Prognostic Score Model Research Articles

Construction of a novel prognostic scoring model for HBV-ACLF liver failure based on dynamic data

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients.

A five-gene prognosis model based on lysine β-hydroxybutyrylation site genes to predict the survival and therapy response in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is one of the most malignancy diseases. Lysine β-hydroxybutyrylation (Kbhb) has been reported to involve various metabolism and cancer progression. MethodsData from online databases (TCGA and GEO) were retrieved for the selection of differential expressed Kbhb site genes (DTRGs). Univariate cox and LASSO analysis were performed to identify the prognostic DTRGs. Based on the optimal DTRGs, a prognostic risk score model was established. Kaplan-Meier and Receiver operator characteristic analysis were conducted to evaluate the predicting ability of the prognosis model. Generated with clinical data, independent analysis and nomogram model were performed. Finally, the differences of survival, immune cell levels, immunotherapy response, drug sensitivity between high- and low-risk groups were explored. ResultsA total of 63 DTRGs were identified in PAAD, and these genes were related to cell division and apoptosis biological functions. Through univariate cox regression and LASSO analysis, 30 DTRGs were selected to be related to prognosis and five (KRT18, ANLN, ECT2, RBM5, and RBM6) were identified as the optimal DTRGs in PAAD. Based on the five optimal DTRGs, a prognostic risk score model was constructed, with promising predictive ability in PAAD survival (AUC >0.70). High-risk group showed lower survival rate (P < 0.05). Moreover, based on the risk score, a nomogram model was also established, which possessed perfect stability. Finally, lower risk score was related to higher immune cell levels, indicating an immune activation in low-risk status, which maybe the reason for the better survival in low-risk group. Furthermore, the immunotherapy response and drug sensitivity were all higher than that in low-risk groups (P < 0.05). ConclusionA five-gene prognosis risk model which exhibit promising predictive ability in survival is constructed for patients with PAAD.

Identification of a prognosis-related phagocytosis regulator gene signature in medulloblastoma

ObjectivesThe aims of this study were to screen for phagocytosis regulator-related genes in tissue samples from children with medulloblastoma (MB) and to construct a prognostic model based on those genes. MethodsDifferentially expressed genes between the MB and control groups were identified using the GSE50161 dataset from the Gene Expression Omnibus database. Prognosis-related phagocytosis regulator genes were selected from the GSE85217 dataset. Intersecting genes of the two datasets (differentially expressed prognosis-related phagocytosis regulator genes) were submitted to unsupervised cluster analysis to identify disease subtypes, after which the association between the subtypes and the immune microenvironment was analyzed. A prognostic risk score model was constructed, and functional, immune-related, and drug sensitivity analyses were performed. ResultsIn total, 23 differentially expressed prognosis-related phagocytosis regulator genes were identified, from which two disease subtypes (clusters 1 and 2) were classified. The prognoses of the patients in cluster 2 were significantly worse than those of the patients in cluster 1. The immune microenvironment differed significantly between the two subtypes. Finally, 10 genes (FAM81A, EZR, NDUFB9, RCOR1, FOXO4, NHLRC2, KIF23, PTPN6, SMAGP, and MED13) were selected to establish the prognostic risk score model. The prognosis in the low-risk group was better than that in the high-risk group. The model genes NDUFB9 and PTPN6 were positively correlated with M2 macrophages. ConclusionTen key phagocytosis regulator genes were screened to construct a prognostic model for MB. These genes may serve as key biomarkers for predicting the prognosis of patients with this type of brain cancer.

Development of a novel colon adenocarcinoma m6A-related lncRNA pair prognostic model.

