Immunogenic Cell Death-Relevant Damage-Associated Molecular Patterns and Sensing Receptors in Triple-Negative Breast Cancer Molecular Subtypes and Implications for Immunotherapy

Identification of immunogenic cell death-related damage-related molecular patterns (DAMPs) to predict outcomes in patients with head and neck squamous cell carcinoma

PURPOSE

Head and neck cancer is the sixth most common type of cancer worldwide, wherein the immune responses are closely associated with disease occurrence, development, and prognosis. Investigation of the role of immunogenic cell death-related genes (ICDGs) in adaptive immune response activation may provide cues into the mechanism underlying the outcome of HNSCC immunotherapy.

METHODS

ICDGs expression patterns in HNSCC were analyzed, after which consensus clustering in HNSCC cohort conducted. A 4-gene prognostic model was constructed through LASSO and Cox regression analyses to analyze the prognostic index using the TCGA dataset, followed by validation with two GEO datasets. The distribution of immune cells and the response to immunotherapy were compared between different risk subtypes through multiple algorithms. Moreover, immunohistochemical (IHC) analyses were conducted to validate the prognostic value of HSP90AA1 as a predictor of HNSCC patient prognosis. In vitro assays were performed to further detect the effect of HSP90AA1 in the development of HNSCC.

RESULTS

A novel prognostic index based on four ICDGs was constructed and proved to be useful as an independent factor of HNSCC prognosis. The risk score derived from this model grouped patients into high- and low-risk subtypes, wherein the high-risk subtype had worse survival outcomes and poorer immunotherapy response. IHC analysis validated the applicability of HSP90AA1 as a predictor of prognosis of HNSCC patients. HSP90AA1 expression in tumor cells promotes the progression of HNSCC.

CONCLUSIONS

Together, these results highlight a novel four-gene prognostic signature as a valuable tool to assess survival status and prognosis of HNSCC patients.

Arsenic trioxide augments immunogenic cell death and induces cGAS-STING-IFN pathway activation in hepatocellular carcinoma

The treatment of hepatocellular carcinoma (HCC) is particularly challenging due to the inherent tumoral heterogeneity and easy resistance towards chemotherapy and immunotherapy. Arsenic trioxide (ATO) has emerged as a cytotoxic agent effective for treating solid tumors, including advanced HCC. However, its effectiveness in HCC treatment remains limited, and the underlying mechanisms are still uncertain. Therefore, this study aimed to characterize the effects and mechanisms of ATO in HCC. By evaluating the susceptibilities of human and murine HCC cell lines to ATO treatment, we discovered that HCC cells exhibited a range of sensitivity to ATO treatment, highlighting their inherent heterogeneity. A gene signature comprising 265 genes was identified to distinguish ATO-sensitive from ATO-insensitive cells. According to this signature, HCC patients have also been classified and exhibited differential features of ATO response. Our results showed that ATO treatment induced reactive oxygen species (ROS) accumulation and the activation of multiple cell death modalities, including necroptosis and ferroptosis, in ATO-sensitive HCC cells. Meanwhile, elevated tumoral immunogenicity was also observed in ATO-sensitive HCC cells. Similar effects were not observed in ATO-insensitive cells. We reported that ATO treatment induced mitochondrial injury and mtDNA release into the cytoplasm in ATO-sensitive HCC tumors. This subsequently activated the cGAS-STING-IFN axis, facilitating CD8+ T cell infiltration and activation. However, we found that the IFN pathway also induced tumoral PD-L1 expression, potentially antagonizing ATO-mediated immune attack. Additional anti-PD1 therapy promoted the anti-tumor response of ATO in ATO-sensitive HCC tumors. In summary, our data indicate that heterogeneous ATO responses exist in HCC tumors, and ATO treatment significantly induces immunogenic cell death (ICD) and activates the tumor-derived mtDNA-STING-IFN axis. These findings may offer a new perspective on the clinical treatment of HCC and warrant further study.

