Prognostic Score Model Research Articles

Prognostic model for unresectable hepatocellular carcinoma treated with dual PD-1 and angiogenesis blockade therapy

Background and aimsDual programmed death 1 (PD-1) and angiogenesis blockade therapy is a frontline treatment for hepatocellular carcinoma (HCC). An accepted model for survival prediction and risk stratification in individual...

Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma.

This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies. The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR. In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients. We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.

Identification and validation of a novel apoptosis-related prognostic risk score model for lung adenocarcinoma.

The prognostic roles of apoptosis-related genes (ARGs) in lung adenocarcinoma (LUAD) have not been fully elucidated. In this study, differentially expressed genes (DEGs) associated with apoptosis and the hub genes were further identified. The prognostic values of the ARGs were evaluated using the LASSO Cox regression method. Prognostic values were determined using Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves in the TCGA and GEO datasets. The correlations, mutation data, and protein expression of the 10 ARGs predictive models were also analyzed. We identified 130 differentially expressed ARGs. DEGs were used to split LUAD cases into two subtypes whose overall survival (OS) were significantly different (P = 0.025). We developed a novel 10-gene signature using LASSO Cox regression. In both TCGA and GEO datasets, the results of the K-M curve and log-rank test showed significant difference in the survival rate of patients in the high-risk group and low-risk group (P < 0.0001). According to the GO and KEGG analyses, ARGs were enriched in cancer-related terms. In both cohorts, the immune status of the high-risk group was significantly lower than that of the low-risk group. Based on the differential expression of the ARGs, we established a new risk model to predict the prognosis of patients with LUAD.

Lysyl oxidase-like 1 predicts the prognosis of patients with primary glioblastoma and promotes tumor invasion via EMT pathway.

Lysyl oxidase enzymes (LOXs), as extracellular matrix (ECM) protein regulators, play vital roles in tumor progression by remodeling the tumor microenvironment. However, their roles in glioblastoma (GBM) have not been fully elucidated. The genetic alterations and prognostic value of LOXs were investigated via cBioPortal. The correlations between LOXs and biological functions/molecular tumor subtypes were explored in The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). After Kaplan‒Meier and Cox survival analyses, a Loxl1-based nomogram and prognostic risk score model (PRSM) were constructed and evaluated by time-dependent receiver operating characteristic curves, calibration curves, and decision curve analyses. Tumor enrichment pathways and immune infiltrates were explored by single-cell RNA sequencing and TIMER. Loxl1-related changes in tumor viability/proliferation and invasion were further validated by CCK-8, western blot, wound healing, and Transwell invasion assays. GBM patients with altered LOXs had poor survival. Upregulated LOXs were found in IDH1-wildtype and mesenchymal (not Loxl1) GBM subtypes, promoting ECM receptor interactions in GBM. The Loxl1-based nomogram and the PRSM showed high accuracy, reliability, and net clinical benefits. Loxl1 expression was related to tumor invasion and immune infiltration (B cells, neutrophils, and dendritic cells). Loxl1 knockdown suppressed GBM cell proliferation and invasion by inhibiting the EMT pathway (through the downregulation of N-cadherin/Vimentin/Snai1 and the upregulation of E-cadherin). The Loxl1-based nomogram and PRSM were stable and individualized for assessing GBM patient prognosis, and the invasive role of Loxl1 could provide a promising therapeutic strategy.

Comprehensive Analysis of the Prognostic Implications and Biological Function of HDACs in Liver Hepatocellular Carcinoma.

