Identification of a pyroptosis-related prognostic signature in breast cancer

BDNF is a prognostic biomarker involved in the immune infiltration of lung adenocarcinoma and associated with programmed cell death

It is well established that genes associated with cell death can serve as prognostic markers for patients with cancer. Programmed cell death (PCD) is known to play a role in cancer cell apoptosis and antitumor immunity. With the continuous discovery of new forms of PCD, the roles of PCD in lung adenocarcinoma (LUAD) require ongoing evaluation. In the present study, mRNA expression data and clinical information associated with 15 forms of PCD were extracted from publicly available databases and systematically analyzed. Utilizing these data, a robust risk prediction model was established that incorporates six PCD-related genes (PRGs). Datasets from the Gene Expression Omnibus database were employed to validate the six genes exhibiting risk-associated characteristics. The PRG-based model reliably predicted the prognosis of patients with LUAD, with the high-risk group showing a poor prognosis, reduced levels of immune infiltration molecules and diminished expression of human leukocyte antigens. Additionally, the relationships among PRGs, somatic mutations, tumor stemness index and immune infiltration were assessed. Based on these risk characteristics, a nomogram was constructed, patient stratification was performed, small-molecule drug candidates were predicted, and somatic mutations and chemotherapy responses were analyzed. Furthermore, reverse transcription-quantitative PCR was used to assess the expression of PDGs in vitro, and the critical role of brain-derived neurotrophic factor in LUAD development was identified through Mendelian randomization, gene knockdown, wound healing, western blot and colony formation assays. These findings offer new insights into the development of targeted therapies for LUAD, particularly in patients with high BDNF expression.

Comprehensive analysis of the role of Caspases in glioma

Caspases (CASPs) are attractive targets for cancer therapy. Many prognostic models based on gene signatures include genes from the CASPs family in diffuse glioma. CASP3, CASP4 and CASP6 in glioma have been studied individually. However, specialized comprehensive analysis of the roles CASPs family in glioma is lacking. Therefore, this study utilized bioinformatics methods to investigate this issue. CASP1-10 expressions were significantly up-regulated in LGG and GBM and glioma, and varied significantly across different clinical subgroups of glioma and LGG and various cell types, and most of CASP1-10 showed significant differences in recurrence status of LGG. 10 signatures (CASP1-10) were associated with poor overall survival (OS) in glioma and LGG and GBM. However, pan-cancer survival analysis showed that CASP1-10 were associated with the prognosis of LGG, but not GBM. CASP1-10 were related to poor prognosis of glioma and LGG, except for CASP9, which was the opposite of a protective factor. CASP1-10 were independent prognostic factors for OS in glioma and LGG, except for CASP5, and also for recurrence-free survival (RFS) in LGG. Most of CASP1-10 were also independent prognostic factors for disease-specific survival (DSS) and progression-free interval (PFI) and had diagnostic value in glioma and LGG. Genetic alterations of CASP1-10 genes set were associated with poor prognosis in LGG. CASP1-10 were involved in immune infiltration and programmed cell death in glioma and LGG and GBM, and might promote the apoptosis of immune cells. Compared to GBM, CASP1-10 had a more significant impact on the prognosis, cancer-related pathways, and immune infiltration in LGG, indicating that CASP1-10 might play important roles in the recurrence and progression of LGG, and might be promising therapeutic targets for LGG. Therefore, it is speculated that natural caspase inhibitor p35 may be a promising drug for the treatment of glioma, especially for LGG.

Heterogeneity analysis and prognostic model construction of HPV negative oral squamous cell carcinoma T cells using ScRNA-seq and bulk-RNA analysis

BACKGROUND

T cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.

METHODS

We acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets. We performed cell‒cell communication, trajectory, and pathway enrichment analyses of T-cell-associated genes. In addition, we constructed and validated a T-cell-associated gene prognostic model for HPV-negative OSCC patients using TCGA and GEO data and assessed the immune infiltration status of HPV-negative OSCC patients .qRT-PCR was used to detect the expression level of prognosis-related genes in different risk groups.