Colon adenocarcinoma (COAD) is among the most prevalent malignancies. Changes to N6-methyladenosine (m6A), the most common RNA modification, can affect how COAD develops. Furthermore, the involvement of long noncoding RNA (lncRNA) in COAD is significant, and it exhibits a close association with m6A modification. Nevertheless, the prognostic significance of lncRNAs that are related to m6A modification in COAD remains unclear. This study aims to establish a m6A-related lncRNA pair signature and reveal its prognostic value in COAD. The current study utilized data from The Cancer Genome Atlas (TCGA) to investigate the predictive significance of m6A-related lncRNA pair signatures in COAD. The identification of m6A-related lncRNAs was conducted through co-expression analysis using the Pearson correlation coefficient. Then, the lncRNA pairs related to prognosis were identified using univariate Cox regression analysis. Receiver operating characteristic (ROC) curves were produced using the least absolute shrinkage and selection operator (LASSO) penalized with Cox analysis to predict overall survival (OS) in order to build a risk score prognostic model. The relationship among the risk scoring model and clinical characteristics, immune-related variables, and medication sensitivity was examined after identifying independent prognostic factors. Thirty-five of the 319 lncRNA pairings associated with m6A were linked to a pattern that predicted risk ratings. It was verified that the risk score model was a reliable predictor that stood alone from clinicopathological features. Differences between high- and low-risk groups were found in clinicopathological traits, immune-related variables, and medication sensitivity analysis according to correlation analyses. Based on paired differentially expressed m6A-related lncRNAs, the proposed COAD prognostic model demonstrated potential clinical predictive value.

Multi omics analysis of mitophagy subtypes and integration of machine learning for predicting immunotherapy responses in head and neck squamous cell carcinoma.

Mitophagy serves as a critical mechanism for tumor cell death, significantly impacting the progression of tumors and their treatment approaches. There are significant challenges in treating patients with head and neck squamous cell carcinoma, underscoring the importance of identifying new targets for therapy. The function of mitophagy in head and neck squamous carcinoma remains uncertain, thus investigating its impact on patient outcomes and immunotherapeutic responses is especially crucial. We initially analyzed the differential expression, prognostic value, intergene correlations, copy number variations, and mutation frequencies of mitophagy-related genes at the pan-cancer level. Through unsupervised clustering, we divided head and neck squamous carcinoma into three subtypes with distinct prognoses, identified the signaling pathway features of each subtype using ssGSEA, and characterized subtype B as having features of an immune desert using various immune infiltration calculation methods. Using multi-omics data, we identified the genomic variation characteristics, mutated gene pathway features, and drug sensitivity features of the mitophagy subtypes. Utilizing a combination of 10 machine learning algorithms, we have developed a prognostic scoring model called Mitophagy Subgroup Risk Score (MSRS), which is used to predict patient survival and the response to immune checkpoint blockade therapy. Simultaneously, we applied MSRS to single-cell analysis to explore intercellular communication. Through laboratory experiments, we validated the biological function of SLC26A9, one of the genes in the risk model. In summary, we have explored the significant role of mitophagy in head and neck tumors through multi-omics data, providing new directions for clinical treatment.

Mechanistic insights into super-enhancer-related genes as prognostic signatures in colon cancer.

Colon cancer (CC) is the most frequently occurring digestive system malignancy and is associated with a dismal prognosis. While super-enhancer (SE) genes have been identified as prognostic markers in several cancers, their potential as practical prognostic markers for CC patients remains unexplored. We obtained super-enhancer-related genes (SERGs) from the Human Super-Enhancer Database (SEdb). Transcriptome and relevant clinical data for colon cancer (CC) were sourced from the Gene Expression Omnibus (GEO) database. Subsequently, we identified up-regulated SERGs by the Weighted Gene Co-expression Network Analysis (WGCNA). Prognostic signatures were constructed via univariate and multivariate Cox regression analysis. We then delved into the mechanisms of these predictive genes by examining immune infiltration. We also assessed differential sensitivities to chemotherapeutic drugs between high- and low-SERGs risk patients. The critical gene was further validated using external datasets and finally confirmed by qRT PCR. We established a ten-gene risk score prognostic model (S100A11, LZTS2, CYP2S1, ZNF552, PSMG1, GJC1, NXN, and DCBLD2), which can effectively predict patient survival rates. This model demonstrated effective prediction capabilities in survival rates at 1, 3, and 5 years and was successfully validated using external datasets. Furthermore, we detected significant differences in immune cell infiltration between high- and low-SERGs risk groups. Notably, high-risk patients exhibited heightened sensitivity to four chemotherapeutic agents, suggesting potential benefits for precision therapy in CC patients. Finally, qRT-PCR validation revealed a significant upregulation of LZTS2 mRNA expression in CC cells. These findings reveal that the SERGs model could effectively predict the prognosis of CC.

Cervical cancer-specific long non-coding RNA landscape reveals the favorable prognosis predictive performance of an ion-channel-related signature model.