Immunogenic Cell Death-related Signature Evaluates the Tumor Microenvironment and Predicts the Prognosis in Diffuse Large B-Cell Lymphoma

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-low subtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor.

A model based on immunogenic cell death-related genes predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

Background

The prognosis of patients with kidney renal clear cell carcinoma (KIRC), a life-threatening condition, is poor. Immunogenic cell death (ICD) induces regulated cell death via immunogenic signal secretion and exposure. ICD induces regulated cell death through immunogenic signal secretion and exposure. ICD plays an essential role in tumorigenesis, however, the role of ICD in KIRC remains unclear.

Methods

This study examined the expression levels of 34 ICD-related genes in The Cancer Genome Atlas (TCGA) data set. Signature genes linked to KIRC survival were identified using Cox regression. Next, a prognostic risk model (RM) was built. Subsequently, the KIRC patients were divided into low- and high-risk groups. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were plotted. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were carried out to investigate the possible role of differential gene expression between the two groups. The immune microenvironment (IME) was assessed using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression, CIBERSORT, and single-sample gene-set enrichment analysis algorithms. An enrichment analysis was used to determine the biological significance of these regulatory networks we conducted. The relationship between immune checkpoint gene expression and risk score, and the relationship between treatment outcome and gene expression were assessed using correlation analyses.

Results

We developed a KIRC RM based on five ICD-related genes (i.e., FOXP3, IFNB1, IL6, LY96, and TLR4), which were identified as the prognostic signature genes. Using the TCGA data set, we conducted a survival analysis and found that the 3-year RM had an area under the curve (AUC) of 0.735, which validated the reliability of the signature. Similarly, using the International Cancer Genome Consortium (ICGC) data set, we found that the 3-year RM had an AUC of 0.732.

Conclusions

A RM based on five ICD-related genes was built to predict the prognosis of KIRC patients. This RM predicted patient prognosis and reflected the tumor IME of KIRC patients. Thus, this RM could be used to promote individualized treatments and provide potential novel targets for immunotherapy.

Efficacy and Mechanism of a Biomimetic Nanosystem Carrying Doxorubicin and an IDO Inhibitor for Treatment of Advanced Triple-Negative Breast Cancer

Introduction

Chemotherapy is still the treatment of choice for advanced triple-negative breast cancer. Chemotherapy combined with immunotherapy is being tried in patients with triple-negative breast cancer. As a kind of "cold tumor", triple-negative breast cancer has a bottleneck in immunotherapy. Indoleamine 2, 3-dioxygenase-1 inhibitors can reverse the immunosuppressive state and enhance the immune response.

Methods

In this study, mesoporous silica nanoparticles were coated with the chemotherapeutic drug doxorubicin and indoleamine 2, 3-dioxygenase 1 inhibitor 1-Methyl-DL-tryptophan (1-MT), and then encapsulate the surfaces of a triple-negative breast cancer cell membrane to construct the tumor dual-targeted delivery system CDIMSN for chemotherapy and immunotherapy, and to investigate the immunogenic death effect of CDIMSN.

Results and discussion

The CDIMSN could target the tumor microenvironment. Doxorubicin induced tumor immunogenic death, while 1-MT reversed immunosuppression. In vivo findings showed that the tumor size in the CDIMSN group was 2.66-fold and 1.56-fold smaller than that in DOX and DIMSN groups, respectively. CDIMSN group was better than naked DIMSN in stimulating CD8+T cells, CD4+T cells and promoting Dendritic Cells(DC) maturation. In addition, blood analysis, biochemical analysis and Hematoxylin staining analysis of mice showed that the bionic nanoparticles had good biological safety.