Background: The prognostic significance and biological functions of the histone deacetylases (HDACs) gene family in liver hepatocellular carcinoma (LIHC) have not been fully investigated. Methods: Using Kaplan-Meier and Cox regression analysis, this study determined if HDAC genes were relevant for prognosis in LIHC. A regression model utilizing HDAC genes and the least absolute shrinkage and selection operator (LASSO) was created to foretell LIHC risk. A selective inhibitor of endogenous HDACs, CKD-581, was studied in vitro and in vivo to determine its effects on the development, invasion, migration, and proliferation of LIHC cell lines. Results: Six HDACs were identified as correlating with the prognosis of LIHC. Overall survival (OS) was found to be shorter in individuals with higher risk scores when compared to those with lower risk scores, according to survival study. Natural killer cell infiltration was higher in individuals with lower risk ratings, which was mainly explained by the type II interferon (IFN) response. Limiting the activity of endogenous HDACs caused LIHC cell death by preventing their migration, invasion, and proliferation. In vivo studies confirmed that blocking HDAC expression inhibited tumor growth in mice. Further mechanistic studies showed that inhibition of HDACs expression elevates the protein levels of P21 and P27, and reduces those of cyclins A2, B1, D1 and E1. Conclusions: The risk score prognostic model based on HDAC genes could provide a valuable prognostic biomarker for LIHC. CKD-581 prohibits LIHC progression via inhibiting the cell cycle signaling pathway. CKD-581 holds promise as a therapeutic agent for the clinical management of LIHC.

PIVKA-II combined with tumor burden score to predict long-term outcomes of AFP-negative hepatocellular carcinoma patients after liver resection.

This study aimed to establish a simple prognostic scoring model based on tumor burden score (TBS) and PIVKA-II to predict long-term outcomes of α-fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) patients. 511 patients were divided into the training cohort (n = 305) and the validation cohort (n = 206) at a ratio of 6:4. Receiver operating characteristic curves (ROC) were established to identify cutoff values of TBS and PIVKA-II. Kaplan-Meier curves were used to analyze survival outcomes. The multivariable Cox regression was used to identify variables independently associated with survival outcomes. The predictive performance of the TBS-PIVKA II score (TPS) model was compared with Barcelona clinic liver cancer (BCLC) stage and American Joint Committee on Cancer (AJCC TNM) stage. The present study established the TPS model using a simple scoring system (0, 1 for low/high TBS [cutoff value: 4.1]; 0, 1 for low/high PIVKA-II [cutoff value: 239 mAU/mL]). The TPS scoring model was divided into three levels according to the summation of TBS score and PIVKA-II score: TPS 0, TPS 1, and TPS 2. The TPS scoring model was able to stratify OS (training: p < 0.001, validation: p < 0.001) and early recurrence (training: p < 0.001; validation: p = 0.001) in the training cohort and the validation cohort. The TPS score was independently associated with OS (TPS 1 vs. 0, HR: 2.28, 95% CI: 1.01-5.17; TPS 2 vs. 0, HR: 4.21, 95% CI: 2.01-8.84) and early recurrence (TPS 1 vs. 0, HR: 3.50, 95% CI: 1.71-7.16; TPS 2 vs. 0, HR: 3.79, 95% CI: 1.86-7.75) in the training cohort. The TPS scoring model outperformed BCLC stage and AJCC TNM stage in predicting OS and early recurrence in the training cohort and the validation cohort. But the TPS scoring model was unable to stratify the late recurrence in the training cohort (p = 0.872) and the validation cohort (p = 0.458). The TPS model outperformed the BCLC stage and AJCC TNM stage in predicting OS and early recurrence of AFP-negative HCC patients after liver resection, which might better assist surgeons in screening AFP-negative HCC patients who may benefit from liver resection.

Systematic analysis based on the cuproptosis-related genes identifies ferredoxin 1 as an immune regulator and therapeutic target for glioblastoma

