The PANoptosis pathway is a recently identified mechanism of cellular death that involves the interaction and synchronization among cellular pyroptosis, apoptosis, and necrosis. More and more evidence suggests that PANoptosis is involved in the development and treatment of cancer. However, a comprehensive understanding of the influence of PANoptosis genes on prognostic value, tumor microenvironment characteristics, and therapeutic outcomes in patients with ovarian cancer (OC) remains incomplete.
The present work was designed to devise a PANoptosis signature for OC prognosis and explore its potential molecular function.
For this study, we obtained RNA sequencing and clinical data for ovarian cancer from the Cancer Genome Atlas (TCGA) and the GSE32062 cohort. Somatic variants of PANoptosis-related genes (PRGs) in OC were analyzed using GSCA. TCGA-OC and GSE32062 were used to construct training and validation cohorts for the model. Differential expression and correlation analyses were performed following the screening of genes with prognostic ability using univariate Cox analysis. Least Absolute Shrinkage nd Selection Operator (LASSO) regression was performed to construct PRG signature based on genes that were differentially expressed and correlated with prognosis. CIBER-SORT and ESTIMATE were used to analyze the relationship between the PRGs signature and immune infiltration. TIDE was used to analyze the relationship between the PRG signature and immune checkpoint genes. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of PRGs in OC.
The PRG signature was constructed using three prognostic genes (AIM2, APAF1, and ZBP1) in both TCGA-OC. The results showed that the PRGs signature had an AUC of 0.521, 0.546, and 0.598 in TCGA-OC and 0.620, 0.586, and 0.579 in GSE32062 to predict to predict OS at 1-, 3-, and 5-year intervals. Furthermore, a higher PRG signature risk score was significantly associated with shorter OS (HR = 1.693, 95% CI: 1.303 - 2.202, p = 8.34 × 10^-5 in TCGA-OC and HR = 1.63, 95% CI: 1.13 - 2.35, p = 0.009 in GSE32062). The risk score was identified as the independent prognostic factor for OC. Patients categorized according to their risk score exhibited notable variations in immune status, response to immunotherapy, and sensitivity to drugs. AIM2, APAF1, and ZBP1 were significantly aberrantly expressed in OC cell lines.
The PRG signature has the potential to serve as a prognostic predictor for OC and to provide new insights into OC treatment.

Different endogenous and exogenous mutational processes cause specific patterns of somatic mutations and mutational signatures. Although their biological research has been intensive, there are only rare studies assessing the possible prognostic role of mutational signatures. We used data from The Cancer Genome Atlas to study the associations between the activity of the mutational signatures and four survival endpoints in 18 types of malignancies. We further explored the prognostic differences according to, for example, the HPV status in head and neck squamous cell carcinomas and smoking status in lung cancers. The predictive power of the signatures over time was evaluated with a dynamic area under the curve model, and the links between mutational signature activities and differences in gene expression patterns were analyzed. In 12 of 18 studied cancer types, we identified at least one mutational signature whose activity predicted survival outcomes after adjusting for the established prognostic factors. For example, overall survival was associated with the activity of mutational signatures in nine cancer types and disease-specific survival in seven cancer types. The clock-like signatures SBS5 and SBS40 were most commonly associated with survival endpoints. The genes of the myosin binding protein and melanoma antigen families were among the most substantially dysregulated genes between the signatures of low and high activity. The differences in gene expression also revealed various enriched pathways. Based on these data, specific mutational signatures associate with the gene expression and have the potential to serve as strong prognostic factors in several cancer types.

BACKGROUND: M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified.
METHODS: Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC.
RESULTS: A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes.
CONCLUSIONS: These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

BACKGROUND: Endoplasmic reticulum stress (ERS) could be a strategy for treating malignant tumors. Moreover, long noncoding RNAs (lncRNAs) can promote tumorigenesis and progression, and forecast the prognosis of cancers. Nevertheless, the prognostic value of ERS-related lncRNAs has not been reported in lung adenocarcinoma (LUAD).
METHODS: The messenger RNA (mRNA), microRNA (miRNA) and lncRNA expression data related to LUAD were obtained in public databases (TCGA and GEO databases). Prognostic ERS-related differentially expressed lncRNAs (ERS-DELs) were obtained and used to build an ERS-related model by Cox regression analysis. Moreover, we further screened independent prognostic elements and built a nomogram. Furthermore, enrichment analysis of genes was conducted to investigate the functions. A lncRNA-miRNA-mRNA network was built to explore mechanism of lncRNAs. Finally, qRT-PCR was utilized to examine the expression levels of lncRNAs.
RESULTS: 30 ERS-DELs were identified, and an ERS-related signature was built based on AF131215.2, LINC00472, LINC01352, RP1-78O14.1, RP11-253E3.3, RP11-98D18.9, and SNHG12. Gene set enrichment analysis indicated that genes in the high-risk group were chiefly focused on the regulation of mRNA binding, and genes in the low-risk group were significantly focused on protein localization to cilia. A lncRNA-miRNA-mRNA network, containing 7 signature lncRNAs, 23 miRNAs, and 128 mRNAs, was also established. Eventually, quantitative real-time polymerase chain reaction was used to confirm that seven prognostic lncRNAs had a consistent expression with the analysis.
CONCLUSIONS: An ERS-related signature containing seven prognostic lncRNAs was built, which offered new thinking concerning the role of ERS-related lncRNAs in LUAD.