Construction of a Novel Damage-Associated Molecular-Pattern-Related Signature to Assess Lung Adenocarcinoma's Prognosis and Immune Landscape

Immunogenic death (ICD) stimulates adaptive immunity and affects immunotherapeutic efficacy, an important part of which is damage-associated molecular patterns (DAMPs). However, the function of these DAMPs for lung adenocarcinoma (LUAD) remains obscure. We initially found differentially expressed genes (DEGs) with prognostic significance related to DAMPs with the TCGA database and then used the least absolute shrinkage and selection operator (LASSO) regression to create a risk signature strongly correlated with overall survival (OS) with eight DEGs. Validation was performed externally using the external data set GSE68465. Lower-risk LUAD patients were found to be more chemotherapy-resistant and enriched for more immune-related pathways than those with higher risk scores, and patients with different risks showed different levels of immune cell infiltration. PANX1, a crucial gene closely associated with lung adenocarcinoma, was identified using the weighted correlation network analysis (WGCNA), and experiments revealed that PANX1 promotes the proliferation as well as invasion of LUAD cells. Furthermore, PANX1 was found to be positively correlated with CD274, CD276, and M2 macrophage markers. We developed and validated an entirely new gene signature related to DAMPs that may be useful for LUAD patient prognosis, immune microenvironment, and chemotherapeutic drug sensitivity prediction. The results may also guide clinical immunotherapy and chemotherapy approaches for LUAD patients.

Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction

Background

Osteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.

Methods

Patient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the "xCell" algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.

Results

Four gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.

Conclusion

The current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

Dysfunctional TLR1 reduces the therapeutic efficacy of chemotherapy by attenuating HMGB1-mediated antitumor immunity in locally advanced colorectal cancer

Regional lymph node metastasis is an important predictor for survival outcome and an indicator for postoperative adjuvant chemotherapy in patients with colorectal cancer. Even with advances in adjuvant chemotherapeutic regimens, 5-year distant metastasis and survival rates are still unsatisfactory. Here, we evaluate the clinical significance of polymorphisms in receptors for HMGB1, which is the hallmark of chemotherapy-induced immunogenic cell death, in patients with stage II-III colon carcinoma (COAD). We found that high cytosolic HMGB1 is elicited in stage III COAD patients who received adjuvant chemotherapy. Patients with the TLR1-N248S polymorphism (rs4833095), which causes loss-of-function in HMGB1-mediated TLR1-TLR2 signaling, may influence the therapeutic efficacy of adjuvant chemotherapy, leading to a high risk of distant metastasis within 5 years [HR = 1.694, 95% CI = 1.063-2.698, p = 0.027], suggesting that TLR1-N248S is an independent prognostic factor for locally advanced colon carcinoma patients. We found that defective TLR1 impaired TLR1/2 signaling during dendritic cell (DC) maturation for the antitumor immune response under immunogenic chemotherapy oxaliplatin (OXP) treatment. Defective TLR1 on DCs impaired their maturation ability by HMGB1 and reduced the secretion of IFNγ from T cells to eradicate tumor cells in vitro. Moreover, systemic inhibition of TLR1/2 dramatically reduced the tumor-infiltrating immune cells by OXP treatment, leading to poor therapeutic response to OXP. In contrast, administration of a TLR1/2 agonist synergistically increased the benefit of OXP treatment and triggered a high density of tumor-infiltrating immune cells. We also observed that fewer tumor-infiltrating cytotoxic T lymphocytes were located within the tumor microenvironment in patients bearing the TLR1-N248S polymorphism. Overall, our results suggest that dysfunctional TLR1 may reduce the therapeutic response to adjuvant chemotherapy by impairing HMGB1-mediated DC maturation and attenuating the antitumor immune response in locally advanced colon carcinoma patients.

Peptide aggregation-induced immunogenic cell death in a breast cancer spheroid model