Glioblastoma multiforme (GBM) is recognized as the prevailing malignant and aggressive primary brain tumor, characterized by an exceedingly unfavorable prognosis. Cuproptosis, a recently identified form of programmed cell death, exhibits a strong association with cancer progression, therapeutic response, and prognostic outcomes. However, the specific impact of cuproptosis on GBM remains uncertain. To address this knowledge gap, we obtained transcriptional and clinical data pertaining to GBM tissues and their corresponding normal samples from various datasets, including TCGA, CGGA, GEO, and GTEx. R software was utilized for the analysis of various statistical techniques, including survival analysis, cluster analysis, Cox regression, Lasso regression, gene enrichment analysis, drug sensitivity analysis, and immune microenvironment analysis. Multiple assays were conducted to investigate the expression of genes related to cuproptosis and their impact on the proliferation, invasion, and migration of glioblastoma multiforme (GBM) cells. The datasets were obtained and prognostic risk score models were constructed and validated using differentially expressed genes (DEGs) associated with cuproptosis. To enhance the practicality of these models, a nomogram was developed.Patients with glioblastoma multiforme (GBM) who were classified as high risk exhibited a more unfavorable prognosis and shorter overall survival compared to those in the low risk group. Additionally, we specifically chose FDX1 from the differentially expressed genes (DEGs) within the high risk group to assess its expression, prognostic value, biological functionality, drug responsiveness, and immune cell infiltration. The findings demonstrated that FDX1 was significantly upregulated and associated with a poorer prognosis in GBM. Furthermore, its elevated expression appeared to be linked to various metabolic processes and the susceptibility to chemotherapy drugs. Moreover, FDX1 was found to be involved in immune cell infiltration and exhibited correlations with multiple immunosuppressive genes, including TGFBR1 and PDCD1LG2. The aforementioned studies offer substantial assistance in informing the chemotherapy and immunotherapy approaches for GBM. In summary, these findings contribute to a deeper comprehension of cuproptosis and offer novel perspectives on the involvement of cuproptosis-related genes in GBM, thereby presenting a promising therapeutic strategy for GBM patients.

Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma

Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.

The identification of N6-methyladenosine-related miRNAs predictive of hepatocellular carcinoma prognosis and immunotherapy efficacy.

Hepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis. The clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p. The expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04-2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells. The clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients.

Single-cell analysis extracted CAFs-related genes to established online app to predict clinical outcome and radiotherapy prognosis of prostate cancer.

Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa. We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy. The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram. The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

Integrating scRNA-seq and bulk RNA-seq to characterize infiltrating cells in the colorectal cancer tumor microenvironment and construct molecular risk models.

Colorectal cancer (CRC) is a malignancy that is both highly lethal and heterogeneous. Although the correlation between intra-tumoral genetic and functional heterogeneity and cancer clinical prognosis is well-established, the underlying mechanism in CRC remains inadequately understood. Utilizing scRNA-seq data from GEO database, we re-isolated distinct subsets of cells, constructed a CRC tumor-related cell differentiation trajectory, and conducted cell-cell communication analysis to investigate potential interactions across cell clusters. A prognostic model was built by integrating scRNA-seq results with TCGA bulk RNA-seq data through univariate, LASSO, and multivariate Cox regression analyses. Eleven distinct cell types were identified, with Epithelial cells, Fibroblasts, and Mast cells exhibiting significant differences between CRC and healthy controls. T cells were observed to engage in extensive interactions with other cell types. Utilizing the 741 signature genes, prognostic risk score model was constructed. Patients with high-risk scores exhibited a significant correlation with unfavorable survival outcomes, high-stage tumors, metastasis, and low responsiveness to chemotherapy. The model demonstrated a strong predictive performance across five validation cohorts. Our investigation involved an analysis of the cellular composition and interactions of infiltrates within the microenvironment, and we developed a prognostic model. This model provides valuable insights into the prognosis and therapeutic evaluation of CRC.

Molecular Characterization and Establishment of a Prognostic Model Based on Primary Immunodeficiency Features in Association with RNA Modifications in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Although immunotherapy is effective for some patients, most find it difficult to benefit from it. This study aims to explore the impact of specific immune pathways and their regulated molecular mechanisms in TNBC. The gene expression data of breast cancer patients were obtained from the TCGA and METABRIC databases. Gene set variation analysis (GSVA) revealed specific upregulation or abnormal expression of immunodeficiency pathways in TNBC patients. Multi-omics data showed significant differential expression of Primary Immunodeficiency Genes (PIDGs) in TNBC patients, who are prone to genomic-level variations. Consensus clustering was used in two datasets to classify patients into two distinct molecular subtypes based on PIDGs expression patterns, with each displaying different biological features and immune landscapes. To further explore the prognostic characteristics of PIDGs-regulated molecules, we constructed a four-gene prognostic PIDG score model and a nomogram using least absolute shrinkage and selection operator (LASSO) regression analysis in combination with clinicopathological parameters. The PIDG score was closely associated with the immune therapy and drug sensitivity of TNBC patients, providing potential guidance for clinical treatment. Particularly noteworthy is the close association of this scoring with RNA modifications; patients with different scores also exhibited different mutation landscapes. This study offers new insights for the clinical treatment of TNBC and for identifying novel prognostic markers and therapeutic targets in TNBC.

