cell death is imperative for advancing the field, particularly within the framework of inter-pathway functional synergy. In this review, we provide an overview of various forms of RCD, including autophagy-dependent cell death, anoikis, ferroptosis, cuproptosis, pyroptosis and immunogenic cell death. We summarize the latest advancements in understanding the molecular mechanisms that regulate RCD in glioma and explore the interconnections between different cell death processes. By comprehending these connections and developing targeted strategies, we have the potential to enhance glioma therapy through manipulation of RCD.

Predicting prognosis and drug sensitivity in bladder cancer: an insight into Pan-programmed cell death patterns regulated by M6A modifications

The team aimed to explore the possible functional significance of M6A regulation in Pan-programmed cell death (PCD) among patients with bladder cancer (BLCA). In BLCA patients, the analysis was conducted on the13 patterns of programmed cell death (PCD) and the regulation of M6A. Transcriptome, genomics, and clinical data were collected from TCGA-BLCA, GEO32548, and IMvigor210. Consensus clustering analysis, functional enrichment analysis, and other prognostic tools were used to validate the Pan-PCD. Finally, in vitro experiments and transcription sequencing were performed to understand the potential influence of the PI3K pathway on Pan-PCD in BLCA patients. Diverse PCD patterns were simultaneously activated, and M6A regulators exhibited significant variability in bladder malignant tissues. The machine learning algorithm established an 8-gene M6A-related Pan-PCD signature. This signature was validated in three independent datasets, and BLCA patients with higher risk scores had worse prognosis. An unsupervised clustering approach identified activated and suppressed Pan-PCD subgroups of BLCA patients, with distinct responses to immunotherapy and drug sensitivity. In addition, the PI3K pathway was identified as a key mechanism for various forms of programmed cell death, encompassing apoptosis, pyroptosis, autophagy, and cell death dependent on lysosomes. This research revealed that the Pan-PCD model was a more promising approach for BLCA patients under M6A regulation. A new signature from M6A-related Pan-PCD was proposed, with prognostic value for survival or drug sensitivity. The PI3K pathway was a key mechanism for multiple PCDs in BLCA patients.

An Investigation into the In Vitro Targeted Killing of CD44-Expressing Triple-Negative Breast Cancer Cells Using Recombinant Photoimmunotherapeutics Compared to Auristatin-F-Based Antibody-Drug Conjugates

Triple-negative breast cancer (TNBC) is the deadliest form of breast cancer with limited treatment options. The persistence of highly tumorigenic CD44-expressing subpopulation referred to as cancer stem cells (CSCs), endowed with the self-renewal capacity, has been associated with therapeutic resistance, hence clinical relapses. To mitigate these undesired events, targeted immunotherapies using antibody-photoconjugate (APC) or antibody-drug conjugate (ADC), were developed to specifically release cytotoxic payloads within targeted cells overexpressing cognate antigen receptors. Therefore, an αCD44(scFv)-SNAP-tag antibody fusion protein was engineered through genetic fusion of a single-chain antibody fragment (scFv) to a SNAPf-tag fusion protein, capable of self-conjugating with benzylguanine-modified light-sensitive near-infrared (NIR) phthalocyanine dye IRDye700DX (BG-IR700) or the small molecule toxin auristatin-F (BG-AURIF). Binding of the αCD44(scFv)-SNAPf-IR700 photoimmunoconjugate to antigen-positive cells was demonstrated by confocal microscopy and flow cytometry. By switching to NIR irradiation, CD44-expressing TNBC was selectively killed through induced phototoxic activities. Likewise, the αCD44(scFv)-SNAPf-AURIF immunoconjugate was able to selectively accumulate within targeted cells and significantly reduced cell viability through antimitotic activities at nano- to micromolar drug concentrations. This study provides an in vitro proof-of-concept for a future strategy to selectively destroy light-accessible superficial CD44-expressing TNBC tumors and their metastatic lesions which are inaccessible to therapeutic light.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and drug response. We utilized 13 PCD pathways, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis to develop predictive models based on 73 machine learning combinations from 10 algorithms. Bulk RNA-sequencing, single-cell RNA-sequencing transcriptomic data, and matched clinicopathological information were obtained from the TCGA-AML, Tyner, and GSE37642-GPL96 cohorts. These datasets were leveraged to construct and validate the models. Additionally, in vitro experiments were conducted to substantiate the bioinformatics findings. The machine learning approach established a 6-gene pan-programmed cell death-related genes index (PPCDI) signature. Validation in two external cohorts showed high PPCDI associated with worse prognosis in AML patients. Incorporating PPCDI with clinical variables, we constructed several robust prognostic nomograms that accurately predicted prognosis of AML patients. Multi-omics analysis integrating bulk and single-cell transcriptomics revealed correlations between PPCDI and immunological features, delineating the immune microenvironment landscape in AML. Patients with high PPCDI exhibited resistance to conventional chemotherapy like doxorubicin but retained sensitivity to dasatinib and methotrexate (FDA-approved drugs for other leukemias), suggesting the potential of PPCDI to guide personalized therapy selection in AML. In summary, we developed a novel PPCDI model through comprehensive analysis of diverse programmed cell death pathways. This PPCDI signature demonstrates great potential in predicting clinical prognosis and drug sensitivity phenotypes in AML patients.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.

Gene signature developed based on programmed cell death to predict the therapeutic response and prognosis for liver hepatocellular carcinoma

Background

The prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC.

Methods

Molecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed.

Results

PCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1-C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil.

Conclusion

A PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.

Bioinformatics analysis of the role of lysosome-related genes in breast cancer

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

Construction of a TAN-associated risk score model with integrated multi-omics data analysis and clinical validation in gastric cancer

AIMS

An increasing number of studies have highlighted the biological significance of neutrophil activation and polarization in tumor progression. However, the characterization of tumor-associated neutrophils (TANs) is inadequately investigated.

MATERIALS AND METHODS

Patients' expression profiles were obtained from TCGA, GEO, and IMvigor210 databases. Six algorithms were used to assess immune cell infiltration. RNA sequencing was conducted to evaluate the differentially expressed genes between induced N1- and N2-like neutrophils. A TAN-associated risk score (TRS) model was established using a combination of weighted gene co-expression network analysis (WGCNA) and RNA-seq data and further assessed in pan-cancer. A clinical cohort of 117 GC patients was enrolled to assess the role of TANs in GC via immunohistochemistry (IHC).