Utilizing multicellular aggregates (spheroids) for in vitro cancer research offers a physiologically relevant model that closely mirrors the intricate tumor microenvironment, capturing properties of solid tumors such as cell interactions and drug resistance. In this research, we investigated the Peptide-Aggregation Induced Immunogenic Response (PAIIR), an innovative method employing engineered peptides we designed specifically to induce immunogenic cell death (ICD). We contrasted PAIIR-induced ICD with standard ICD and non-ICD inducer chemotherapeutics within the context of three-dimensional breast cancer tumor spheroids. Our findings reveal that PAIIR outperforms traditional chemotherapeutics in its efficacy to stimulate ICD. This is marked by the release of key damage-associated molecular patterns (DAMPs), which bolster the phagocytic clearance of dying cancer cells by dendritic cells (DCs) and, in turn, activate powerful anti-tumor immune responses. Additionally, we observed that PAIIR results in elevated dendritic cell activation and increased antitumor cytokine presence. This study not only showcases the utility of tumor spheroids for efficient high-throughput screening but also emphasizes PAIIR's potential as a formidable immunotherapeutic strategy against breast cancer, setting the stage for deeper exploration and potential clinical implementation.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

Comprehensive analysis of ICD-related lncRNAs in predicting risk stratification, clinical prognosis and immune response for breast cancer

BACKGROUND

Breast cancer (BRCA) represents a significant threat with high mortality rates due to relapse, metastasis, and chemotherapy resistance. As a regulated cell death process characterized by the induction of immunogenic signals, immunogenic cell death (ICD) has been identified as an effective anti-tumorigenesis approach. However, the comprehensive study and its clinical value of ICD-related lncRNAs in BRCA is still missing.

METHODS

The transcriptome matrix and corresponding clinical information of BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was performed to identify ICD-related lncRNAs (ICDRLs). To determine the prognostic value of the identified ICDRLs, univariate Cox regression analysis, LASSO algorithm, and multivariate Cox regression analysis were employed to construct a risk model. The prognostic risk model was subsequently evaluated using univariate and multivariate Cox regression analysis, as well as Nomogram analysis. In vitro experiments were also conducted to validate the bioinformatics findings using quantitative real-time PCR (qRT-PCR).

RESULTS

We established a prognostic risk signature consisting of five ICDRLs. The prognostic value of this model was subsequently confirmed in guiding BRCA prognostic stratification. Furthermore, we explored the correlation of the risk score with various clinical characteristics and chemotherapy response. qRT-PCR result confirmed the abnormal expression of ICDRLs, which was consistent with the bioinformatics data.

CONCLUSIONS

Our findings provide evidence of the critical role of ICDRLs in BRCA and offer a novel perspective for exploring precise treatment options for BRCA patients.

Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma

OBJECTIVE

Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD).

METHODS

The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets.

RESULTS

A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients.

CONCLUSION

The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.

Comprehensive analysis of the immunogenic cell death-related signature for predicting prognosis and immunotherapy efficiency in patients with lung adenocarcinoma

BACKGROUND

Although immunotherapy has been considered as a potent strategy for lung adenocarcinoma (LUAD), only a small part of patients was served as potentially clinical benefiters. Immunogenic cell death (ICD), a type of regulated cell death (RCD), which enable to reshape the tumor immune microenvironment and contribute to the immunotherapy efficiency. Developing a novel ICD-based signature may be a potential strategy to differentiate prognosis of patients with LUAD and predict efficacy of immunotherapy.

METHODS

In this study, 34 ICD-related genes (ICDRGs) were identified and analyzed in LUAD samples from the Cancer Genome Atlas (TCGA). 572 patients with LUAD were divided into two distinct clusters according to ICDRGs expression levels. Patients were subsequently classified into two distinct gene subtypes based on differentially expressed genes (DEGs) analyzed between two ICD-related clusters. We further developed and validated a novel ICD-related score (ICDRS) followed by comprehensive investigation about the landscape of the prognosis, immune-based features, immunotherapautic responses and sensitivity of target drugs in patients with LUAD.

RESULTS

After confirming transcriptomic aberrations and appraising prognostic value of ICDRGs, two ICD-associated subtypes were initially determined by consensus clustering in accordance with differentially expressional levels of ICDRGs. It was shown that patients in the ICD high-subtype possessed the superior clinical prognosis, abundant immune cell infiltration and higher involvement in immune-related signaling compared with the ICD low-subtype. A signature of ICD-related score (ICDRS) was further established and validated, which was served as an independent prognostic indicator for LUAD patients. These comprehensive results revealed that the high-score patients represented better clinical prognosis, higher immune infiltration-related characteristics, stronger expression of immune checkpoints, and better response to immune checkpoint inhibitor therapy and multiple targeted drugs. To further verify our analysis, we selected TLR4 as the representative of ICDRGs and evaluated its expression on the lung normal cells and cancer cells in vitro. Then, relative animal experiments were performed in vivo, with results of that the stimulation of TLR4 suppressed the growth of lung cancer.