RESULTS

ScRNA-seq was conducted on 28,000 cells derived from 14 HPV-negative OSCC samples and 6 normal samples. We identified 4,635 T cells from these cells and identified 774 differentially expressed genes(DEGs) associated with T cells across five distinct T-cell subtypes. Through the integration of bulk-RNAseq data, we established a prognostic model based on DEGs related to T cells. By separating patients into high-risk and low-risk groups according to these prognostic related genes, we can accurately predict their survival rates and the immune infiltration status of the TME.qRT-PCR results showed that compared with the patients of low risk group, the expression of PMEPA1, SH2D2A, SMS and PRDX4 were significantly up-regulated in high risk group.

CONCLUSION

This study provides a resource for understanding the heterogeneity of T cells in HPV-negative OSCC patients and associated prognostic risk models. It provides new insights for predicting survival and level of immune infiltration in patients with HPV-negative OSCC.

Construction and evaluation of a multifactorial clinical model for discriminating benign and malignant breast tumors using LASSO algorithm based on retrospective cohort study

Breast cancer is one of the malignant tumors that seriously threaten women's health, and early diagnosis and detection of breast cancer are crucial for effective treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important diagnostic tool that allows for the dynamic observation of blood flow characteristics of breast tumors, including small lesions within the affected tissue. Currently, it is widely used in clinical practice and has been shown promising prospects. This study included a total of 1,987 patients who underwent breast surgery at Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine from January 1, 2019 to December 31, 2019. Comprehensive patient information was collected, including ultrasound, mammography findings, physical examination details, age, family history, and pathological diagnoses. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to assign values to the x variables, facilitating the construction and validation of the LASSO model group. Receiver operating characteristic curves were generated using support vector machines to determine the area under the curve (AUC), as well as to assess sensitivity and specificity. There were no statistically significant differences (P>0.05) in average age, body mass index, tumor location, or tumor benignity/malignancy between the training and test sets. The AUC, sensitivity, and specificity of mammography for predicting the benignity or malignancy of breast tumors were 0.83, 86.96%, and 76%, respectively. In comparison, the AUC, sensitivity, and specificity of DCE-MRI for the same predictions were 0.91, 91.3%, and 88%, respectively. The predictive performance of DCE-MRI was significantly higher than that of mammography (P<0.05). In conclusion, both mammography and DCE-MRI demonstrated high AUC, sensitivity, and specificity in predicting the benignity or malignancy of breast tumors. However, DCE-MRI showed superior predictive performance, making it a valuable tool for the early detection of clinical breast cancer with potential for broader clinical application.

Inhibition of NLRP3 inflammasome contributes to paclitaxel efficacy in triple negative breast cancer treatment

Chronic inflammation and NLRP3 inflammasome activation are among the determining factors of breast malignancies. Paclitaxel (PTX) is a drug used in breast cancer treatment which sustains prolonged inflammation, reducing the effectiveness of chemotherapy. Considering the impact of inflammatory processes in cancer progression, there is a strong concern to develop therapeutic strategy targeting NLRP3 inflammasome for triple-negative breast cancer (TNBC) treatment. Therefore, the aim of this study was to evaluate the potential of PTX and NLRP3 inflammasome modulation to counterbalance TNBC by inducing programmed cell death and inhibiting the activity of pro-inflammatory cytokines. The obtained results suggested the strong interaction between NLRP3 inflammasome and TNBC and revealed that pharmacological inhibition, using NLRP3-specific inhibitor MCC950, and genetic silencing of NLRP3 inflammasome using specific small interfering RNA, reduced inflammatory responses and facilitated PTX-determined tumor cell death. Thus, NLRP3 inflammasome manipulation in combination with anti-tumor drugs opens up new therapeutic perspectives for TNBC therapy.