KEY FINDINGS

A TRS signature was built with 10 TAN-related genes (TRGs) and most TRGs were highly abundant in the TANs of the GC microenvironment. The TRS model could accurately predict patients' prognosis, as well as their responses to chemotherapy and immunotherapy. The TRS was positively correlated with pro-tumor immune cells and exhibited negative relationship with anti-tumor immune cells. Additional functional analyses revealed that the signature was positively related to pro-tumor and immunosuppression pathways, such as the hypoxia pathway, across pan-cancer. Furthermore, our clinical cohort demonstrated TANs as an independent prognostic factor for GC patients.

SIGNIFICANCE

This study constructed and confirmed the value of a novel TRS model for prognostic prediction of GC and pan-cancer. Further evaluation of TRS and TANs will help strengthen the understanding of the tumor microenvironment and guide more effective therapeutic strategies.

Molecular insights into programmed cell death in esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a deadly type of esophageal cancer. Programmed cell death (PCD) is an important pathway of cellular self-extermination and is closely involved in cancer progression. A detailed study of its mechanism may contribute to ESCC treatment.

Methods

We obtained expression profiling data of ESCC patients from public databases and genes related to 12 types of PCD from previous studies. Hub genes in ESCC were screened from PCD-related genes applying differential expression analysis, machine learning analysis, linear support vector machine (SVM), random forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. In addition, based on the HTFtarget and TargetScan databases, transcription factors (TFs) and miRNAs interacting with the hub genes were selected. The relationship between hub genes and immune cells were analyzed using the CIBERSORT algorithm. Finally, to verify the potential impact of the screened hub genes on ESCC occurrence and development, a series of in vitro cell experiments were conducted.

Results

We screened 149 PCD-related DEGs, of which five DEGs (INHBA, LRRK2, HSP90AA1, HSPB8, and EIF2AK2) were identified as the hub genes of ESCC. The area under the curve (AUC) of receiver operating characteristic (ROC) curve of the integrated model developed using the hub genes reached 0.997, showing a noticeably high diagnostic accuracy. The number of TFs and miRNAs regulating hub genes was 105 and 22, respectively. INHBA, HSP90AA1 and EIF2AK2 were overexpressed in cancer tissues and cells of ESCC. Notably, INHBA knockdown suppressed ECSS cell migration and invasion and altered the expression of important apoptotic and survival proteins.

Conclusion

This study identified significant molecules with promising accuracy for the diagnosis of ESCC, which may provide a new perspective and experimental basis for ESCC research.

RNA modification gene WDR4 facilitates tumor progression and immunotherapy resistance in breast cancer

INTRODUCTION

Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking.

OBJECTIVES

This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients.

METHODS

Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining.

RESULTS

This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification.

CONCLUSION

Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma

BACKGROUND

Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies.

OBJECTIVE

We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells.

METHODS

The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells.

RESULTS

A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1.

CONCLUSION

The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

A novel prognostic signature related to programmed cell death in osteosarcoma

Background

Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.

Method

We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.

Result

In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.

Conclusion

The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

Spindle apparatus coiled-coil protein 1 (SPDL1) serves as a novel prognostic biomarker in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) has a poor prognosis, an ineffective diagnosis, and a high degree of aggressiveness. Therefore, novel therapeutic targets for TNBC urgently need to be identified.

METHODS

Through a series of bioinformatics analyses, including analysis of differential gene expression, protein-protein interaction (PPI) network, univariate cox regression, immune infiltration, pathway enrichment, etc, as well as auxiliary immunohistochemistry (IHC) and protein quantitativae analysis, to explore prognostic marker for TNBC.

RESULTS

In TNBC tissues, we found that SPDL1 (CCDC99) was considerably overexpressed at both the mRNA and protein levels compared to that in normal and non-TNBC tissues. Additionally, we found that SPDL1-high expression was strongly linked to poor prognosis in TNBC patients. Excessive SPDL1 expression was positively correlated with tumor growth and strongly linked to the cell cycle, DNA replication, and the p53 signaling pathway. In addition, CIBERSORT analysis revealed that SPDL1 can affect the tumor immune microenvironment (TME) in TNBC, encourage the development of TNBC and act as a potential prognostic biomarker for TNBC. Patients with SPDL1-high expression were more sensitive to AZD8055. Notably, we discovered that SPDL1 is highly expressed in the majority of malignancies and may have an impact on the pancancer prognosis.

CONCLUSIONS

SPDL1 can serve as a novel prognostic marker for TNBC and pancancer patients.

Therapeutic strategies of targeting non-apoptotic regulated cell death (RCD) with small-molecule compounds in cancer

Regulated cell death (RCD) is a controlled form of cell death orchestrated by one or more cascading signaling pathways, making it amenable to pharmacological intervention. RCD subroutines can be categorized as apoptotic or non-apoptotic and play essential roles in maintaining homeostasis, facilitating development, and modulating immunity. Accumulating evidence has recently revealed that RCD evasion is frequently the primary cause of tumor survival. Several non-apoptotic RCD subroutines have garnered attention as promising cancer therapies due to their ability to induce tumor regression and prevent relapse, comparable to apoptosis. Moreover, they offer potential solutions for overcoming the acquired resistance of tumors toward apoptotic drugs. With an increasing understanding of the underlying mechanisms governing these non-apoptotic RCD subroutines, a growing number of small-molecule compounds targeting single or multiple pathways have been discovered, providing novel strategies for current cancer therapy. In this review, we comprehensively summarized the current regulatory mechanisms of the emerging non-apoptotic RCD subroutines, mainly including autophagy-dependent cell death, ferroptosis, cuproptosis, disulfidptosis, necroptosis, pyroptosis, alkaliptosis, oxeiptosis, parthanatos, mitochondrial permeability transition (MPT)-driven necrosis, entotic cell death, NETotic cell death, lysosome-dependent cell death, and immunogenic cell death (ICD). Furthermore, we focused on discussing the pharmacological regulatory mechanisms of related small-molecule compounds. In brief, these insightful findings may provide valuable guidance for investigating individual or collaborative targeting approaches towards different RCD subroutines, ultimately driving the discovery of novel small-molecule compounds that target RCD and significantly enhance future cancer therapeutics.

Identification of disulfidptosis-related clusters and construction of a disulfidptosis-related gene prognostic signature in triple-negative breast cancer

Objective

This study aimed to explore disulfidptosis-related clusters of triple-negative breast cancer (TNBC) and build a reliable disulfidptosis-related gene signature for forecasting TNBC prognosis.

Methods

The disulfidptosis-related clusters of TNBC were identified based on public datasets, and a comparative analysis was conducted to assess their differences in the overall survival (OS) and immune cell infiltration. Morever, the differentially expressed genes (DEGs) between clusters were recognized. Then, the prognostic DEGs were then chosen. A prognostic signature was constructed by the prognostic DEGs, followed by nomogram construction, drug sensitivity, immune correlation, immunotherapy response prediction, and cluster association analyses.