CONCLUSIONS

In conclusion, our comprehensive analysis of ICDRGs in LUAD demonstrated their function in serving as a biomarker of predicting prognosis and clinical effects of immunotherapy and targeted drugs, which is meaningful to improve our understanding of ICDRGs and brought inspirations about evaluating prognosis and developing effective therapeutic strategies to patients with LUAD.

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

BACKGROUND

Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy.

METHODS

A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared.

RESULTS

The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells.

CONCLUSION

In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.

Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma

BACKGROUND

Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients.

METHODS

A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared.

RESULTS

The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden.

CONCLUSIONS

In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.

Immune cell death-related lncRNA signature as a predictive factor of clinical outcomes and immune checkpoints in gastric cancer

Background: Immune cell death (ICD) is a type of tumor cell death that has recently been shown to activate and regulate tumor immunity. However, the role of ICD-related long non-coding RNAs (lncRNAs) in gastric cancer remains to be clarified.

Methods: We obtained 375 tumor samples from the Cancer Genome Atlas (TCGA) database and randomly assigned them to training and verification groups. LASSO and Cox regression analysis were utilized to identify ICD-related lncRNAs and establish a risk model. The changes in the immune microenvironment of the two groups were compared by examining the tumor-infiltrating immune cells.

Results: We established a tumor signature based on nine ICD-related lncRNAs. In light of the receiver operating characteristic and Kaplan–Meier curves, the prognostic values of this risk model were verified. Multivariate regression analysis showed that the risk score was an independent risk factor for the prognosis of patients in both the training cohort (HR 2.52; 95% CI: 1.65–3.87) and validation cohort (HR 2.70; 95% CI: 1.54–4.8). A nomogram was developed to predict the 1-, 3-, and 5-year survival of patients with gastric cancer, and the signature was linked to high levels of immunological checkpoint expression (B7-H3, VSIR).

Conclusions: An ICD-related lncRNA signature could predict the immune response and prognosis of patients with gastric cancer. This prognostic signature could be employed to independently monitor the efficacy of immunotherapy for gastric cancer patients.

A comprehensive analysis of immunogenic cell death and its key gene HSP90AA1 in bladder cancer

BACKGROUND

Bladder cancer (BLCA) is defined as a type of urinary cancer with high incidence and lack of specific biomarkers and drug targets. Immunogenic cell death (ICD) has been classified as a regulated type of cell death. Growing evidence suggested that ICD can reshape the tumor immune microenvironment, which may contribute to the development of immunotherapy strategies. The aim of this study was to reveal the specific mechanism of ICD in bladder cancer and to further predict the prognostic immunotherapy outcomes.

METHODS

By consensus clustering analysis, bladder cancer patients in TCGA database were divided into different ICD subtypes. Additionally, we developed an ICD-scoring system and constructed the ICD score-based risk signature and nomogram to better characterize patients. Furthermore, we carried out a series of experiments to verify the relevant findings.

RESULTS

Based on the transcriptome expression levels of ICD-related genes, a total of 403 BLCA patients in the TCGA database were divided into two subgroups with different ICD molecular patterns by consensus cluster analysis. These subgroups showed different clinicopathological features, survival outcomes, tumor microenvironment (TME) characteristics, immune-related scores, and treatment response. Moreover, the established prediction model and ICD score can effectively distinguish high risk/score patients from low risk/score patients, which has excellent predictive value. Finally, we found that the key gene HSP90AA1 was highly expressed in the high-ICD score group and in bladder cancer tissues, and was confirmed to be associated with the proliferation of bladder cancer cells.