Research progress on the effect of pyroptosis on the occurrence, development, invasion and metastasis of colorectal cancer

Pyroptosis is a type of programmed cell death mediated by gasdermines (GSDMs). The N-terminal domain of GSDMs forms pores in the plasma membrane, causing cell membrane rupture and the release of cell contents, leading to an inflammatory response and mediating pyrodeath. Pyroptosis plays an important role in inflammatory diseases and malignant tumors. With the further study of pyroptosis, an increasing number of studies have shown that the pyroptosis pathway can regulate the tumor microenvironment and antitumor immunity of colorectal cancer and is closely related to the occurrence, development, treatment and prognosis of colorectal cancer. This review aimed to explore the molecular mechanism of pyroptosis and the role of pyroptosis in the occurrence, development, treatment and prognosis of colorectal cancer (CRC) and to provide ideas for the clinical diagnosis and treatment of CRC.

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.

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

Developing a prognosis and chemotherapy evaluating model for colon adenocarcinoma based on mitotic catastrophe-related genes

Mitotic catastrophe (MC) is a novel form of cell death that plays an important role in the treatment and drug resistance of colon adenocarcinoma (COAD). However, MC related genes in COAD treatment and prognosis evaluation are rarely studied. In this study, the transcriptome data, somatic mutation and copy number variation data were obtained from The Cancer Genome Atlas (TCGA) database. The mitotic catastrophe related genes (MCRGs) were obtained from GENCARDS website. Differential gene analysis was conducted with LIMMA package. Univariate Cox regression analysis was used to identify prognostic related genes. Mutation analysis was performed and displayed by maftools package. RCircos package was used for localizing the position of genes on chromosomes. "Glmnet" R package was applied for constructing a risk model via the LASSO regression method. Consensus clustering analyses was implemented for clustering different subtypes. Functional enrichment analysis through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods, immune infiltration analysis via single sample gene set enrichment analysis (ssGSEA), tumor mutation burden and drug sensitivity analysis by pRRophetic R package were also carried out for risk model or molecular subtype's assessment. Additionally, the connections between the expression of hub genes and overall survival (OS) were obtained from online Human Protein Atlas (HPA) website. Real-Time Quantitative Polymerase Chain Reaction (RT‑qPCR) further validated the expression of hub genes. A total of 207 differentially expressed MCRGs were selected in the TCGA cohort, 23 of which were significantly associated with OS in COAD patients. Subsequently, we constructed risk score prognostic models with 5 hub MCRGs, including SYCE2, SERPINE1, TRIP6, LIMK1, and EEPD1. The high-risk patients suffered from poorer prognosis. Furthermore, we developed a nomogram that gathered age, sex, staging, and risk score to accurately forecast the clinical survival outcomes in 1, 3, and 5 years. The results of functional enrichment suggested a significant correlation between MCRGs characteristics and cancer progression, with important implications for the immune microenvironment. Moreover, patients who displayed high TMB and high risk score showed worse prognosis, and risk characteristics were associated with different chemotherapeutic agents. Finally, RT‑qPCR verified the increased expression of the five MCRGs in clinical samples. The five MCRGs in the prognostic signature were associated with prognosis, and could be treated as reliable prognostic biomarkers and therapeutic targets for COAD patients with distinct clinicopathological characteristics, thereby providing a foundation for the precise application of pertinent drugs in COAD patients.

Development of lymph node metastasis-related prognostic markers in breast cancer

BACKGROUND

Lymph node metastasis (LNM) from Breast cancer (BC) is commonly seen in BC progression. Currently, the identification of genes linked with LNM in BC remains in mystery.

METHODS

Genes related to BC LNM were screened, and a risk model was constructed based on LASSO-Cox analysis. Combined with the Kaplan-Meier curve, the ability of riskscore to distinguish different baseline characteristics was evaluated, and model was verified by the receiver operating characteristic (ROC) curve. The expression levels of prognostic marker genes were analyzed by qRT-PCR and western blot (WB).