Results

Two disulfidptosis-related clusters of TNBC were identified, which had different OS and macrophage infiltration. Moreover, 235 DEGs were identified between two clusters. A prognostic signature was then constructed by five prognostic DEGs including HLA-DQA2, CCL13, GBP1, LAMP3, and SLC7A11. This signature was highly valuable in predicting prognosis. A nomogram was built by risk score and AJCC stage, which could forecast OS accurately. Moreover, patients with high-risk scores exhibited greater sensitivity to chemotherapy drugs such as lapatinib and had a lower immunotherapy response.

Conclusions

Two TNBC clusters linked to disulfidptosis were identified, with different OS and immune cell infiltration. Moreover, a five-disulfidptosis-related gene signature may be a powerful prognostic biomarker for TNBC.

Single-nucleus transcriptome unveils the role of ferroptosis in ischemic stroke

Multiple cell death pathways are involved in neuronal death in ischemic stroke (IS). However, the role of different cell death pathways in different cell types has not been elucidated. By analyzing three single-nucleus RNA sequencing (snRNA-seq) data of IS, we first found that a variety of programmed cell death (PCD) -related genes were significantly changed in different cell types. Based on machine learning and virtual gene knockout, we found that ferroptosis related genes, ferritin heavy chain 1 (Fth1) and ferritin light chain (Ftl1), play a key role in IS. Ftl1 and Fth1 can promote microglia activation, as well as the production of inflammatory factors and chemokines. Cell communication analysis showed that activated microglia could enhance chemotactic peripheral leukocyte infiltration, such as macrophages and neutrophils, through Spp1-Cd44 and App-Cd74 signaling, thereby aggravating brain tissue damage. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) showed that P2ry12 and Mef2c were significantly decreased in oxygen-glucose deprivation (OGD) group, while Ftl1, Fth1, Apoe, Ctsb, Cd44 and Cd74 were significantly increased in OGD group. Collectively, our findings suggested targeted therapy against microglia Ftl1 and Fth1 might improve the state of microglia, reduce the infiltration of peripheral immune cells and tissue inflammation, and then improve the ischemic brain injury in mouse.

Single-cell RNA sequencing elucidated the landscape of breast cancer brain metastases and identified ILF2 as a potential therapeutic target

Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single-cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi-omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer-binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM.

SPACA6P-AS: a trailblazer in breast cancer pathobiology and therapeutics

OBJECTIVE

The primary objective of this investigation is to delve into the involvement of the long noncoding RNA (lncRNA) SPACA6P-AS in breast cancer (BC) development, focusing on its expression pattern, association with clinical-pathological features, impact on prognosis, as well as its molecular and immunological implications.

METHODS

Bioinformatics analysis was conducted utilizing RNA sequencing data of 1083 BC patients from the TCGA database. Functional exploration of SPACA6P-AS was carried out through the construction of survival curves, GO and KEGG enrichment analysis, and single-sample gene set enrichment analysis (ssGSEA). Furthermore, its functionality was validated through in vitro cell experiments and in vivo nude mouse model experiments.

RESULTS

SPACA6P-AS showed a remarkable increase in expression levels in BC tissues (p < 0.001) and demonstrated a close relationship to poor prognosis (overall survival HR = 1.616, progression-free interval HR = 1.40, disease-specific survival HR = 1.54). Enrichment analysis revealed that SPACA6P-AS could impact biological functions such as protease regulation, endopeptidase inhibitor activity, taste receptor activity, taste transduction, and maturity-onset diabetes of the young pathway. ssGSEA analysis indicated a negative correlation between SPACA6P-AS expression and immune cell infiltration like dendritic cells and neutrophils, while a positive correlation was observed with central memory T cells and T helper 2 cells. Results from in vitro and in vivo experiments illustrated that silencing SPACA6P-AS significantly inhibited the proliferation, migration, and invasion capabilities of BC cells. In vitro experiments also highlighted that dendritic cells with silenced SPACA6P-AS exhibited enhanced capabilities in promoting the proliferation of autologous CD3 + T cells and cytokine secretion. These discoveries elucidate the potential multifaceted roles of SPACA6P-AS in BC, including its potential involvement in modulating immune cell infiltration in the tumor microenvironment.

CONCLUSION

The high expression of lncRNA SPACA6P-AS in BC is closely linked to poor prognosis and may facilitate tumor progression by influencing specific biological processes, signaling pathways, and the immune microenvironment. The regulatory role of SPACA6P-AS positions it as a prospective biomarker and target for therapeutic approaches for BC diagnosis and intervention.

A new perspective: deciphering the aberrance and clinical implication of disulfidptosis signatures in clear cell renal cell carcinoma

Recent research has discovered disulfidptosis as a form of programmed cell death characterized by disulfide stress. However, its significance in clear cell renal cell carcinoma (ccRCC) remains unclear. To investigate this, data from The Cancer Genome Atlas were collected and used to identify ccRCC subgroups. Unsupervised clustering was employed to determine ccRCC heterogeneity. The mutation landscape and immune microenvironment of the subgroups were analyzed. The Disulfidptosis-Related Score was calculated using the LASSO-penalized Cox regression algorithm. The E-MATB-1980 cohort was used to validate the signature. The role of SLC7A11 in ccRCC metastasis was explored using western blotting and Transwell assays. Disulfidptosis-related genes are commonly downregulated in cancers and are linked to hypermethylation and copy number variation. The study revealed that ccRCC is divided into two sub-clusters: the disulfidptosis-desert sub-cluster, which is associated with a poor prognosis, a higher mutation frequency, and an immunosuppressive microenvironment. A 14-gene prognostic model was developed using differentially expressed genes and was validated in the E-MATB-1980 cohort. The low-risk group demonstrated longer overall and disease-free survival and responded better to targeted immunotherapy. Results from in vitro experiments identified SLC7A11 as a key participant in ccRCC metastasis.

The landscape of programmed cell death-related lncRNAs in Alzheimer's disease and Parkinson's disease

This study delivers a thorough analysis of long non-coding RNAs (lncRNAs) in regulating programmed cell death (PCD), vital for neurodegenerative diseases like Alzheimer's disease (AD) and Parkinson's disease (PD). We propose a new framework PCDLnc, and identified 20 significant lncRNAs, including HEIH, SNHG15, and SNHG5, associated with PCD gene sets, which were known for roles in proliferation and apoptosis in neurodegenerative diseases. By using GREAT software, we identified regulatory functions of top lncRNAs in different neurodegenerative diseases. Moreover, lncRNAs cis-regulated mRNAs linked to neurodegeneration, including JAK2, AKT1, EGFR, CDC42, SNCA, and ADIPOQ, highlighting their therapeutic potential in neurodegenerative diseases. A further exploration into the differential expression of mRNA identified by PCDLnc revealed a role in apoptosis, ferroptosis and autophagy. Additionally, protein-protein interaction (PPI) network analysis exposed abnormal interactions among key genes, despite their consistent expression levels between disease and normal samples. The randomforest model effectively distinguished between disease samples, indicating a high level of accuracy. Shared gene subsets in AD and PD might serve as potential biomarkers, along with disease-specific gene sets. Besides, we also found the strong relationship between AD and immune infiltration. This research highlights the role of lncRNAs and their associated genes in PCD in neurodegenerative diseases, offering potential therapeutic targets and diagnostic markers for future study and clinical application.