CONCLUSION

To sum up, we established a new classification system for BLCA based on ICD-related genes. This stratification has significant predictive power for clinical outcomes and can effectively evaluate the prognosis and immunotherapy of BLCA patients. Finally, it was proved that HSP90AA1 was highly expressed in BLCA and would be a promising therapeutic target for BLCA.

Identification of Immunogenic Cell-Death-Related Subtypes and Development of a Prognostic Signature in Gastric Cancer

Background: Immunogenic cell death (ICD) is considered a promising type of regulated cell death and exerts effects by activating the adaptive immune response, reshaping the tumor environment (TME) and improving therapeutic efficacy. However, the potential roles and prognostic value of ICD-associated genes in gastric cancer (GC) remain unclear. Methods: The RNA expression data and clinical information of 1090 GC patients from six cohorts were collected. Consensus clustering was used to identify three distinct molecular subtypes. Then, a robust prognostic ICD_score for predicting prognosis was built via WGCNA and LASSO Cox regression according to the TCGA cohort, and the predictive capability of the ICD_score in GC patients was validated in the other cohorts. ICD-related immune features were analyzed using a CIBERSORT method and verified by immunofluorescence. Results: We found that ICD-related gene variations were correlated with clinical outcomes, tumor immune microenvironment (TIME) characteristics and treatment response. We then constructed an ICD signature that classifies cases as low- and high-ICD_score groups. The high-ICD_score group indicates unfavorable OS, a more advanced TNM stage, and presents an immune-suppressed phenotype, which has more infiltrations of pro-tumor immune cells, such as macrophages, which was verified by immunofluorescence. In addition, a nomogram containing the ICD_score showed a high predictive accuracy with AUCs of 0.715, 0.731 and 0.8 on Years 1, 3, and 5. Conclusion: We performed the first and synthesis ICD analysis in GC and built a clinical application tool based on the ICD signature, which paved a new path for assessing prognosis and guiding individual treatment.

Identification of Key Biomarkers Associated with Immunogenic Cell Death and Their Regulatory Mechanisms in Severe Acute Pancreatitis Based on WGCNA and Machine Learning

Immunogenic cell death (ICD) is a form of programmed cell death with a strong sense of inflammatory detection, whose powerful situational awareness can cause the reactivation of aberrant immunity. However, the role of ICD in the pathogenesis of severe acute pancreatitis (SAP) has yet to be investigated. This study aims to explore the pivotal genes associated with ICD in SAP and how they relate to immune infiltration and short-chain fatty acids (SCFAs), in order to provide a theoretical foundation for further, in-depth mechanistic studies. We downloaded GSE194331 datasets from the Gene Expression Omnibus (GEO). The use of differentially expressed gene (DEG) analysis; weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis allowed us to identify a total of three ICD-related hub genes (LY96, BCL2, IFNGR1) in SAP. Furthermore, single sample gene set enrichment analysis (ssGSEA) demonstrated that hub genes are closely associated with the infiltration of specific immune cells, the activation of immune pathways and the metabolism of SCFAs (especially butyrate). These findings were validated through the analysis of gene expression patterns in both clinical patients and rat animal models of SAP. In conclusion, the first concept of ICD in the pathogenesis of SAP was proposed in our study. This has important implications for future investigations into the pro-inflammatory immune mechanisms mediated by damage-associated molecular patterns (DAMPs) in the late stages of SAP.

Exploration of Immunogenic Cell Death-Associated Genes for Predicting Prognosis and Immunological Characteristics in Cervical Squamous Cell Carcinoma