RESULTS

A higher survival rate and longer survival time in low-risk BC patients. The 1, 3 and 5 year AUC values of the training set were 0.79, 0.74, and 0.73, respectively. Results for the validation set was similar to the training set. The differentially expressed genes between the high- and low-risk groups were significantly enriched in immune pathways. In addition, the low-risk group had higher levels of immune infiltration. qRT-PCR and WB results showed that in BC, CDH10, SMR3A, POU3F2, and FABP7 were down-regulated, and LHX1 was up-regulated.

CONCLUSIONS

We built a prognostic model of BC based on LNM-related genes, proffering evaluation for prognosis and precise cure of BC.

SIGNIFICANCE

At present, the genes related to lymph node metastasis in BC are still largely unknown and need to be further explored. Searching for potential lymph node metastasis-related genes of BC will provide meaningful biomarkers for BC treatment. Based on TCGA-BRCA data, we established an effective 11-gene prognostic risk model that could predict patient outcomes independently. Our model could classify BC patients and distinguish patients with poor prognosis effectively. Besides, the feature genes we identified might exert a predictive function in immunotherapy. The results of this study provide a new reference for the prognosis and treatment of BC patients with lymph node metastasis.

Single-Cell Analyses Reveal Necroptosis's Potential Role in Neuron Degeneration and Show Enhanced Neuron-Immune Cell Interaction in Parkinson's Disease Progression

Parkinson's disease (PD) is a common neuron degenerative disease among the old, characterized by uncontrollable movements and an impaired posture. Although widely investigated on its pathology and treatment, the disease remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) has been applied to the area of PD, providing valuable data for related research. However, few works have taken deeper insights into the causes of neuron death and cell-cell interaction between the cell types in the brain. Our bioinformatics analyses revealed necroptosis-related genes (NRGs) enrichment in neuron degeneration and selecting the cells by NRGs levels showed two subtypes within the main degenerative cell types in the midbrain. NRG-low subtype was largely replaced by NRG-high subtype in the patients, indicating the striking change of cell state related to necroptosis in PD progression. Moreover, we carried out cell-cell interaction analyses between cell types and found that microglia (MG)'s interaction strength with glutamatergic neuron (GLU), GABAergic neuron (GABA), and dopaminergic neuron (DA) was significantly upregulated in PD. Also, MG show much stronger interaction with NRG-high subtypes and a stronger cell killing function in PD samples. Additionally, we identified CLDN11 as a novel interaction pattern specific to necroptosis neurons and MG. We also found LEF1 and TCF4 as key transcriptional regulators in neuron degeneration. These findings suggest that MG were significantly overactivated in PD patients to clear abnormal neurons, especially the NRG-high cells, explaining the neuron inflammation in PD. Our analyses provide insights into the causes of neuron death and inflammation in PD from single-cell resolution, which could be seriously considered in clinical trials.

Identification of metabolism-related gene signature in lung adenocarcinoma

AIM

Lung cancer is one of the most common cancers in China and has a high mortality rate. Most patients who are diagnosed have lost the opportunity to undergo surgery. Aberrant metabolism is closely associated with tumorigenesis. We aimed to identify an effective metabolism-related prediction model for assessing prognosis based on the cancer genome atlas (TCGA) and GSE116959 databases.

METHODS

TCGA and GSE116959 datasets from Gene Expression Omnibus were used to obtain lung adenocarcinoma (LUAD) data. Additionally, we captured metabolism-related genes (MRGs) from the GeneCards database. First, we extracted differentially expressed genes using R to analyze the LUAD data. We then selected the same differentially expressed genes, including 168 downregulated and 77 upregulated genes. Finally, 218 differentially expressed MRGs (DEMRGs) were included to perform functional enrichment analysis and construct a protein-protein interaction network with the help of Cytoscape and Search Tool for the Retrieval of Interacting Genes database. Cytoscape was used to visualize the intensive intervals in the network. Then univariate and Least Absolute Shrinkage and Selection Operator Cox regression analyses, which assisted in identifying the overall survival (OS)-related DEMRGs and building a 10-DEMRG prognosis model, were performed. The prognostic values, tumor immunity relevance, and molecular mechanism were further investigated. A nomogram incorporating signature, age, gender, and TNM stage was established.