Crosstalk of different cell-death patterns predicts prognosis and drug sensitivity in glioma

BACKGROUND

Glioma is a malignant brain tumor originating from glial cells, and there still a challenge to accurately predict the prognosis. Programmed cell death (PCD) plays a key role in tumorigenesis and immune response. However, the crosstalk and potential role of various PCDs in prognosis and tumor microenvironment remains unknown. Therefore, we comprehensively discussed the relationship between different models of PCD and the prognosis of glioma and provided new ideas for the optimal targeted therapy of glioma.

MATERIALS AND METHODS

We compared and analyzed the role of 14 PCD patterns on the prognosis from different levels. We constructed the cell death risk score (CDRS) index and conducted a comprehensive analysis of CDRS and TME characteristics, clinical characteristics, and drug response.

RESULTS

Effects of different PCDs at the genomic, functional, and immune microenvironment levels were discussed. CDRS index containing 6 gene signatures and a nomogram were established. High CDRS is associated with a worse prognosis. Through transcriptome and single-cell data, we found that patients with high CDRS showed stronger immunosuppressive characteristics. Moreover, the high-CDRS group was resistant to the traditional glioma chemotherapy drug Vincristine, but more sensitive to the Temozolomide and the clinical experimental drug Bortezomib. In addition, we identified 19 key potential therapeutic targets during malignant differentiation of tumor cells.

CONCLUSION

Overall, we provide the first systematic description of the role of 14 PCDs in glioma. A new CDRS model was built to predict the prognosis and to provide a new idea for the targeted therapy of glioma.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine

Background

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC.

Methods

Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation.

Results

Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts.

Conclusion

This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

Analyzing the involvement of diverse cell death-related genes in diffuse large B-cell lymphoma using bioinformatics techniques

Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin's lymphoma and exhibits significant heterogeneity. Various forms of programmed cell death (PCD) have been established to have close associations with tumor onset and progression. To this end, this study has compiled 16 PCD-related genes. The investigation delved into genes linked with prognosis, constructing risk models through consecutive application of univariate Cox regression analysis and Lasso-Cox regression analysis. Furthermore, we employed RT-qPCR to validate the mRNA expression levels of certain diagnosis-related genes. Subsequently, the models underwent validation through KM survival curves and ROC curves, respectively. Additionally, nomogram models were formulated employing prognosis-related genes and risk scores. Lastly, disparities in immune cell infiltration abundance and the expression of immune checkpoint-associated genes between high- and low-risk groups, as classified by risk models, were explored. These findings contribute to a more comprehensive understanding of the role played by the 16 PCD-associated genes in DLBCL, shedding light on potential novel therapeutic strategies for the condition.

Identification and clinical validation of diverse cell-death patterns-associated prognostic features among low-grade gliomas

Low-grade glioma (LGG) is heterogeneous at biological and transcriptomic levels, and it is still controversial for the definition and typing of LGG. Therefore, there is an urgent need for specific and practical molecular signatures for accurate diagnosis, individualized therapy, and prognostic evaluation of LGG. Cell death is essential for maintaining homeostasis, developing and preventing hyperproliferative malignancies. Based on diverse programmed cell death (PCD) related genes and prognostic characteristics of LGG, this study constructed a model to explore the mechanism and treatment strategies for LGG cell metastasis and invasion. We screened 1161 genes associated with PCD and divided 512 LGG samples into C1 and C2 subtypes by consistent cluster analysis. We analyzed the two subtypes' differentially expressed genes (DEGs) and performed functional enrichment analysis. Using R packages such as ESTIMATE, CIBERSOTR, and MCPcounter, we assessed immune cell scores for both subtypes. Compared with C1, the C2 subtype has a poor prognosis and a higher immune score, and patients in the C2 subtype are more strongly associated with tumor progression. LASSO and COX regression analysis screened four characteristic genes (CLU, FHL3, GIMAP2, and HVCN1). Using data sets from different platforms to validate the four-gene feature, we found that the expression and prognostic correlation of the four-gene feature had a high degree of stability, showing stable predictive effects. Besides, we found downregulation of CLU, FHL3, and GIMAP2 significantly impairs the growth, migration, and invasive potential of LGG cells. Take together, the four-gene feature constructed based on PCD-related genes provides valuable information for further study of the pathogenesis and clinical treatment of LGG.

Targeting PRKDC activates the efficacy of antitumor immunity while sensitizing to chemotherapy and targeted therapy in liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) ranks among the malignancies with the highest mortality rates, primarily due to chemoresistance culminating in treatment failure. Despite its impact, predictive models addressing disease progression, tumor microenvironment, and drug sensitivity remain elusive for LIHC patients. Recognizing the significant influence of various programmed cell death (PCD) modes on tumor evolution, this study investigates PCD genes to elucidate their implications on the prognosis and immune landscape of LIHC.

METHODS

To develop the classification and model, we employed a total of 17 genes associated with PCD patterns. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database, specifically from LIHC patients. Moreover, we obtained spatial transcriptome data and additional bulk datasets from the Gene Expression Omnibus (GEO) database to conduct further analysis. Various experiments were conducted to validate the role of the PCD gene PRKDC in proliferation, migration, invasion, EMT, cell cycle, therapeutic sensitivity, and antitumor immunity.

RESULTS

A novel LIHC classification based on these genes divided patients into two clusters, C1 and C2. The C2 cluster exhibited characteristics indicative of poor prognosis and an immune-activated microenvironment. This group showed greater response potential to immune checkpoint inhibitors, displaying higher levels of certain immune signatures and receptors. A programmed cell death index (PCDI) was constructed using 17 selected PCD genes. This index could effectively predict patient prognosis, with higher PCDI indicating poorer survival rates and a more pro-tumor microenvironment. Immune landscapes revealed varying interactions with PCDI, suggesting therapeutic targets and insights into treatment resistance. Moreover, experiments results suggested that PRKDC can augment the invasive nature and growth of malignant cells and it can serve as a potential target for therapy, offering hope for ameliorating the prognosis of LIHC patients.