Background. The tumor microenvironment (TME) has gradually entered the vision of researchers and is becoming a vital part of the occurrence of cervical squamous cell carcinoma (CSCC). However, understanding the specific composition of TME still confront enormous challenges, particularly immune and stromal components. Methods. In this study, we performed an unsupervised cluster analysis to determine the immunogenic cell death-associated subtype of CSCC patients. The differences in immune status, genomic alteration, and clinical outcomes between each subtype were compared. Subsequently, we screened vital prognostic factors. The HPA database was employed to verify the protein localization and the expression level between cancer and adjacent tissues. Results. CSCC patients were divided into three subtypes according to the expression of immunogenic cell death-associated genes. Cluster C has the highest survival rate because of the lower activation of tumor-related pathways. The immune score and stromal score of patients with Cluster B were the highest, so it may be considered that stromal tissue inhibits the anti-tumor effect of immunocytes. In addition, we constructed a risk score based on immunogenic cell death-associated genes to screen for vital markers. We systematically revealed the genomic alteration of vital markers. Conclusions. We have established a novel immunogenic cell death-associated risk scoring system in CSCC, and the expression of immunogenic cell death-associated genes may be a valuable biomarker for immunotherapy strategies. Our work may contribute to the development of new immunomodulators and develop new precision immunotherapies for CSCC.

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8⁺ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC).

Upregulated Immunogenic Cell-Death-Associated Gene Signature Predicts Reduced Responsiveness to Immune-Checkpoint-Blockade Therapy and Poor Prognosis in High-Grade Gliomas

Background: Immunogenic cell death (ICD) has emerged as a potential mechanism mediating adaptive immune response and tumor immunity in anti-cancer treatment. However, the signature of ICD in high-grade gliomas (HGGs) remains largely unknown, and its relevance to immunotherapies is still undetermined. The purpose of this study is to identify ICD-associated genotypes in order to explore their relevance to tumor immunity, patient prognosis and therapeutic efficacy of immune checkpoint blockade (ICB) therapy in HGGs. Methods: Bulk RNA-seq data and clinical information on 169 and 297 patients were obtained from the Cancer Genome Atlas (TCGA) and China Glioma Genome Atlas (CGGA), respectively. The functional enrichment and characterization of ICD genotyping were detected, and the ICD prognostic signature prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The responsiveness to immunotherapy was predicted according to the scoring of the ICD prognostic signature. Results: The HGG patients with high ICD gene signature (C1) showed poor outcomes, increased activity of immune modulation and immune escape, high levels of immune-checkpoint markers, and HLA-related genes, which may explain their reduced response to ICB immunotherapy. A gene set of the ICD signature, composing FOXP3, IL6 LY96, MYD88 and PDIA3, showed an independent prognostic value in both the TCGA and the CGGA HGG cohort. Conclusions: Our in silico analyses identified the ICD gene signature in HGGs with potential implications for predicting the responsiveness to ICB immune therapy and patient outcomes.

Development and validation of prognostic index based on immunogenic cell death-related genes with melanoma

Although immune checkpoint inhibitors have improved the overall survival rate of skin cutaneous melanoma (SKCM) patients, there is a wide variation and low response rate to these treatments in clinical immunotherapy for melanoma patients. These problems can be addressed through the induction of immunogenic cell death (ICD).We constructed an ICD-based prognostic model to predict the prognosis of SKCM patients and the efficacy of immunotherapy. Information on melanoma and normal samples obtained by TCGA and GTEx was stratified by ICD-related genes. The samples were divided into two subtypes according to high and low expression of ICD using an unsupervised clustering method (K-means). Patients with ICD-high subtype showed longer overall survival. We found that the ICD-related differential genes were associated with several cell death and immune-related pathways through GO, KEGG and GSEA. Immunoscore and tumor purity of ICD-associated genes was calculated using ESTIMATE, and ICD-high subtypes had higher immunoscore and lower tumor purity than ICD-low subtypes. Seven ICD-associated genes were obtained by one-way Cox regression and Lasso regression of ICD genes. Risk models were constructed to classify melanoma patients into high- risk and low-risk groups. The expression of ICD-related pivotal genes was lower in the high-risk group than in the low-risk group, and the survival time was significantly higher in the low-risk group than in the high-risk group. We then found that ICD risk characteristics had predictive value for the clinical efficacy of immunotherapy, with higher ICD risk scores in the immunotherapy non-responsive group. Combined with clinicopathological factors, a nomogram was established. the ROC and calibration curves assessed the ability of the nomogram to predict prognosis. We developed a new classification system for SKCM based on the characteristics of ICDs. This stratification has important clinical implications for estimating the prognosis and immunotherapy of SKCM patients.