Novel molecular insights into pyroptosis in triple-negative breast cancer prognosis and immunotherapy.

Patients with triple-negative breast cancer (TNBC) often have a poor prognostic outcome. Current treatment strategies cannot benefit all TNBC patients. Previous findings suggested pyroptosis as a novel target for suppressing cancer development, although the relationship between TNBC and pyroptosis-related genes (PRGs) was still unclear. Gene expression data and clinical follow-up of TNBC patients were collected from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). PRGs were screened using weighted gene co-expression network analysis. Cox regression analysis and the least absolute shrinkage and selection operator (i.e. LASSO) technique were applied to construct a pyroptosis-related prognostic risk score (PPRS) model, which was further combined with the clinicopathological characteristics of TNBC patients to develop a survival decision tree and a nomogram. The model was used to calculate the PPRS, and then the overall survival, immune infiltration, immunotherapy response and drug sensitivity of TNBC patients were analyzed based on the PPRS. The PPRS model was closely related to clinicopathological features and can independently and accurately predict the prognosis of TNBC. According to normalized PPRS, patients in different cohorts were divided into two groups. Compared with the high-PPRS group, the low-PPRS group had significantly higher ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) score, immune score and stromal score, and it also had overexpressed immune checkpoints and significantly reduced Tumor Immune Dysfunction and Exclusion (TIDE) score, as well as higher sensitivity to paclitaxel, veliparib, olaparib and talazoparib. A decision tree and nomogram based on PPRS and clinical characteristics can improve the prognosis stratification and survival prediction for TNBC patients. A PPRS model was developed to predict TNBC patients' immune characteristics and response to immunotherapy, chemotherapy and targeted therapy, as well as their survival outcomes.

Identification of biomarkers associated with melanoma based on bioinformatics analysis

Background: Melanoma is an aggressive tumor caused by melanocytes characterized by a poor prognosis at the stage of metastasis. Therefore, it is crucial to identify biomarkers for progression and prognosis for the treatment of melanoma. Objectives: The study aimed to identify the specific genes related to the prognosis of melanoma utilizing bioinformatics analyses. Methods: Differentially expressed genes (DEGs) between melanoma tissues and normal tissues were screened from the GSE 3,189 gene expression profile of Gene Expression Omnibus (GEO). A co-expression network was constructed by weighted correlation network analysis (WGCNA). Functional enrichment analysis for DEGs was performed. The risk prognostic model and nomogram predictive model were established utilizing the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. Results: Using the differential expression analysis and WGCNA, 1,408 DEGs were screened between melanoma tissues and normal tissues. Functional enrichment analysis proved that these genes primarily participated in the cell cycle and mitotic phase regulation in cancer. In addition, 17 optimal DEGs were identified for constructing the risk score prognostic model. Cox regression analysis further revealed that ectonucleotide pyrophosphatase/phosphodiesterase 4 (ENPP4) and FGR proto-oncogene, Src family tyrosine kinase (FGR) were the key genes significantly associated with survival. A nomogram prediction model was established for individual survival probability by integrating pathological T/N/M stage, age, ENPP4, and FGR. High FGR or ENPP4 expression indicated a better prognosis in melanoma patients. Conclusion: This study identified FGR and ENPP4 as potentially useful prognostic biomarkers for melanoma. The corresponding risk score prognostic model and nomogram may be a reliable tool for predicting the prognosis of melanoma patients.

Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma

BackgroundOsteosarcoma (OS) is the most common primary malignancy of bone tumors. More and more ARHGAP family genes have been confirmed are to the occurrence, development, and invasion of tumors. However, its significance in osteosarcoma remains unclear. In this study, we aimed to identify the relationship between ARHGAP family genes and prognosis in patients with OS.MethodsOS samples were retrieved from the TCGA and GEO databases. We then performed LASSO regression analysis and multivariate COX regression analysis to select ARHGAP family genes to construct a risk prognosis model. We then validated this prognostic model. We utilized ESTIMATE and CIBERSORT algorithms to calculate the stroma and immune scores of samples, as well as the proportions of tumor infiltrating immune cells (TICs). Finally, we conducted in vivo and in vitro experiments to investigate the effect of ARHGAP28 on osteosarcoma.ResultsWe selected five genes to construct a risk prognosis model. Patients were divided into high- and low-risk groups and the survival time of the high-risk group was lower than that of the low-risk group. The high-risk group in the prognosis model constructed had relatively poor immune function. GSEA and ssGSEA showed that the low-risk group had abundant immune pathway infiltration. The overexpression of ARHGAP28 can inhibit the proliferation, migration, and invasion of osteosarcoma cells and tumor growth in mice, and IHC showed that overexpression of ARHGAP28 could inhibit the proliferation of tumor cells.ConclusionWe constructed a risk prognostic model based on five ARHGAP family genes, which can predict the overall survival of patients with osteosarcoma, to better assist us in clinical decision-making and individualized treatment.

The TAE score predicts prognosis of unresectable HCC patients treated with TACE plus lenvatinib with PD-1 inhibitors.

Transcatheter arterial chemoembolization combined with lenvatinib and PD-1 inhibitors (triple therapy) exhibits promising efficacy for unresectable hepatocellular carcinoma (uHCC). We aimed to evaluate the prognosis of patients with uHCC who received triple therapy and develop a prognostic scoring model to identify patients who benefit the most from triple therapy. A total of 246 patients with uHCC who received triple therapy at eight centers were included and assigned to the training and validation cohorts. Prognosis was evaluated by the Kaplan-Meier curves. The prognostic model was developed by utilizing predictors of overall survival (OS), which were identified through the Cox proportional hazards model. In the training cohort, the 3-year OS was 52.0%, with a corresponding progression-free survival (PFS) of 30.6%. The median PFS was 13.2months [95% confidence interval, 9.7-16.7]. Three variables (total bilirubin ≥ 17μmol/L, alpha-fetoprotein ≥ 400ng/mL, and extrahepatic metastasis) were predictors of poor survival and were used for developing a prognostic model (TAE score). The 2-year OS rates in the favorable (0 points), intermediate (1 point), and dismal groups (2-3 points) were 96.9%, 61.4%, and 11.4%, respectively (p < 0.001). The PFS was also stratified according to the TAE score. These findings were confirmed in an external validation cohort. Triple therapy showed encouraging clinical outcomes, and the TAE score aids in identifying patients who would benefit the most from triple therapy.

A Prognostic Score Model for Relapsed/Refractory Acute Myeloid Leukemia Patients after Allogeneic Hematopoietic Stem Cell Transplantation