CONCLUSIONS

The study uncovers the insights of programmed cell death in the prognosis and potential therapeutic interventions. And we found that PRKDC can serve as a target for enhancing the efficacy of antitumor immunity while sensitizing chemotherapy and targeted therapy in liver hepatocellular carcinoma.

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BACKGROUND

Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.

METHODS

This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.

RESULTS

Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.

CONCLUSIONS

ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Assessing the role of programmed cell death signatures and related gene TOP2A in progression and prognostic prediction of clear cell renal cell carcinoma

Kidney Clear Cell Carcinoma (KIRC), the predominant form of kidney cancer, exhibits a diverse therapeutic response to Immune Checkpoint Inhibitors (ICIs), highlighting the need for predictive models of ICI efficacy. Our study has constructed a prognostic model based on 13 types of Programmed Cell Death (PCD), which are intertwined with tumor progression and the immune microenvironment. Validated by analyses of comprehensive datasets, this model identifies seven key PCD genes that delineate two subtypes with distinct immune profiles and sensitivities to anti-PD-1 therapy. The high-PCD group demonstrates a more immune-suppressive environment, while the low-PCD group shows better responses to PD-1 treatment. In particular, TOP2A emerged as crucial, with its inhibition markedly reducing KIRC cell growth and mobility. These findings underscore the relevance of PCDs in predicting KIRC outcomes and immunotherapy response, with implications for enhancing clinical decision-making.

Revealing prognostic insights of programmed cell death (PCD)-associated genes in advanced non-small cell lung cancer

The management of patients with advanced non-small cell lung cancer (NSCLC) presents significant challenges due to cancer cells' intricate and heterogeneous nature. Programmed cell death (PCD) pathways are crucial in diverse biological processes. Nevertheless, the prognostic significance of cell death in NSCLC remains incompletely understood. Our study aims to investigate the prognostic importance of PCD genes and their ability to precisely stratify and evaluate the survival outcomes of patients with advanced NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate Cox regression analyses for prognostic gene screening. Ultimately, we identified seven PCD-related genes to establish the PCD-related risk score for the advanced NSCLC model (PRAN), effectively stratifying overall survival (OS) in patients with advanced NSCLC. Multivariate Cox regression analysis revealed that the PRAN was the independent prognostic factor than clinical baseline factors. It was positively related to specific metabolic pathways, including hexosamine biosynthesis pathways, which play crucial roles in reprogramming cancer cell metabolism. Furthermore, drug prediction for different PRAN risk groups identified several sensitive drugs explicitly targeting the cell death pathway. Molecular docking analysis suggested the potential therapeutic efficacy of navitoclax in NSCLC, as it demonstrated strong binding with the amino acid residues of C-C motif chemokine ligand 14 (CCL14), carboxypeptidase A3 (CPA3), and C-X3-C motif chemokine receptor 1 (CX3CR1) proteins. The PRAN provides a robust personalized treatment and survival assessment tool in advanced NSCLC patients. Furthermore, identifying sensitive drugs for distinct PRAN risk groups holds promise for advancing targeted therapies in NSCLC.

Constructing a novel prognostic model for triple-negative breast cancer based on genes associated with vasculogenic mimicry

BACKGROUND

Research has shown a connection between vasculogenic mimicry (VM) and cancer progression. However, the functions of genes related to VM in the emergence and progression of TNBC have not been completely elucidated.

METHODS

A survival risk model was constructed by screening biomarkers using DESeq2 and WGCNA based on public TNBC transcriptome data. Furthermore, gene set enrichment analysis was performed, and tumor microenvironment and drug sensitivity were analyzed. The selected biomarkers were validated via quantitative PCR detection, immunohistochemical staining, and protein detection in breast cancer cell lines. Biomarkers related to the proliferation and migration of TNBC cells were validated via in vitro experiments.

RESULTS

The findings revealed that 235 target genes were connected to the complement and coagulation cascade pathways. The risk score was constructed using KCND2, NRP1, and VSTM4. The prognosis model using the risk score and pathological T stage yielded good validation results. The clinical risk of TNBC was associated with the angiogenesis signaling pathway, and the low-risk group exhibited better sensitivity to immunotherapy. Quantitative PCR and immunohistochemistry indicated that the expression levels of KCND2 in TNBC tissues were higher than those in adjacent nontumor tissues. In the TNBC cell line, the protein expression of KCND2 was increased. Knockdown of KCND2 and VSTM4 inhibited the proliferation and migration of TNBC cells in vitro.

CONCLUSIONS

In this study, three VM-related biomarkers were identified, including KCND2, NRP1, and VSTM4. These findings are likely to aid in deepening our understanding of the regulatory mechanism of VM in TNBC.

Comprehensive bioinformatics analytics and in vivo validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction

BACKGROUND

Globally, Acute Myocardial Infarction (AMI) is a common cause of heart failure (HF), which has been a leading cause of mortality resulting from non-communicable diseases. On the other hand, increasing evidence suggests that the role of energy production within the mitochondria strongly links to the development and progression of heart diseases, while Cuproptosis, a newly identified cell death mechanism, has not yet been comprehensively analyzed from the aspect of cardiovascular medicine.

MATERIALS AND METHODS

8 transcriptome profiles curated from the GEO database were integrated, from which a diagnostic model based on the Stacking algorithm was established. The efficacy of the model was evaluated in a multifaced manner (i.e., by Precision-Recall curve, Receiver Operative Characteristic curve, etc.). We also sequenced our animal models at the bulk RNA level and conducted qPCR and immunohistochemical staining, with which we further validated the expression of the key contributor gene to the model. Finally, we explored the immune implications of the key contributor gene.

RESULTS

A merged machine learning model containing 4 Cuproptosis-related genes (i.e., PDHB, CDKN2A, GLS, and SLC31A1) for robust AMI diagnosis was developed, in which SLC31A1 served as the key contributor. Through in vivo modeling, we validated the aberrant overexpression of SLC31A1 in AMI. Besides, further transcriptome analysis revealed that its high expression was correlated with significant potential immunological implications in the infiltration of many immune cell types, especially monocyte.

CONCLUSIONS

We constructed an AMI diagnostic model based on Cuproptosis-related genes and validated the key contributor gene in animal modeling. We also analyzed the effects on the immune system for its overexpression in AMI.