UNASSIGNED: Ferroptosis-related genes are correlated with the prognosis of patients with neuroblastoma (NB) remains unknown. This study aims to establish a prognostic ferroptosis-related gene model for predicting prognostic value in pediatric NB patients.
UNASSIGNED: The gene expression array and clinical characteristics of NB were downloaded from a public database. Correlations between ferroptosis-related genes and drug responses were analyzed by Childhood Cancer Therapeutics. The prognostic model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in NB patients from the ICGC cohort. The survival analysis was performed by Cox regression analysis. single-sample gene set enrichment analysis (ssGSEA) was used to quantify the immune cell infiltration correlation.
UNASSIGNED: Overall, 70 genes were identified as ferroptosis-related differentially expressed genes (DEGs) from 247 samples. Then, 13 ferroptosis-related genes were correlated with OS in the univariate Cox regression analysis. Five prognostic ferroptosis-related DEGs (pFR-DEGs) (STEAP3, MAP1LC3A, ULK2, MTOR and TUBE1), which were defined as the intersection of DEGs and prognostic ferroptosis-related genes, were identified and utilized to construct the prognostic signature. The correlation between five pFR-DEGs and drug responses was analyzed, and the box plots indicated that MTOR gene expression was highest, suggesting that MTOR expression is related to progressive NB disease. The receiver operating characteristic (ROC) curve showed that the model had moderate predictive power. The survival analysis indicated that the high-risk group had poor overall survival (OS) (P=2.087×10-06). Univariate and multivariate analyses identified the risk score as a significant prognostic risk factor [P=0.003, hazard ratio (HR) =1.933]. Immune cell infiltration correlation analysis showed that the high-risk group was related to more immune cells.
UNASSIGNED: The present study indicated a difference in ferroptosis-related gene expression between low- and high-risk NB patients. The ferroptosis-related signature could serve as a prognostic prediction tool. Additionally, immune infiltration might play an important role in different risk groups for NB patients.

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcomes. To comprehensively dissect the molecular landscape of GBM and heterogeneous macrophage clusters in the progression of GBM, this study integrates single-cell and bulk transcriptome data to recognize a distinct pro-tumor macrophage cluster significantly associated with the prognosis of GBM and develop a GBM prognostic signature to facilitate prior subtypes. Leveraging glioma single-cell sequencing data, we identified a novel pro-tumor macrophage subgroup, marked by S100A9, which might interact with endothelial cells to facilitate tumor progression via angiogenesis. To further benefit clinical application, a prognostic signature was established with the genes associated with pro-tumor macrophages. Patients classified within the high-risk group characterized with enrichment in functions related to tumor progression, including epithelial-mesenchymal transition and hypoxia, displays elevated mutations in the TERT promoter region, reduced methylation in the MGMT promoter region, poorer prognoses, and diminished responses to temozolomide therapy, thus effectively discriminating between the prognostic outcomes of GBM patients. Our research sheds light on the intricate microenvironment of gliomas and identifies potential molecular targets for the development of novel therapeutic approaches.

Programmed cell death (PCD) is a process that eliminates infected, damaged, or possibly neoplastic cells to sustain homeostatic multicellular organisms. Although long noncoding RNAs (lncRNAs) are involved in various types of PCD and regulate tumor growth, invasion, and migration, the role of PCD-related lncRNAs in bladder cancer still lacks systematic exploration. In this research, we integrated multiple types of PCD as pan-PCD and identified eight pan-PCD-related lncRNAs (LINC00174, HCP5, HCG27, UCA1, SNHG15, GHRLOS, CYB561D2, and AGAP11). Then, we generated a pan-PCD-related lncRNA prognostic signature (PPlncPS) with excellent predictive power and reliability, which performed equally well in the E-MTAB-4321 cohort. In comparison with the low-PPlncPS score group, the high-PPlncPS score group had remarkably higher levels of angiogenesis, matrix, cancer-associated fibroblasts, myeloid cell traffic, and protumor cytokine signatures. In addition, the low-PPlncPS score group was positively correlated with relatively abundant immune cell infiltration, upregulated expression levels of immune checkpoints, and high tumor mutation burden (TMB). Immunogenomic profiles revealed that patients with both low PPlncPS scores and high TMB had the best prognosis and may benefit from immune checkpoint inhibitors. Furthermore, for patients with high PPlncPS scores, docetaxel, staurosporine, and luminespib were screened as potential therapeutic candidates. In conclusion, we generated a pan-PCD-related lncRNA signature, providing precise and individualized prediction for clinical prognosis and some new insights into chemotherapy and immune checkpoint inhibitor therapy for bladder cancer.