Introduction Patients with acute myeloid leukemia (AML) can achieve complete remission (CR) after receiving initial therapy, while some patients will develop relapsed/refractory AML (R/R-AML), which has a poor prognosis. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) has been shown to be effective in improving the prognosis of R/R-AML patients. It is necessary to evaluate the factors that can affect the prognosis of patients receiving allo-HSCT for R/R-AML, including leukemia-free survival (LFS) and overall survival (OS). In previous studies, such prognostic evaluation factors included cytogenetics, response to salvage induction therapy before transplantation, time from diagnosis to transplantation, and acute graft-versus-host disease (aGVHD). However, there is still a lack of a prognostic model that can systematically predict prognosis at an early stage after transplantation. In this study, a prognostic model was established to predict the 2-year prognosis of R/R-AML patients receiving allo-HSCT. Method Refractory AML is defined as no CR after 2 courses of intensive induction treatment, while relapsed AML is defined as ≥ 5% bone marrow blasts, reappearance of blasts in the blood or development of extramedullary AML after CR. In this retrospective study, patients receiving allo-HSCT for R/R-AML from January 1, 2015, to June 30, 2021 at Peking University People's Hospital were identified and divided into a derivation cohort and an external validation cohort according to the time of transplantation. Detailed data of all patients were collected from medical records. Categorical variables were analyzed using the 2-tailed Fisher's exact test. Continuous variables were analyzed using the Mann‒Whitney U test. Factors with a P value &amp;lt; 0.10 were included in the multivariate Cox analysis. Factors that were identified as independent prognostic factors in the multivariate Cox analysis were included in the score model. Results A total of 200 patients who received allo-HSCT for relapsed/refractory AML were identified. The median age of diagnosis of AML was 39.5 years (range, 18-68 years), and the median age of transplantation was 40 years (range, 20-68 years). In the overall survival analysis, age at diagnosis, age at transplantation, sex, white blood cell (WBC) count at diagnosis, WBC engraftment time and duration from AML diagnosis to allo-HSCT were identified as potential prognostic factors. In the multivariate Cox analysis, age &amp;gt; 55 years (HR, 3.587; 95% CI, 1.099 to 11.702; P =.034), WBC level at AML diagnosis &amp;lt; 4.0×10^9/L (HR, 4.548; 95% CI, 1.209 to 17.104; P =.009) and duration from AML diagnosis to HSCT &amp;gt; 250 days (HR, 6.541; 95% CI, 0.941 to 5.488; P =.058) were identified as independent prognostic factors. A model for the survival rate of R/R AML patients receiving allo-HSCT was developed according to the regression coefficients of the multivariate analysis. Patients who met the criteria of age &amp;gt; 55 years, WBC level at AML diagnosis &amp;lt; 4.0×10^9/L or duration from AML diagnosis to HSCT &amp;gt; 250 days received 1 point for each variable. Points 0, 1-2, and 3 were defined as low, median and high risk, respectively. This prognostic score model performed well in the external cohort (AuROC =.783; 95% confidence interval,.599 to.966). In the LFS analysis, WBC count at AML diagnosis &amp;lt; 4.0×10^9/L (HR, 3.120; 95% CI, 1.198 to 8.122; P =.020) and female sex (HR, 2.778; 95% CI, 1.013 to 7.617; P =.047) were related to poor LFS . Conclusion In this study, we developed a prognostic score model of 2-year OS for R/R‒AML patients after receiving allo-HSCT. Independent prognostic factors included age at transplantation (&amp;gt; 55 years), WBC count at AML diagnosis (&amp;lt; 4.0×10^9/L) and duration from AML diagnosis to HSCT (&amp;gt; 250 days). This model can effectively evaluate the 2-year mortality of R/R-AML patients after receiving allo-HSCT and help improve the prognosis of patients. There was a higher mortality for patients with R/R‒AML after receiving allo-HSCT when the WBC count at the time of AML diagnosis was lower than the normal range. Considering that the main cause of death of patients in this study was infection, this correlation may be related to the destruction and reconstruction of the immune system.

Risk Factors and Prognostic Scoring Model for 28-Day Mortality in Hematologic Patients with Stenotrophomonas M altophilia Bacteremia

Objective: Patients with hematologic diseases are at a higher risk for Stenotrophomonasmaltophilia(SM) bacteremia, which poses significant burden on morbidity and mortality. The aim of this study was to evaluate the early predictors of 28-day mortality in hematologic patients with SM bacteremia and to construct a reliable prediction model which can be used to identify patients at higher risk for mortality. Methods: This retrospective analysis of 131 hospitalized patients diagnosed with SM bacteremia was conducted in a tertiary blood disease center in Tianjin, China, from January 2012 to June 2023. Univariate analysis and multivariate logistic regression analysis were used to identify prognostic risk factors and develop a prognostic mortality scoring system and internally validated by bootstrap method (resampling = 1000). Results: The overall 28-day mortality rate in our study was 29.77% (39/131). The mean age was 45 (IQR 30.5-56.0) years and 70 individuals (53.44%) were diagnosed with myeloid malignancies. The existing condition severity-related scores did not perform well in predicting 28-day mortality for the patients in our population (AUROC values: SOFA: 0.842; ECOG: 0.831; APACHE Ⅱ: 0.709; SAPS Ⅱ: 0.675; aCCI: 0.654; Pitt score: 0.629). Through univariate and multivariate analysis, we identified four independent risk factors associated with 28-day mortality: SOFA score (OR = 3.67, 95% CI 1.99-6.78, P &amp;lt; 0.001; score = corresponding SOFA score), age ≥ 60 years (OR = 6.95, 95% CI 1.44-33.55, P = 0.0158; score = 2), bloody sputum or hemoptysis (OR = 5.32, 95% CI 1.08-26.29, P = 0.0402; score = 2) and neutropenia duration ≥ 25 days (OR = 3.92, 95% CI 1.01-15.26, P = 0.0487; score = 1). These factors were used to develop a prognostic scoring model, which yielded good discrimination (AUC = 0.889, 95% CI: 0.829-0.949) and calibration (Hosmer-Lemeshow goodness-of-fit test P = 0.998), with similar performance in the internal bootstrap validation (AUC = 0.888). Furthermore, all patients were divided into the high-risk group (≥ 7 points) and the low-risk group (&amp;lt; 7 points) based on the model and compared using Kaplan-Meier curves and log-rank tests. Additionally, we founded that the use of trimethoprim-sulfamethoxazole (TMP/SMX) after infection was associated with a better prognosis ( P = 0.012). Conclusions: The prediction model, utilizing SOFA score, age, bloody sputum or hemoptysis and neutropenia duration, proves to be an effective tool for early identification of hematologic patients with SM bacteremia at high risk of mortality.