Irradiated microparticles suppress prostate cancer by tumor microenvironment reprogramming and ferroptosis

Immunogenic cell death (ICD) plays a crucial role in triggering the antitumor immune response in the tumor microenvironment (TME). Recently, considerable attention has been dedicated to ferroptosis, a type of ICD that is induced by intracellular iron and has been demonstrated to change the immune desert status of the TME. However, among cancers that are characterized by an immune desert, such as prostate cancer, strategies for inducing high levels of ferroptosis remain limited. Radiated tumor cell-derived microparticles (RMPs) are radiotherapy mimetics that have been shown to activate the cGAS-STING pathway, induce tumor cell ferroptosis, and inhibit M2 macrophage polarization. RMPs can also act as carriers of agents with biocompatibility. In the present study, we designed a therapeutic system wherein the ferroptosis inducer RSL-3 was loaded into RMPs, which were tested in in vitro and in vivo prostate carcinoma models established using RM-1 cells. The apoptosis inducer CT20 peptide (CT20p) was also added to the RMPs to aggravate ferroptosis. Our results showed that RSL-3- and CT20p-loaded RMPs (RC@RMPs) led to ferroptosis and apoptosis of RM-1 cells. Moreover, CT20p had a synergistic effect on ferroptosis by promoting reactive oxygen species (ROS) production, lipid hydroperoxide production, and mitochondrial instability. RC@RMPs elevated dendritic cell (DC) expression of MHCII, CD80, and CD86 and facilitated M1 macrophage polarization. In a subcutaneously transplanted RM-1 tumor model in mice, RC@RMPs inhibited tumor growth and prolonged survival time via DC activation, macrophage reprogramming, enhancement of CD8+ T cell infiltration, and proinflammatory cytokine production in the tumor. Moreover, combination treatment with anti-PD-1 improved RM-1 tumor inhibition. This study provides a strategy for the synergistic enhancement of ferroptosis for prostate cancer immunotherapies.

Decoding the molecular landscape: A novel prognostic signature for uveal melanoma unveiled through programmed cell death-associated genes

Uveal melanoma (UM) is a rare but aggressive malignant ocular tumor with a high metastatic potential and limited therapeutic options, currently lacking accurate prognostic predictors and effective individualized treatment strategies. Public databases were utilized to analyze the prognostic relevance of programmed cell death-related genes (PCDRGs) in UM transcriptomes and survival data. Consensus clustering and Lasso Cox regression analysis were performed for molecular subtyping and risk feature construction. The PCDRG-derived index (PCDI) was evaluated for its association with clinicopathological features, gene expression, drug sensitivity, and immune infiltration. A total of 369 prognostic PCDRGs were identified, which could cluster UM into 2 molecular subtypes with significant differences in prognosis and clinicopathological characteristics. Furthermore, a risk feature PCDI composed of 11 PCDRGs was constructed, capable of indicating prognosis in UM patients. Additionally, PCDI exhibited correlations with the sensitivity to 25 drugs and the infiltration of various immune cells. Enrichment analysis revealed that PCDI was associated with immune regulation-related biological processes and pathways. Finally, a nomogram for prognostic assessment of UM patients was developed based on PCDI and gender, demonstrating excellent performance. This study elucidated the potential value of PCDRGs in prognostic assessment for UM and developed a corresponding risk feature. However, further basic and clinical studies are warranted to validate the functions and mechanisms of PCDRGs in UM.

Biomarkers of Alzheimer's Disease Associated with Programmed Cell Death Reveal Four Repurposed Drugs

Alzheimer's disease (AD) is a neurodegenerative disorder and the most common cause of dementia. Programmed cell death (PCD) is mainly characterized by unique morphological features and energy-dependent biochemical processes. The predominant pathway leading to cell death in AD has not been thoroughly analyzed, although there is evidence of neuron loss in AD and numerous pathways of PCD have been associated with this process. A better understanding of the systems biology underlying the relationship between AD and PCD could lead to the development of new therapeutic approaches. To this end, publicly available transcriptome data were examined using bioinformatic methods such as differential gene expression and weighted gene coexpression network analysis (WGCNA) to find PCD-related AD biomarkers. The diagnostic significance of these biomarkers was evaluated using a logistic regression-based predictive model. Using these biomarkers, a multifactorial regulatory network was developed. Last, a drug repositioning study was conducted to propose new drugs for the treatment of AD targeting PCD. The development of 3PM (predictive, preventive, and personalized) drugs for the treatment of AD would be enabled by additional research on the effects of these drugs on this disease.

Unraveling neoantigen-associated genes in bladder cancer: An in-depth analysis employing 101 machine learning algorithms

The therapeutic outcomes for bladder cancer (BLCA) remain suboptimal. Concurrently, there is a growing appreciation for the role of neoantigens in tumors. In this study, we explored the mechanisms underlying the involvement of neoantigen-associated genes in BLCA and their impact on prognosis. Our analysis incorporated both single-cell sequencing and bulk sequencing data sourced from publicly available databases. By employing a comprehensive set of 10 machine learning algorithms, we generated 101 algorithm combinations. The optimal combination, determined based on consistency indices, was utilized to construct a prognostic model comprising nine genes (CAPG, ACTA2, PDIA6, AKNA, PTMS, SNAP23, ID2, CD3G, SP140). Subsequently, we validated this model in an independent cohort, demonstrating its robust testing efficacy. Moreover, we explored the correlations between various clinical traits, model scores, and genes. Leveraging extensive public data resources, we conducted a drug sensitivity analysis to provide insights for targeted drug screening. Additionally, consensus clustering analysis and immune infiltration analysis were performed on bulk sequencing datasets and immunotherapy cohorts. These analyses yield valuable insights into the role of neoantigens in BLCA, guiding future research endeavors.

Characteristics and significance of programmed cell death-related gene expression signature in skin cutaneous melanoma

BACKGROUND

Programmed cell death (PCD) pathways play crucial roles in the pathogenesis of skin cutaneous melanoma (SKCM). Understanding their prognostic significance and clinical implications is imperative for the development of personalized treatment strategies.

METHODS

A total of 1466 PCD-related genes were analyzed using data from The Cancer Genome Atlas (TCGA)-SKCM cohort (n = 353). Prognostic cell death index (CDI) was established and validated through survival analysis and predictive modeling. Functional enrichment, protein-protein interaction (PPI), consensus clustering, and tumor microenvironment assessment and drug sensitivity analysis were performed to elucidate the biological and clinical relevance of CDI.

RESULTS

CDI effectively stratified SKCM patients into high and low-risk groups, demonstrating significant differences in survival outcomes. It exhibited predictive value for survival at 1, 3, and 5 years. The concordance index (C-index) was 0.794 in the training set, and 0.792 and 0.821 in the internal and external validation sets, respectively. The corresponding area under curve (AUC) was all above 0.75 in these data sets. Functional enrichment analysis revealed significant associations with immune response and inflammatory processes. PPI analysis identified key molecular modules associated with apoptosis and chemokine signaling. Consensus clustering unveiled three discernible subtypes demonstrating notable disparities in survival outcomes based on CDI expression profiles. Assessment of the tumor microenvironment highlighted correlations with immune cell infiltration such as M1 macrophages and T cells. Drug sensitivity analysis indicated tight correlations between CDI levels and response to immunotherapy.