OBJECTIVE: Nucleotide metabolic reprogramming as a hallmark of cancer is closely related to the occurrence and progression of cancer. We aimed to comprehensively analyze the nucleotide metabolism-related gene set and clinical significance in gliomas.
METHODS: The RNA sequencing data of 702 gliomas from the Cancer Genome Atlas (TCGA) dataset were included as the training set, and the RNA sequencing data from the other three datasets (CGGA, GSE16011, and Rembrandt) were used as independent validation sets. Survival curve, Cox regression analysis, time-dependent ROC curve and nomogram model were performed to evaluate prognostic power of signature. R language was the main tool for bioinformatic analysis and graphical work.
RESULTS: Based on the expression profiles of nucleotide metabolism-related genes, consensus clustering identified two robust clusters with different prognosis. We then developed a nucleotide metabolism-related signature that was closely related to clinical, pathological, and genomic characteristics of gliomas. And ROC curve showed that our signature was a potential biomarker for mesenchymal subtype. Survival curve and Cox regression analysis revealed signature as an independent prognostic factor for gliomas. In addition, we constructed a nomogram model to predict individual survival. Finally, functional analysis showed that nucleotide metabolism not only affected cell division and cell cycle, but also was associated with immune response in gliomas.
CONCLUSIONS: We developed a nucleotide metabolism-related signature to predict prognosis and provided new insights into the role of nucleotide metabolism in gliomas.

BACKGROUND: Recent advances in immunotherapy have elicited a considerable amount of attention as viable therapeutic options for several cancer types, the present study aimed to explore the immunotherapy-related genes (IRGs) and develop a prognostic risk signature in gastric carcinoma (GC) based on these genes.
METHODS: IRGs were identified by comparing immunotherapy responders and non-responders in GC. Then, GC patients were divided into distinct subtypes by unsupervised clustering method based on IRGs, and the differences in immune characteristics and prognostic stratification between these subtypes were analyzed. An immunotherapy-related risk score (IRRS) signature was developed and validated for risk classification and prognosis prediction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Besides, the predictive ability of the IRRS in immunotherapy response was also determined.
RESULTS: A total of 63 IRGs were identified, and 371 GC patients were stratified into two molecular subgroups with significantly different prognosis and immune characteristics. Then, an IRRS signature comprised of three IRGs (CENP8, NRP1, and SERPINE1) was constructed to predict the prognosis of GC patients in TCGA cohort. Importantly, external validation in multiple GEO cohorts further confirmed the universal applicability of the IRRS in distinct populations. Furthermore, we found that the IRRS was significantly correlated with patient\'s responsiveness to immunotherapy, GC patients with low IRRS are more likely to benefit from existing immunotherapy.
CONCLUSIONS: The risk score could serve as a robust prognostic biomarker, provide therapeutic benefits for immunotherapy and may be helpful for clinical decision making in GC patients.

A comparison of error rates between studies in forensic handwriting examination has been made in response to the lack of knowledge on error in this field. Fifteen indicators have been used to describe and compare error rates between studies. The results of each study have been processed to determine these indicators. Parameters related to the participants, conclusion scale, amount of time allocated to the task, and the task itself are also reported. The error rate indicators are provided for each study, and then combined across studies using values of mean and standard deviation. Experts were found to perform better than laypeople. For handwritten texts, absolute error rate for experts ranges from 0.32% to 5.85% (with a mean of 2.84 ± 2.33%), and for laypeople from 11.43% to 28.72% (with a mean of 21.40 ± 8.94%). For signatures, absolute error rate for experts ranges from 0% to 4.86% (with a mean of 2.50 ± 1.55%), and for laypeople from 10.68% to 28% (with a mean of 19.55 ± 7.05%). Overall, experts have an absolute error rate of 2.63 ± 1.73% (against 20.16 ± 7.20% for laypeople). Experts are also more likely to give inconclusive answers than laypeople. Overall, the rate of absolute inconclusive answers for experts is 21.96 ± 23.15% (against 8.13 ± 7.96% for laypeople). The comparative review of error rates presented in the present article contributes to validating the discipline by showing how research has been devoted to meeting the criteria of testing to be considered scientific.