RESULTS

A 10-DEMRG model was established to forecast the OS of LUAD through Least Absolute Shrinkage and Selection Operator regression analysis. This prognostic signature stratified LUAD patients into low-risk and high-risk groups. The receiver operating characteristic curve and K-M analysis indicated good performance of the DEMRGs signature at predicting OS in the TCGA dataset. Univariate and multivariate Cox regression also revealed that the DEMRGs signature was an independent prognosis factor in LUAD. We noticed that the risk score was substantially related to the clinical parameters of LUAD patients, covering age and stage. Immune analysis results showed that risk score was associated with some immune cells and immune checkpoints. Nomogram also verified the clinical value of the DEMRGs signature.

CONCLUSION

In this study, we constructed a DEMRGs signature and established a prognostic nomogram that is robust and reliable to predict OS in LUAD. Overall, the findings could help with therapeutic customization and personalized therapies.

Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data

BACKGROUND

Breast cancer (BC) is one of the females' most common malignant tumors there are large individual differences in its prognosis. We intended to uncover novel useful genetic biomarkers and a risk signature for BC to aid determining clinical strategies.

METHODS

A combined significance (p combined) was calculated for each gene by Fisher's method based on the RNA-seq, CNV, and DNA methylation data from TCGA-BRCA. Genes with a p combined< 0.01 were subjected to univariate cox and Lasso regression, whereby an RS signature was established. The predicted performance of the RS signature would be assessed in GSE7390 and GSE20685, and emphatically analyzed in triple-negative breast cancer (TNBC) patients, while the expression of immune checkpoints and drug sensitivity were also examined. GSE176078, a single-cell dataset, was used to validate the differences in cellular composition in tumors between TNBC patients with different RS.

RESULTS

The RS signature consisted of C15orf52, C1orf228, CEL, FUZ, PAK6, and SIRPG showed good performance. It could distinguish the prognosis of patients well, even stratified by disease stages or subtypes and also showed a stronger predictive ability than traditional clinical indicators. The down-regulated expressions of many immune checkpoints, while the decreased sensitivity of many antitumor drugs was observed in TNBC patients with higher RS. The overall cells and lymphocytes composition differed between patients with different RS, which could facilitate a more personalized treatment.

CONCLUSION

The six genes RS signature established based on multi-omics data exhibited well performance in predicting the prognosis of BC patients, regardless of disease stages or subtypes. Contributing to a more personalized treatment, our signature might benefit the outcome of BC patients.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer

Introduction

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME.

Methods

This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan et al. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model.

Result

Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation.

Discussion

This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

Lysine lactylation (Kla) might be a novel therapeutic target for breast cancer

BACKGROUND

Histone lysine lactylation (Kla) is a newly identified histone modification, which plays a crucial role in cancer progression. Hence, we determined the prognostic value of Kla in breast cancer (BC).

METHODS

We obtained RNA expression profiles of BC from The Cancer Genome Atlas (TCGA), following screening out Kla-specific genes. Furthermore, we determined the prognostic value of Kla by constructing a cox model based on Kla-specific genes. Subsequently, we identified expression of lactate accumulation-related genes and prognostic Kla-specific genes through Human Protein Atlas (HPA), and further performed a correlation analysis based on their expression. Meanwhile, we explored the effects of Kla on BC tumor microenvironment (TME), drug therapy and immunotherapy. Moreover, we predicted the pathways influenced by Kla via gene set enrichment analysis (GSEA).