Combining single-cell sequencing data to construct a prognostic signature to predict survival, immune microenvironment, and immunotherapy response in gastric cancer patients

Background and objective

Gastric cancer (GC) represents a major factor inducing global cancer-associated deaths, but specific biomarkers and therapeutic targets for GC are lacking at present. Therefore, the present work focused on developing an immune-related genetic signature at the single-cell level for categorizing GC cases and predicting patient prognostic outcome, immune status as well as treatment response.

Methods

Single-cell RNA-sequencing (scRNA-seq) data were combined with bulk RNA-seq data in GC patients for subsequent analyses. Differences in overall survival (OS), genomic alterations, immune status, together with estimated immunotherapeutic outcomes were measured between different groups.

Results

Nine cell types were identified by analyzing scRNA-seq data from GC patients, and marker genes of immune cells were also selected for subsequent analysis. In addition, an immune-related signature was established to predict OS while validating the prediction power for GC patients. Afterwards, a nomogram with high accuracy was constructed for improving our constructed signature’s clinical utility. The low-risk group was featured by high tumor mutation burden (TMB), increased immune activation, and microsatellite instability-high (MSI-H), which were related to the prolonged OS and used in immunotherapy. By contrast, high-risk group was associated with microsatellite stability (MSS), low TMB and immunosuppression, which might be more suitable for targeted therapy. Meanwhile, the risk score generated by our signature was markedly related to the cancer stem cell (CSC) index. In addition, the immunotherapeutic response prediction accuracy of our signature was validated in an external dataset IMvigor210 cohort.

Conclusion

A signature was constructed according to scRNA-seq data analysis. The signature-screened low- and high-risk patients had different prognoses, immune statuses and enriched functions and pathways. Such results shed more lights on immune status of GC, prognosis assessment, and development of efficient immunotherapeutic treatments.

Integration of single-cell RNA sequencing and bulk RNA sequencing to reveal an immunogenic cell death-related 5-gene panel as a prognostic model for osteosarcoma

Background

Despite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.

Methods

Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.

Results

The malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.

Conclusions

Overall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.

A damage-associated molecular patterns-related gene signature for the prediction of prognosis and immune microenvironment in children stage III acute lymphoblastic leukemia

BACKGROUND

Immunogenic cell death (ICD)-mediated immune response provides a strong rationale to overcome immune evasion in acute lymphoblastic leukemia (ALL). ICD will produce damage-associated molecular patterns (DAMPs) in tumor microenvironment. However, there are few studies on the application of DAMPs-related molecular subtypes in clinically predicting stage III of ALL prognosis. The current study is to identify the DAMPs-associated genes and their molecular subtypes in the stage III of ALL and construct a reliable risk model for prognosis as well as exploring the potential immune-related mechanism.

MATERIALS AND METHODS

We used Target and EBI database for differentially expressed genes (DEGs) analysis of the stage III pediatric ALL samples. Three clusters were identified based on a consistent clustering analysis. By using Cox regression and LASSO analysis, we determined DEGs that attribute to survival benefit. In addition, the Gene Set Enrichment Analysis (GSEA) was performed to identify potential molecular pathways regulated by the DAMPs-related gene signatures. ESTIMATE was employed for evaluating the composition of immune cell populations.

RESULTS

A sum of 146 DAMPs-associated DEGs in ALL were determined and seven transcripts among them were selected to establish a risk model. The DAMPs-associated gene signature significantly contributed to worse prognosis in the high-risk group. We also found that the high-risk group exhibited low immune cell infiltration and high expression of immune checkpoints.

CONCLUSION

In summary, our study showed that the DAMPs-related DEGs in the stage III of children ALL could be used to predict their prognosis. The risk model of DAMPs we established may be more sensitive to immunotherapy prediction.