Ion channels play an important role in tumorigenesis and progression of cervical cancer. Multiple long non-coding RNA genes are widely involved in ion channel-related signaling regulation. However, the association and potential clinical application of lncRNAs in the prognosis of cervical cancer are still poorly explored. Thirteen patients with cervical cancer were enrolled in current study. Whole transcriptome (involving both mRNAs and lncRNAs) sequencing was performed on fresh tumor and adjacent normal tissues that were surgically resected from patients. A comprehensive cervical cancer-specific lncRNA landscape was obtained by our custom pipeline. Then, a prognostic scoring model of ion-channel-related lncRNAs was established by regression algorithms. The performance of the predictive model as well as its association with the clinical characteristics and tumor microenvironment (TME) status were further evaluated. To comprehensively identify cervical cancer-specific lncRNAs, we sequenced 26 samples of cervical cancer patients and integrated the transcriptomic results. We built a custom analysis pipeline to improve the accuracy of lncRNA identification and functional annotation and obtained 18,482 novel lncRNAs in cervical cancer. Then, 159 ion channel- and tumorigenesis-related (ICTR-) lncRNAs were identified. Based on nine ICTR-lncRNAs, we also established a prognostic scoring model and validated its accuracy and robustness in assessing the prognosis of patients with cervical cancer. Besides, the TME was characterized, and we found that B cells, activated CD8+ T, and tertiary lymphoid structures were significantly associated with ICTR-lncRNAs signature scores. We provided a thorough landscape of cervical cancer-specific lncRNAs. Through integrative analyses, we identified ion-channel-related lncRNAs and established a predictive model for assessing the prognosis of patients with cervical cancer. Meanwhile, we characterized its association with TME status. This study improved our knowledge of the prominent roles of lncRNAs in regulating ion channel in cervical cancer.

A risk score based on polyamine metabolism and chemotherapy-related genes predicts prognosis and immune cells infiltration of lung adenocarcinoma.

We aimed to explore whether the genes associated with both platinum-based therapy and polyamine metabolism could predict the prognosis of LUAD. We searched for the differential expression genes (DEGs) associated with platinum-based therapy, then we interacted them with polyamine metabolism-related genes to obtain hub genes. Subsequently, we analysed the main immune cell populations in LUAD using the scRNA-seq data, and evaluated the activity of polyamine metabolism of different cell subpopulations. The DEGs between high and low activity groups were screened to identify key DEGs to establish prognostic risk score model. We further elucidated the landscape of immune cells, mutation and drug sensitivity analysis in different risk groups. Finally, we got 10 hub genes associated with both platinum-based chemotherapy and polyamine metabolism, and found that these hub genes mainly affected signalling transduction pathways. B cells and mast cells with highest polyamine metabolism activity, while NK cells were found with lowest polyamine metabolism activity based on scRNA-seq data. DEGs between high and low polyamine metabolism activity groups were identified, then 6 key genes were screened out to build risk score, which showed a good predictive power. The risk score showed a universal negative correlation with immunotherapy checkpoint genes and the cytotoxic T cells infiltration. The mutation rates of EGFR in low-risk group was significantly higher than that of high-risk group. In conclusion, we developed a risk score based on key genes associated with platinum-based therapy and polyamine metabolism, which provide a new perspective for prognosis prediction of LUAD.

Single-cell analysis uncovers high-proliferative tumour cell subtypes and their interactions in the microenvironment of gastric cancer.