CONCLUSION

Our comprehensive analysis establishes the prognostic significance of PCD-related genes in SKCM. CDI emerges as a promising prognostic biomarker, offering insights into tumor biology and potential implications for personalized treatment strategies. Further validation and clinical integration of CDI are warranted to improve SKCM management and patient outcomes.

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC.

METHODS

By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores.

RESULTS

A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines.

CONCLUSION

Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Comprehensive pan-cancer analysis of YBX family reveals YBX2 as a potential biomarker in liver cancer

Background

The Y-box-binding proteins (YBX) act as a multifunctional role in tumor progression, metastasis, drug resistance by regulating the transcription and translation process. Nevertheless, their functions in a pan-cancer setting remain unclear.

Methods

This study examined the clinical features expression, prognostic value, mutations, along with methylation patterns of three genes from the YBX family (YBX1, YBX2, and YBX3) in 28 different types of cancer. Data used for analysis were obtained from Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A novel YBXs score was created using the ssGSEA algorithm for the single sample gene set enrichment analysis. Additionally, we explored the YBXs score's association with the tumor microenvironment (TME), response to various treatments, and drug resistance.

Results

Our analysis revealed that YBX family genes contribute to tumor progression and are indicative of prognosis in diverse cancer types. We determined that the YBXs score correlates significantly with numerous malignant pathways in pan-cancer. Moreover, this score is also linked with multiple immune-related characteristics. The YBXs score proved to be an effective predictor for the efficacy of a range of treatments in various cancers, particularly immunotherapy. To summarize, the involvement of YBX family genes is vital in pan-cancer and exhibits a significant association with TME. An elevated YBXs score indicates an immune-activated TME and responsiveness to diverse therapies, highlighting its potential as a biomarker in individuals with tumors. Finally, experimental validations were conducted to explore that YBX2 might be a potential biomarker in liver cancer.

Conclusion

The creation of YBXs score in our study offered new insights into further studies. Besides, YBX2 was found as a potential therapeutic target, significantly contributing to the improvement of HCC diagnosis and treatment strategies.

Integrated analysis of single-cell and bulk RNA sequencing data reveals prognostic characteristics of lysosome-dependent cell death-related genes in osteosarcoma

BACKGROUND

Tumor cells exhibit a heightened susceptibility to lysosomal-dependent cell death (LCD) compared to normal cells. However, the role of LCD-related genes (LCD-RGs) in Osteosarcoma (OS) remains unelucidated. This study aimed to elucidate the role of LCD-RGs and their mechanisms in OS using several existing OS related datasets, including TCGA-OS, GSE16088, GSE14359, GSE21257 and GSE162454.

RESULTS

Analysis identified a total of 8,629 DEGs1, 2,777 DEGs2 and 21 intersection genes. Importantly, two biomarkers (ATP6V0D1 and HDAC6) linked to OS prognosis were identified to establish the prognostic model. Significant differences in risk scores for OS survival were observed between high and low-risk cohorts. Additionally, scores of dendritic cells (DC), immature DCs and γδT cells differed significantly between the two risk cohorts. Cell annotations from GSE162454 encompassed eight types (myeloid cells, osteoblastic OS cells and plasma cells). ATP6V0D1 was found to be significantly over-expressed in myeloid cells and osteoclasts, while HDAC6 was under-expressed across all cell types. Moreover, single-cell trajectory mapping revealed that myeloid cells and osteoclasts differentiated first, underscoring their pivotal role in patients with OS. Furthermore, ATP6V0D1 expression progressively decreased with time.

CONCLUSIONS

A new prognostic model for OS, associated with LCD-RGs, was developed and validated, offering a fresh perspective for exploring the association between LCD and OS.

Identification of cuproptosis-related miRNAs in triple-negative breast cancer and analysis of the miRNA-mRNA regulatory network

Introduction

The close association between cuproptosis and tumor immunity in triple-negative breast cancer (TNBC) allows its monitoring for predicting the prognosis of patients with TNBC. Nevertheless, the biological function and prognostic value of cuproptosis-related miRNAs and their target genes have not been reported.

Purpose

To construct the miRNA and mRNA-based risk models associated with cuproptosis for patients with TNBC.

Methods

Comparison of expression levels for genes associated with cuproptosis was executed between patients in the normal individuals and the TCGA-TNBC cohort. Conducting differential analysis resulted in the identification of differentially expressed miRNA (DE-miRNAs) and differentially expressed genes (DEGs) between the TNBC and Control samples. Screening for prognostic miRNAs and biomarkers involved employing univariate Cox analysis and least absolute shrinkage and selection operator regression analyses. These methods were utilized to construct risk models aimed at predicting the survival of patients with TNBC. Based on the median value of risk scores, patients were then stratified into low- and high-risk groups. Functional enrichment analysis was employed to explore the potential function and pathways of prognostic genes. Additionally, independent prognostic analysis was performed through univariate and multivariate Cox regression. Immune infiltration analysis was performed to examine disparities in the infiltration of immune cells between the two risk groups. Finally, the prognostic gene expression was mined in key cell types of TNBC.

Results

We obtained 5213 DEGs and 204 DE-miRNAs related to cuproptosis between TNBC and Control samples. Five prognostic miRNAs (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were closely associated with TNBC. Significant differences in the functions of prognostic genes between the two risk groups were observed, encompassing adipogenesis, inflammatory response, and hormone metabolic process. The prognostic gene regulatory network revealed that miR200C-3p regulated ZFPM2 and CFL2, and miR-1277-3p regulated BMP2 and RORA. A nomogram was created based on riskScore, cancer status, and pathologic stage to predict 1/3/5-year survival of patients with TNBC. Immune infiltration analysis indicated that the immune microenvironment may be associated with the progression of TNBC. Interestingly, prognostic genes exhibited higher expression levels in T cells, fibroblasts, endothelial cells, and monocytes compared to other cells.

Conclusions

Five prognostic miRNA (miR-203a-3p, miR-1277-3p, miR-135b-5p, miR-200c-3p, and miR-592) and three biomarkers (DENND5B, IGF1R, and MEF2C) were significantly associated with TNBC, it provides new therapeutic targets for the treatment and prognosis of TNBC.

Deciphering the prognostic features of bladder cancer through gemcitabine resistance and immune-related gene analysis and identifying potential small molecular drug PIK-75

BACKGROUND

Bladder cancer (BCa) stands out as a prevalent and highly lethal malignancy worldwide. Chemoresistance significantly contributes to cancer recurrence and progression. Traditional Tumor Node Metastasis (TNM) stage and molecular subtypes often fail to promptly identify treatment preferences based on sensitivity.