RESULTS

A total of 1073 BC samples and 112 normal controls were obtained from TCGA, and 23 tumor samples were removed owing to inadequate clinical information. We identified 257 differentially expressed Kla-specific genes (DEKlaGs) in BC. A cox model involved with CCR7, IGFBP6, NDUFAF6, OVOL1 and SDC1 was established, and risk score could be visualized as an independent biomarker for BC. Meanwhile, Kla was remarkably associated with BC immune microenvironment, drug therapy and immunotherapy. Kla was identified to be related to activation of various BC-related KEGG pathways.

CONCLUSION

In conclusion, Kla contributes to drug resistance and undesirable immune responses, and plays a crucial role in BC prognosis, suggesting that Kla was expected to be a new therapeutic target for BC.

A novel pyroptosis-related signature predicts prognosis and indicates immunotherapy in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) has been recognized as a frequently occurring oral malignant tumor. Pyroptosis plays an extremely important role in the occurrence and development of cancer, but the role of pyroptosis in OSCC remains unclear.

METHODS

OSCC-related data were obtained from the TCGA and GEO databases. A PSscore risk model was constructed through LASSO regression analysis. The GEO database was utilized as the validation set of the model. The "ESTIMATE" and "CIBERSORT" algorithms were utilized to additionally evaluate the relationship between the immune cell score and PSscore. TIDE and IPS algorithms were used to assess patient response to immunotherapy. In addition, Western blot analysis and MTT assay was used to further validate key genes.

RESULTS

Comprehensive bioinformatics analysis showed that a low-PSscore had a significant survival advantage, richer immune cell infiltration, more active immune-related pathways, higher TME scores, and lower tumor purity. The results of TIDE and IPS analysis indicated that the high-PSscore group had higher immune escape potential and was less sensitive to immunotherapy. In contrast, the low-PSscore group patients might be more sensitive to PD1 and CTLA4 + PD1 immunotherapy. Univariate and multivariate COX results indicated that PSscore was an independent prognostic factor in OSCC patients. Another important finding is that BAK1 is a potential target of OSCC and is related to the Nod-like receptor signaling pathway. Knockdown of BAK1 can significantly reduce the proliferation of OSCC cells.

CONCLUSION

The PSscore model could be utilized as a powerful prognostic indicator and can help in the development of new immunotherapies.

Molecular subtyping and immune score system by a novel pyroptosis-based gene signature precisely predict immune infiltrating, survival and response to immune-checkpoint blockade in breast cancer

Breast cancer is one of the most prevalent and lethal types of cancer affecting women globally. Pyroptosis is a recently elucidated form of inflammatory cell death mediated by the gasdermin family and is considered to be associated with the tumor immune microenvironment. However, the impact of pyroptosis on breast cancer patients remains unclear. In this study, we identified 31 Pyroptosis-Related-Genes (PRGs) and investigated their association with patient survival using data from the TCGA database. We then established a gene signature comprising 6 PRGs that were significantly correlated with prognosis, and used these genes to classify breast cancer into 2 molecular subtypes. We investigated the characteristics of these two subtypes and found that our molecular subtyping accurately separated the samples into two groups with distinct immune infiltration and prognosis. Patients with higher expression of these genes had significantly greater immune infiltrating, T cell signaling, and better prognosis. Moreover, we developed an immune score system based on these 6 genes that accurately predicted the immune infiltrating of patients and their response to immune-checkpoint blockade, which was difficult to achieve with previous models. Additionally, through single-cell analyses, we found that patients with higher immune scores had stronger cytotoxic immune cells. In summary, our study identified a novel gene set and developed an immune scoring system based on this gene set that can precisely predict the immune microenvironment and responses to immunotherapy of breast cancer (BRCA) patients, which could be useful in clinical trials.