Gastric cancer (GC) remains a prominent malignancy that poses a significant threat to human well-being worldwide. Despite advancements in chemotherapy and immunotherapy, which have effectively augmented patient survival rates, the mortality rate associated with GC remains distressingly high. This can be attributed to the elevated proliferation and invasive nature exhibited by GC. Our current understanding of the drivers behind GC cell proliferation remains limited. Hence, in order to reveal the molecular biological mechanism behind the swift advancement of GC, we employed single-cell RNA-sequencing (scRNA-seq) to characterize the tumour microenvironment in this study. The scRNA-seq data of 27 patients were acquired from the Gene Expression Omnibus database. Differential gene analysis, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis were employed to investigate 38 samples. The copy number variation level exhibited by GC cells was determined using InferCNV. The CytoTRACE, Monocle and Slingshot analysis were used to discern the cellular stemness and developmental trajectory of GC cells. The CellChat package was utilized for the analysis of intercellular communication crosstalk. Moreover, the findings of the data analysis were validated through cellular functional tests conducted on the AGS cell line and SGC-7901 cell line. Finally, this study constructed a risk scoring model to evaluate the differences of different risk scores in clinical characteristics, immune infiltration, immune checkpoints, functional enrichment, tumour mutation burden and drug sensitivity. Within the microenvironment of GC, we identified the presence of 8 cell subsets, encompassing NK_T cells, B_Plasma cells, epithelial cells, myeloid cells, endothelial cells, mast cells, fibroblasts, pericytes. By delving deeper into the characterization of GC cells, we identified 6 specific tumour cell subtypes: C0 PSCA+ tumour cells, C1 CLDN7+ tumour cells, C2 UBE2C+ tumour cells, C3 MUC6+ tumour cells, C4 CHGA+ tumour cells and C5 MUC2+ tumour cells. Notably, the C2 UBE2C+ tumour cells demonstrated a close association with cell mitosis and the cell cycle, exhibiting robust proliferative capabilities. Our findings were fortified through enrichment analysis, pseudotime analysis and cell communication analysis. Meanwhile, knockdown of the transcription factor CREB3, which is highly active in UBE2C+ tumour cells, significantly impedes the proliferation, migration and invasion of GC cells. And the prognostic score model constructed with CREB3-related genes showcased commendable clinical predictive capacity, thus providing valuable guidance for patients' prognosis and clinical treatment decisions. We have identified a highly proliferative cellular subgroup C2 UBE2C+ tumour cells in GC for the first time. The employment of a risk score model, which is based on genes associated with UBE2C expression, exhibits remarkable proficiency in predicting the prognosis of GC patients. In our investigation, we observed that the knockdown of the transcription factor CREB3 led to a marked reduction in cellular proliferation, migration and invasion in GC cell line models. Implementing a stratified treatment approach guided by this model represents a judicious and promising methodology.

Establishment and validation of a prognostic scoring model based on disulfidptosis-related long non-coding RNAs in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD), a frequently occurring gastrointestinal tumour, is often detected late and has a poor prognosis. Long non-coding RNAs (lncRNAs) significantly affect tumour development. Recent studies have identified disulfidptosis as a previously unexplained form of cell death. Herein, we aimed to examine the predictive value of disulfidptosis-related lncRNA models for the clinical prognosis and immunotherapy of STAD. STAD-related transcriptomic data were obtained from The Cancer Genome Atlas (TCGA), whereas genes associated with disulfidptosis were identified from previously published papers. A risk prediction model for disulfidptosis-related lncRNAs was developed using the Cox regression and least absolute shrinkage selection algorithm methods. The accuracy of the model was confirmed using calibration curves, and the biological functions were analysed using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). Finally, the tumour mutation burden (TMB) and tumour immune dysfunction and exclusion (TIDE) algorithms were used to screen drugs that are sensitive to STAD. The risk prediction models were constructed using seven disulfidptosis-related lncRNAs. The validated results were consistent with the predicted ones, with significant survival differences. When combined with clinical data, the risk scores were used as independent prognostic markers. Based on the tumour mutation load, the high-risk patient group had a poorer survival rate as compared with the low-risk patient group. Further studies were conducted to understand the different groups' inconsistent responses to immune status; subsequently, relatively sensitive drugs were identified. Overall, seven markers of disulfidptosis-related lncRNAs associated with STAD were found to facilitate prognostic prediction, suggesting new ideas for immunotherapy and clinical applications.

Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.

Skin cutaneous melanoma (SKCM) is malignant cancer known for its high aggressiveness and unfavorable prognosis, particularly in advanced tumors. Anoikis is a specific pattern of programmed cell death associated with tumor regeneration, migration, and metastasis. Nevertheless, limited research has been conducted to investigate the function of anoikis in SKCM. Anoikis-related genes (ARGs) were extracted from Genecards to identify SKCM subtypes and to explore the immune microenvironment between the different subtypes. Prognostic models of SKCM were developed by LASSO COX regression analysis. Subsequently, the predictive value of risk scores in SKCM and the association with immunotherapy were further explored. Finally, the expression of 6 ARGs involved in the model construction was detected by immunohistochemistry and PCR. This study identified 20 ARGs significantly associated with SKCM prognosis and performed disease subtype analysis of samples based on these genes, different subtypes exhibited significantly different clinical features and tumor immune microenvironment (TIME) landscapes. The risk score prognostic model was generated by further screening and identification of the six ARGs. The model exhibited a high degree of sensitivity and specificity to predict the prognosis of individuals with SKCM. These high- and low-risk populations showed different immune statuses and drug sensitivity. Further immunohistochemical and PCR experiments identified significant differential expression of the six ARGs in tumor and normal samples. Anoikis-based features may serve as novel prognostic biomarkers for SKCM and may provide important new insights for survival prediction and individualized treatment development.