Development and validation of a disulfidptosis-related scoring system to predict clinical outcome and immunotherapy response in acute myeloid leukemia by integrated analysis of single-cell and bulk RNA-sequencing.

Background: Disulfidptosis is a metabolically relevant mode of cell death, and its relationship with acute myeloid leukemia (AML) has not been clarified. In this study, disulfidptosis scores were computed to examine the potential biological mechanisms. Methods: Consensus clustering was applied to detect disulfidptosis-related molecular subtypes. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a DRG prognostic model. Results: DRGs are upregulated in AML and associated with poor prognosis. The higher the disulfidptosis activity score, the worse the clinical outcome for patients, accompanied by increased immune checkpoint expression and tumor marker pathway activity. The two molecular subtypes exhibited distinct prognoses and tumor microenvironment (TME) profiles. A prognostic risk score model was established using six DRGs, and the AML cohort was divided into high- and low-risk score groups. Patients in the high-risk group experienced significantly worse prognosis, which was validated in seven AML cohorts. Receiver Operating Characteristic (ROC) curve analysis indicated that the area under the curve values for risk score prediction of 1-, 3-, and 5-year survival were 0.779, 0.714, and 0.778, respectively. The nomogram, in conjunction with clinicopathological factors, further improved the accuracy of prognosis prediction. The high-risk score group exhibited a higher somatic mutation frequency, increased immune-related signaling pathway activity, and greater immune checkpoint expression, suggesting a certain degree of immunosuppression. Patients with advanced age and higher cytogenetic risk also had elevated risk scores. According to drug prediction and AML anti-PD-1 therapy cohort analysis, the low-risk score group displayed greater sensitivity to chemotherapy drugs like cytarabine and midostaurin, while the high-risk score group was more responsive to anti-PD-1 therapy. Finally, clinical samples were collected for sequencing analysis, confirming that the progression of myeloid leukemia was associated with a higher risk score and a negative disulfidptosis score, suggesting that the poor prognosis of AML may be associated with disulfidptosis resistance. Conclusion: In conclusion, a systematic analysis of DRGs can help to identify potential disulfidptosis-related mechanisms and provide effective new biomarkers for prognosis prediction, TME assessment, and the establishment of personalized treatment plans in AML.

Construction of an anoikis-associated lncRNA-miRNA-mRNA network reveals the prognostic role of β-elemene in non-small cell lung cancer

β-Elemene is the main active ingredient in Curcumae Rhizoma that exerts antitumour effects. Anoikis affects tumour development through various biological pathways in non-small cell lung cancer (NSCLC), but the regulation between β-elemene and anoikis remains to be explored. First, we explored the molecular expression patterns of anoikis-associated genes (AAGs) using consensus clustering and characterized the impact of AAGs on patient prognosis, clinical characteristics, and genomic instability. In addition, we revealed that AAG regulatory genes have rich interactions with β-elemene targets, and established a lncRNA-miRNA-mRNA network to explain the effect of β-elemene on anoikis. Finally, to reveal the prognostic effect of their correlation, the prognostic scoring model and clinical nomogram of β-elemene and anoikis were successfully established by least absolute shrinkage and selection operator (LASSO) and random forest algorithms. This prognostic scoring model containing noncoding RNA (ncRNA) can indicate the immunotherapy and mutational landscape, providing a novel theoretical basis and direction for the study of the antitumour mechanism of β-elemene in NSCLC patients.