METHODS

In this study, we developed a prognostic signature for BCa with uni-Cox + LASSO + multi-Cox survival analysis in multiple independent cohorts. Six machine learning algorithms were adopted to screen out the hub gene, RAC3. IHC staining was used to validate the expression of RAC3 in BCa tumor tissue. RT-qPCR and Western blot were performed to detect and quantify the mRNA and protein levels of RAC3. CCK8, colony formation, wound healing, and flow cytometry analysis of apoptosis were employed to determine cell proliferation, migration, and apoptosis. Molecular docking was used to find small target drugs, PIK-75. 3D cell viability assay was applied to evaluate the ATP viability of bladder cancer organoids before and after PIK-75 treated.

RESULTS

The established clinical prognostic model, GIRS, comprises 13 genes associated with gemcitabine resistance and immunology. This model has demonstrated robust predictive capabilities for survival outcomes across various independent public cohorts. Additionally, the GIRS signature shows significant correlations with responses to both immunotherapy and chemotherapy. Leveraging machine learning algorithms, the hub gene, RAC3, was identified, and potential upstream transcription factors were screened through database analysis. IHC results showed that RAC3 was higher expressed in GEM-resistant BCa patients. Employing molecular docking, the small molecule drug PIK-75, as binding to RAC3, was identified. Experiments on cell lines, organoids and animals validated the biological effects of PIK-75 in bladder cancer.

CONCLUSIONS

The GIRS signature offers a valuable complement to the conventional anatomic TNM staging system and molecular subtype stratification in bladder cancer. The hub gene, RAC3, plays a crucial role in BCa and is significantly associated with resistance to gemcitabine. The small molecular drug, PIK-75 having the potential as a therapeutic agent in the context of gemcitabine-resistant and immune-related pathways.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis

BACKGROUND

This study aimed to identify potential biomarkers for the diagnosis and treatment of osteoporosis (OP).

METHODS

Data sets were downloaded from the Gene Expression Omnibus database, and differentially programmed cell death-related genes were screened. Functional analyses were performed to predict the biological processes associated with these genes. Least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) machine learning algorithms were used to screen for characteristic genes, and receiver operating characteristics were used to evaluate the diagnosis of disease characteristic gene values. Gene set enrichment analysis (GSEA) and single-sample GSEA were conducted to analyze the correlation between characteristic genes and immune infiltrates. Cytoscape and the Drug Gene Interaction Database (DGIdb) were used to construct the mitochondrial RNA-mRNA-transcription factor network and explore small-molecule drugs. Reverse transcription real-time quantitative PCR (RT-qPCR) analysis was performed to evaluate the expression of biomarker genes in clinical samples.

RESULTS

In total, 25 differential cell death genes were identified. Among these, two genes were screened using the LASSO, SVM, and RF algorithms as characteristic genes, including BRSK2 and VPS35. In GSE56815, the area under the receiver operating characteristic curve of BRSK2 was 0.761 and that of VPS35 was 0.789. In addition, immune cell infiltration analysis showed that BRSK2 positively correlated with CD56dim natural killer cells and negatively correlated with central memory CD4 + T cells. Based on the data from DGIdb, hesperadin was associated with BRSK2, and melagatran was associated with VPS35. BRSK2 and VPS35 were expectably upregulated in OP group compared with controls (all p < 0.05).

CONCLUSIONS

BRSK2 and VPS35 may be important diagnostic biomarkers of OP.

A PANoptosis pattern to predict prognosis and immunotherapy response in head and neck squamous cell carcinoma

Individuals diagnosed with head and neck squamous cell carcinoma (HNSCC) experience a significant occurrence rate and are susceptible to premature spreading, resulting in a bleak outlook. Therapeutic approaches, such as chemotherapy, targeted therapy, and immunotherapy, may exhibit primary and acquired resistance during the advanced phases of HNSCC. There is currently no viable solution to tackle this issue. PANoptosis-a type of non-apoptotic cell death-is a recently identified mechanism of cellular demise that entails communication and synchronization among thermal apoptosis, apoptosis, and necrosis mechanisms. However, the extent to which PANoptosis-associated genes (PRG) contribute to the forecast and immune reaction of HNSCC remains mostly undisclosed. The present study aimed to thoroughly analyze the potential importance of PRG in HNSCC and report our discoveries. We systematically analyzed 19 PRG from previous studies and clinical data from HNSCC patients to establish a PAN-related signature and assess its prognostic, predictive potential. Afterward, the patient information was separated into two gene patterns that corresponded to each other, and the analysis focused on the connection between patient prognosis, immune status, and cancer immunotherapy. The PAN score was found to correlate with survival rates, immune systems, and cancer-related pathways. We then validated the malignant role of CD27 among them in HNSCC. In summary, we demonstrated the effectiveness of PAN.Score-based molecular clustering and prognostic features in predicting the outcome of HNSCC. The discovery we made could enhance our comprehension of the significance of PAN.Score in HNSCC and facilitate the development of more effective treatment approaches.

Ezrin's role in gastric cancer progression: Implications for immune microenvironment modulation and therapeutic potential

At present, surgical resection is the most effective method for the treatment of gastric cancer. However, death caused by inoperable metastasis is still very common, despite research in this area. The mechanisms underlying the occurrence, development, and metastasis of gastric cancer are not fully understood. Ezrin, a plasma membrane-microfilament junction participates in a variety of cellular activities and is closely related to tumorigenesis and development. Few studies have explored the relationship between the tumor immune microenvironment and ezrin expression in gastric cancer. In this study, we used proteomic techniques to analyze the differentially expressed proteins between the gastric cancer cell lines MKN-45 and HGC-27 and screened ezrin as the target protein. We collected patient information from The TCGA and GEO databases, and the results showed that ezrin was positively correlated with adverse clinical features. We further explored the relationship between ezrin expression levels, immune microenvironment, and genomic changes. We found that ezrin was involved in immune regulation and genomic instability in gastric cancer. When the expression of ezrin is high, immune cell infiltration also increases. We also predicted that ezrin is closely related to immunotherapy and chemosensitivity. Single-cell transcriptome data showed that the ezrin gene was mainly expressed in B cells and epithelial cells, and the expression of EZR in these epithelial cells was positively correlated with the epithelial-mesenchymal transformation pathway and Pi3k-AKT pathway score. Through functional verification of the stably transfected cell line constructed by lentivirus, the results of the liver metastasis model in nude mice suggested that high expression of ezrin leads to the formation of more metastatic foci. In summary, our results clarify the prognostic, immunological, and therapeutic value of ezrin in gastric cancer and provide a theoretical basis for more accurate treatment.