High ECM2 Expression Predicts Poor Clinical Outcome and Promotes the Proliferation, Migration, and Invasiveness of Glioma

OBJECTIVE

Glioma is the most prevalent and fatal intracranial malignant tumor. Extracellular matrix protein 2 (ECM2) has rarely been studied in gliomas. Therefore, we explored the role of ECM2 in lower-grade gliomas (LGGs).

METHODS

The RNA-seq and clinicopathology data were obtained from the TCGA database. The immunohistochemical (IHC) staining was used to verify the expression of ECM2. Functional enrichment analyses, immune-related analyses, drug sensitivity, and mutation profile analyses were further conducted. Cox regression and Kaplan-Meier curves were utilized for survival analyses, while four external datasets were used to validate the prognostic role of ECM2. Furthermore, qRT-PCR, CCK-8, wound healing, and transwell assays were performed to confirm the function of ECM2 in gliomas.

RESULTS

The study found a significant upregulation of ECM2 expression with increasing glioma grades and a significant association between ECM2 expression and tumor immune infiltration. Cox regression verified the prognostic role of ECM2 in LGG patients (HR = 1.656, 95%CI = 1.055-2.600, p = 0.028). High ECM2 expression was significantly associated with poor outcome (p < 0.001). Four external datasets validated its prognostic value. After the knockdown of ECM2, the functional experiments showed a significant decrease in proliferation, migration, and invasion in glioma cell lines.

CONCLUSION

The study suggested the potential of ECM2 as a novel immune-associated prognostic biomarker and therapeutic target for glioma patients.

The gene signature of tertiary lymphoid structures within ovarian cancer predicts the prognosis and immunotherapy benefit

Ovarian cancer (OC) has the lowest survival rate among gynecologic malignancies. Ectopic lymphocyte aggregates, namely tertiary lymphoid structures (TLSs), have been reported as positive biomarkers for tumor prognosis. However, the related gene signature of tertiary lymphoid structure in ovarian cancer was less understood. Therefore, this study first exhibited the organizational patterns of tertiary lymphoid structure by H&E staining and immunohistochemistry (IHC), and confirmed the improved survival values of tertiary lymphoid structure and quantified tumor-infiltrating lymphocytes (CD20⁺ B cells and CD8⁺ T cells) in ovarian cancer patients. Secondly, we collected the genes involved in tertiary lymphoid structure from databases. By the univariate regression analysis, the tertiary lymphoid structure gene signature (CETP, CCR7, SELL, LAMP3, CCL19, CXCL9, CXCL10, CXCL11, and CXCL13) with prognostic value, characteristically of ovarian cancer, was constructed in the TCGA dataset and validated in the GSE140082 dataset. Thirdly, by performing CIBERSORT and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, we found that the high expression of this gene signature was positively correlated with developed immune infiltration and reduced immune escape. The improved IPS score and application in the IMvigor210 dataset received PD-L1 proved the predictive value of immunotherapy for this gene signature. Furthermore, this signature showed a better correlation between tumor mutation burden and classical checkpoint genes. In conclusion, Tertiary lymphoid structure plays important role in tumor immunity and the gene signature can be evaluated as a biomarker for predicting prognosis and guiding immunotherapy in ovarian cancer.

The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer

Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

Identification of differentially expressed genes at the single-cell level and prognosis prediction through bulk RNA sequencing data in breast cancer

Background: The invention and development of single-cell technologies have contributed a lot to the understanding of tumor heterogeneity. The objective of this research was to investigate the differentially expressed genes (DEGs) between normal and tumor cells at the single-cell level and explore the clinical application of these genes with bulk RNA-sequencing data in breast cancer.

Methods: We collected single-cell, bulk RNA sequencing (RNA-seq) and microarray data from two public databases. Through single-cell analysis of 23,909 mammary gland cells from seven healthy donors and 33,138 tumor cells from seven breast cancer patients, cell type-specific DEGs between normal and tumor cells were identified. With these genes and the bulk RNA-seq data, we developed a prognostic signature and validated the efficacy in two independent cohorts. We also explored the differences of immune infiltration and tumor mutational burden (TMB) between the different risk groups.