Enhancing prognostic prediction of invasive candidiasis among cancer patients with a serum C5a-based scoring model.

Invasive candidiasis poses a life-threatening risk, and early prognosis assessment is vital for timely interventions to reduce mortality. Serum C5a levels have recently been linked to prognosis, but confirmation in cancer patients is pending. We detected the concentrations of serum C5a in hospitalized cancer patients with invasive candidiasis from 2020 to 2023, and retrospectively analyzed the clinical data. 372 cases were included in this study, with a 90-day mortality rate of 21.8%. Candida albicans (48.7%) remained the predominant pathogen, followed by Candida glabrata (25.5%), Candida tropicalis (12.4%), and Candida parapsilosis (8.3%). Gastrointestinal cancer was the most diagnosed pathology type (37.6%). Serum C5a demonstrated a noteworthy correlation with 90-day mortality, and employing a cutoff value of 36.7ng/ml revealed significantly higher 90-day mortality in low-C5a patients (41.2%) compared to high-C5a patients (6.3%) (p < 0.001). We also identified no source control, no surgery, metastasis, or chronic renal failure independently correlated with the 90-day mortality. Based on this, a prognostic model combining C5a and clinical parameters was constructed, which performed better than models built solely on C5a or clinical parameters. Furthermore, we weighted scores to each parameter in the model and presented diagnostic sensitivity and specificity corresponding to different score points calculated by the model. We constructed a prognostic scoring model including serum C5a and clinical parameters, which would contribute to precise prognosis assessment and benefit the outcome among cancer patients.

Development of a Prognostic Risk Model Based on Oxidative StressRelated Genes for Platinum-Resistant Ovarian Cancer Patients.

Ovarian Cancer (OC) is a heterogeneous malignancy with poor outcomes. Oxidative stress plays a crucial role in developing drug resistance. However, the relationships between Oxidative Stress-related Genes (OSRGs) and the prognosis of platinum-resistant OC remain unclear. This study aimed to develop an OSRGs-based prognostic risk model for platinum-resistant OC patients. Gene Set Enrichment Analysis (GSEA) was performed to determine the expression difference of OSRGs between platinum-resistant and -sensitive OC patients. Cox regression analyses were used to identify the prognostic OSRGs and establish a risk score model. The model was validated by using an external dataset. Machine learning was used to determine the prognostic OSRGs associated with platinum resistance. Finally, the biological functions of selected OSRG were determined via in vitro cellular experiments. Three gene sets associated with oxidative stress-related pathways were enriched (p < 0.05), and 105 OSRGs were found to be differentially expressed between platinum-resistant and - sensitive OC (p < 0.05). Twenty prognosis-associated OSRGs were identified (HR: 0:562-5.437; 95% CI: 0.319-20.148; p < 0.005), and seven independent OSRGs were used to construct a prognostic risk score model, which accurately predicted the survival of OC patients (1-, 3-, and 5-year AUC=0.69, 0.75, and 0.67, respectively). The prognostic potential of this model was confirmed in the validation cohort. Machine learning showed five prognostic OSRGs (SPHK1, PXDNL, C1QA, WRN, and SETX) to be strongly correlated with platinum resistance in OC patients. Cellular experiments showed that WRN significantly promoted the malignancy and platinum resistance of OC cells. The OSRGs-based risk score model can efficiently predict the prognosis and platinum resistance of OC patients. This model may improve the risk stratification of OC patients in the clinic.

Construction of oxidative phosphorylation-related prognostic risk score model in uveal melanoma

BackgroundUveal melanoma (UVM) is a malignant intraocular tumor in adults. Targeting genes related to oxidative phosphorylation (OXPHOS) may play a role in anti-tumor therapy. However, the clinical significance of oxidative phosphorylation in UVM is unclear.MethodThe 134 OXPHOS-related genes were obtained from the KEGG pathway, the TCGA UVM dataset contained 80 samples, served as the training set, while GSE22138 and GSE39717 was used as the validation set. LASSO regression was carried out to identify OXPHOS-related prognostic genes. The coefficients obtained from Cox multivariate regression analysis were used to calculate a risk score, which facilitated the construction of a prognostic model. Kaplan-Meier survival analysis, logrank test and ROC curve using the time “timeROC” package were conducted. The immune cell frequency in low- and high-risk group was analyzed through Cibersort tool. The specific genomic alterations were analyzed by “maftools” R package. The differential expressed genes between low- or high-risk group were analyzed and performed Gene Ontology (GO) and GSEA. Finally, we verified the function of CYC1 in UVM by gene silencing in vitro.ResultsA total of 9 OXPHOS-related prognostic genes were identified, including NDUFB1, NDUFB8, ATP12A, NDUFA3, CYC1, COX6B1, ATP6V1G2, ATP4B and NDUFB4. The UVM prognostic risk model was constructed based on the 9 OXPHOS-related prognostic genes. The prognosis of patients in the high-risk group was poorer than low-risk group. Besides, the ROC curve demonstrated that the area under the curve of the model for predicting the 1 to 5-year survival rate of UVM patients were all more than 0.88. External validation in GSE22138 and GSE39717 dataset revealed that these 9 genes could also be utilized to evaluate and predict the overall survival of patients with UVM. The risk score levels related to immune cell frequency and specific genomic alterations. The DEGs between the low- and high- risk group were enriched in tumor OXPHOS and immune related pathway. In vitro experiments, CYC1 silencing significantly inhibited UVM cell proliferation and invasion, induced cell apoptosis.ConclusionIn sum, a prognostic risk score model based on oxidative phosphorylation-related genes in UVM was developed to enhance understanding of the disease. This prognostic risk score model may help to find potential therapeutic targets for UVM patients. CYC1 acts as an oncogene role in UVM.

MCM5 is a Novel Therapeutic Target for Glioblastoma.

MCM5 is a DNA licensing factor involved in cell proliferation and has been previously established as an excellent biomarker in a number of malignancies. Nevertheless, the role of MCM5 in GBM has not been fully clarified. The present study aimed to investigate the potential roles of MCM5 in the treatment of GBM and to elucidate its underlying mechanism, which is beneficial for developing new therapeutic strategies and predicting prognosis. Firstly, we obtained transcriptomic and proteomic data from the TCGA and CPTAC databases on glioma patients. Employing the DeSeq2 R package, we then identified genes with joint differential expression in GBM tissues subjected to chemotherapy. To develop a prognostic risk score model, we performed univariate and multivariate Cox regression analyses. In vitro knockdown and overexpression of MCM5 were used to further investigate the biological functions of GBM cells. Additionally, we also delved into the upstream regulation of MCM5, revealing associations with several transcription factors. Finally, we investigated differences in immune cell infiltration and drug sensitivity across diverse risk groups identified in the prognostic risk model. In this study, the chemotherapy-treated GBM samples exhibited consistent alterations in 46 upregulated and 94 downregulated genes at both the mRNA and protein levels. Notably, MCM5 emerged as a gene with prognostic significance as well as potential therapeutic relevance. In vitro experiments subsequently validated the role of increased MCM5 expression in promoting GBM cell proliferation and resistance to TMZ. Correlations with transcription factors such as CREB1, CTCF, NFYB, NRF1, PBX1, TEAD1, and USF1 were discovered during upstream regulatory analysis, enriching our understanding of MCM5 regulatory mechanisms. The study additionally delves into immune cell infiltration and drug sensitivity, providing valuable insights for personalized treatment approaches. This study identifies MCM5 as a key player in GBM, demonstrating its prognostic significance and potential therapeutic relevance by elucidating its role in promoting cell proliferation and resistance to chemotherapy.

An inflammation-related subtype classification for analyzing tumor microenvironment and clinical prognosis in colorectal cancer.