Results: A total of 6,175 cell-type-specific DEGs were obtained through the single-cell analysis between normal and tumor cells in breast cancer, of which 1,768 genes intersected with the bulk RNA-seq data. An 18-gene signature was constructed to assess the outcomes in breast cancer patients. The efficacy of the signature was notably prominent in two independent cohorts. The low-risk group showed higher immune infiltration and lower TMB. Among the 18 genes in the signature, 16 were also differentially expressed in the bulk RNA-seq dataset.

Conclusion: Cell-type-specific DEGs between normal and tumor cells were identified through single-cell transcriptome data. The signature constructed with these DEGs could stratify patients efficiently. The signature was also closely correlated with immune infiltration and TMB. Nearly all the genes in the signature were also differentially expressed at the bulk RNA-seq level.

Pan-Cancer Pyroptosis Analyses Identified Novel Immunology and Chemotherapy-Related Prognostic Signatures in Cancer Subtypes

Despite mounting evidence linking pyroptotic cell death to tumor growth, the clinical significance and disease mechanism of pyroptosis in cancer remain uncertain. In this study, we established a unique gene signature (π signature) that can be used as a predictive and prognostic tool in pyroptosis-related cancer subtypes. We found that the 13 core pyroptosis genes exerted opposite prognostic effects in different cancer types, which were subgrouped as pyroptosis positively related cancer and pyroptosis negatively related cancer. Subsequently, π signature was identified separately from the hub genes in pyroptosis positively related cancer and pyroptosis negatively related cancer subtypes. It was shown that π signature was well correlated with patient survival, pathological stages, tumor lymphocyte infiltration, and immunotherapy response. π signature was also applied as a predictive tool for chemotherapy drug responses and used as an independent factor for patient overall survival prediction. In short, this elaborated genetic signature could help us understand the oncogenic mechanism and pave the way for further therapeutic strategies based on pyroptosis.

A pyroptosis-related gene signature for prognostic and immunological evaluation in breast cancer

Purpose

Pyroptosis exerts an undesirable impact on the clinical outcome of breast cancer. Since any single gene is insufficient to be an appropriate marker for pyroptosis, our aim is to develop a pyroptosis-related gene (PRG) signature to predict the survival status and immunological landscape for breast cancer patients.

Methods

The information of breast cancer patients was retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the gene expressions of this signature in breast cancer. Its prognostic value was evaluated by univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, receiver operating characteristics (ROCs), univariate/multivariate analysis, and nomogram. Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to explore its potential biological function in breast cancer. The potential correlation between this signature and tumor immunity was revealed based on single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms.

Results

A PRG signature containing GSDMC, GZMB, IL18, and TP63 was created in a TCGA training cohort and validated in two validation GEO cohorts GSE58812 and GSE37751. Compared with a human mammary epithelial cell line MCF-10A, the expression levels of GSDMC, GZMB and IL18 were upregulated, while TP63 was found with lower expression level in breast cancer cells SK-BR-3, BT-549, MCF-7, and MDA-MB-231 using RT-qPCR assay. Based on univariate and multivariate Cox models, ROC curve, nomogram as well as calibration curve, it was revealed that this signature with high-risk score could independently predict poor clinical outcomes in breast cancer. Enrichment analyses demonstrated that the involved mechanism was tightly linked to immune-related processes. SsGSEA, ESTIMATE and CIBERSORT algorithms further pointed out that the established model might exert an impact on immune cell abundance, immune cell types and immune-checkpoint markers. Furthermore, individuals with breast cancer responded differently to these therapeutic agents based on this signature.

Conclusions

Our data suggested that this PRG signature with high risk was tightly associated with impaired immune function, possibly resulting in an unfavorable outcome for breast cancer patients.