The inflammatory response plays an essential role in the tumor microenvironment (TME) of colorectal cancer (CRC) by modulating tumor growth, progression, and response to therapy through the recruitment of immune cells, production of cytokines, and activation of signaling pathways. However, the molecular subtypes and risk score prognostic model based on inflammatory response remain to be further explored. Inflammation-related genes were collected from the molecular signature database and molecular subtypes were identified using nonnegative matrix factorization based on the TCGA cohort. We compared the clinicopathological features, immune infiltration, somatic mutation profile, survival prognosis, and drug sensitivity between the subtypes. The risk score model was developed using LASSO and multivariate Cox regression in the TCGA cohort. The above results were independently validated in the GEO cohort. Moreover, we explored the biological functions of the hub gene, receptor interacting protein kinase 2 (RIPK2), leveraging proteomics data, in vivo, and in vitro experiments. We identified two inflammation-related subtypes (inflammation-low and inflammation-high) and have excellent internal consistency and stability. Inflammation-high subtype showed higher immune cell infiltration and increased sensitivity to common chemotherapeutic drugs, while inflammation-low subtype may be more suitable for immunotherapy. Besides, the two subtypes differ significantly in pathway enrichment and biological functions. In addition, the 11-gene signature prognostic model constructed from inflammation-related genes showed strong prognostic assessment power and could serve as a novel prognostic marker to predict the survival of CRC patients. Finally, RIPK2 plays a crucial role in promoting malignant proliferation of CRC cell validated by experiment. This study provides new insights into the heterogeneity of CRC and provides novel opportunities for treatment development and clinical decision making.

Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Exploring the role of coagulation-related genes in renal cell carcinoma: Implications for tumor microenvironment and prognostic biomarkers

PurposeClear cell renal cell carcinoma (ccRCC) frequently progresses to advanced stages due to tumor thrombus (TTs) formation. In this study, we aimed to investigate the role of coagulation-related pathway activation in the progression of ccRCC. MethodsConsensus clustering was used to identify coagulation-related molecular clusters of ccRCC patients from The Cancer Genome Atlas Program (TCGA) database. The function of coagulation and its correlation with the immune microenvironment were investigated. Protein-protein interactions and differential expression analysis were used to identify the key gene, which was verified by external experiments. The coagulation-associated risk score was constructed by cox proportional hazards regression. ResultsNotable disparities were detected in immune characteristics, prognostic differentiation and drug sensitivity between two coagulation-related clusters. Through the integration of clinical stage significance and protein-protein interactions, the key gene MMP9 was screened and it was significantly correlated with CD4+T cells, CD8+T cells and Treg cells. A coagulation-related risk score prognostic model was developed in the Cancer Genome Atlas (TCGA) cohort for risk stratification and prognosis prediction. The prognostic predictive values of the coagulation-related risk score were further authenticated in both TCGA-KIRC and E-MTAB-1980 cohorts. ConclusionThere is an obvious correlation between the coagulation and the tumor microenvironment in ccRCC. As a key coagulation-related gene, MMP9 may promote the progression of renal cell carcinoma by influencing immune infiltration of CD8+T cells and Treg cells. Additionally, the risk score could be used as a durable prognostic biomarker, which could assist in clinical decision making for ccRCC patients.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

A novel immunogenic cell death-related classification indicates the immune landscape and predicts clinical outcome and treatment response in acute myeloid leukemia

BackgroundImmunogenic cell death (ICD) is closely related to anti-tumor therapy and regulates the tumor microenvironment (TME). This study aims to explore the molecular characteristics of ICD in acute myeloid leukemia (AML) and to analyze the value of ICD-related biomarkers in TME indication, prognosis prediction, and treatment response evaluation in AML.MethodsSingle-sample gene set enrichment analysis was used to calculate the ICD score. LASSO regression was used to construct a prognostic risk score model. We also analyzed differences in clinical characteristics, immune landscape, immunotherapy response, and chemotherapy sensitivity between high-risk and low-risk patients.ResultsThis study identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune landscape between different ICD subtypes. Subsequently, a novel ICD-related prognostic risk score model was developed, which accurately predicted the prognosis of AML patients and was validated in nine AML cohorts. Moreover, there were significant correlations between risk scores and clinicopathological factors, somatic mutations, TME characteristics, immune cell infiltration, immunotherapy response, and chemosensitivity. We further validated the model gene expression in a clinically real-world cohort.ConclusionsThe novel ICD-related signatures identified and validated by us can serve as promising biomarkers for predicting clinical outcomes, chemotherapy sensitivity, and immunotherapy response in AML patients, guiding the establishment of personalized and accurate treatment strategies for AML.Graphical