Significant Predictor Of Prognosis Research Articles

Analysis of the Prognosis Prediction Ability of a Necroptosis-Related Gene Signature and its Relationship With the Hepatocellular Carcinoma Immune Microenvironment Using Bioinformatics Analysis and Experimental Validation.

Background: Hepatocellular carcinoma (HCC) is one of the most malignant cancers and has a poor prognosis. The immune microenvironment is closely related to the drug sensitivity of a tumor. Necroptosis was reported to be a key factor for HCC. The prognostic value of necroptosis-related genes and their association with the tumor immune microenvironment are still unknown. Methods: Necroptosis-related genes that could comprise a signature for predicting the prognosis of HCC cases were identified using univariate analysis and least absolute shrinkage and selection operator Cox regression analysis. The association between this prognosis prediction signature and HCC immune microenvironment was analyzed. The immunological activities and drug sensitivities were compared between different risk score groups identified using the prognosis prediction signature. The expression levels of the five genes comprising the signature were validated using RT-qPCR. Results: A prognosis prediction signature consisting of five necroptosis-related genes was constructed and validated. Its risk score was = (0.1634 × PGAM5 expression) + (0.0134 × CXCL1 expression) - (0.1007 × ALDH2 expression) + (0.2351 × EZH2 expression) - (0.0564 × NDRG2 expression). The signature was found to be significantly associated with the infiltration of B cells, CD4+ T cells, neutrophils, macrophages, and myeloid dendritic cells into the HCC immune microenvironment. The number of infiltrating immune cells and the expression levels of immune checkpoints in the immune microenvironment of high-risk score patients were higher. Sorafenib and immune checkpoint blockade were determined to be ideally suited for treating high-risk score patients and low-risk score patients, respectively. Finally, RT-qPCR results confirmed that the expression levels of EZH2, NDRG2, and ALDH2 were significantly down-regulated in HuH7 and HepG2 cells compared to those in LO2 cells. Conclusion: The necroptosis-related gene signature developed herein can classify patients with HCC according to prognosis risk well and is associated with infiltration of immune cells into the tumor immune microenvironment.

Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases.

Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations. TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients. We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.

Identification of a 6-gene signature associated with ferroptosis for predicting the prognosis in prostate cancer

Ferroptosis is intimately correlated with the development of cancers. We aimed to identify ferroptosis-related prognostic signatures for prognosis prediction of prostate adenocarcinoma (PRAD). The expression profile and clinical data of patients were from The Cancer Genome Atlas Program (TCGA) database. The Cox regression and Lasso analyses were utilized to construct a multigene signature, and the Kaplan-Meier (K-M), receiver operating characteristic (ROC), and decision curve analysis (DCA) curves were used to validate the predictive effect. Additionally, pathway enrichment analyses were performed to explore the potential mechanism associated with biomarkers. In this study, 41 ferroptosis-related genes (FRGs) were differentially expressed between PRAD and normal tissues. Then, we finally constructed a risk model consisting of 6 signatures (Transferrin Receptor (TFRC), Ferritin Heavy Chain 1 (FTH1), Poly (RC) Binding Protein 2 (PCBP2), Acyl-CoA Synthetase Long Chain Family Member 3 (ACSL3), Prion Protein (PRNP), and Lysophosphatidylcholine Acyltransferase 3 (LPCAT3)) among 41 biomarkers. The K-M, ROC, and DCA curves all validated the fine predictive performance of our prognostic signature. We also revealed the significant clinical value of each signature in PRAD. The enrichment analysis suggested the correlation of these genes with the calcium signaling pathway, Transforming Growth Factor Beta 1 (TGF-β), and Wingless-Type MMTV Integration Site Family (WNT) pathways, implying that these genes might be involved in the migration of PRAD. In conclusion, the 6-gene ferroptosis-related signature could serve as a novel biomarker for predicting the prognosis in PRAD. Their function in cancer migration needs further investigation.

Identification of a ferroptosis-related gene signature for prognosis prediction in colorectal cancer patients and relationship with vitamin D

Ferroptosis is a promising colorectal cancer (CRC) treatment strategy; however, its value in prognosis remains at an exploratory stage. Little research has been conducted on vitamin D and ferroptosis, although vitamin D has been shown to inhibit CRC through various mechanisms. A retrospective study was conducted using RNA-seq profiles and corresponding clinical information of CRC patients retrieved from TCGA and GEO databases.We used R package to process and analyze the data. We established the prognostic signature with elastic network regression model. KEGG was used to analyze pathways related to FRGs, and protein-protein interaction(PPI)was used to identify potential interactions with vitamin D. In HCT116 cells, the levels of cysteine (Cys), glutathione (GSH), and reactive oxygen species (ROS) and the expression of ferroptosis marker genes were measured by Western blot and qRT-PCR. Results showed, a prognostic signature containing 39 FRGs was established, and the Area Under Curve (AUC) of the 2nd, 5th, and 8th years were 0.81, 0.81, and 0.78, respectively. There were distinct differences in survival probability between the high- and low-risk groups, and the signature was applicable to stratified survival analysis based on tumor stage. The risk score possessed an independent prognostic value. Importantly, we found that vitamin D receptor (VDR) has a potential relationship with many FRGs, and vitamin D promotes ferroptosis in CRC cells and affects the expression of TP53, MAPK3, and SLC7A11. In summary, a signature with FRGs can effectively predict the prognosis of CRC. Vitamin D can promote ferroptosis in CRC.

Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: Evidence from bioinformatic and experiments.

The progression process of lung cancer can be accelerated by M2 macrophages. However, genes that affect M2 macrophage polarization remain unidentified. The Cancer Genome Atlas, Gene Expression Omnibus, and Arrayexpress databases were used to obtain open-access data. The analysis of public data was mostly performed with R studio. The RNA levels of specific genes were detected using quantitative real-time PCR. The proliferation ability of the cells was assessed by CCK8, colony formation, and EdU assays. Based on the multiple datasets, we noticed a poor prognosis in patients with high M2 macrophage infiltration. There were 114 genes differentially expressed between high and low M2 macrophages infiltrated samples, regarded as M2 macrophage-related genes. Subsequently, a prognosis prediction signature consisting of ABHD5, HS3ST2, TM6SF1, CAPZA2, LEPROT, HNMT, and MRO was identified and presented a satisfactory performance. The pathway enrichment results revealed a positive correlation between riskscore and enrichment scores for most immunotherapy-related positive terms. Also, there might be an increase in genomic instability among patients at high risk. Interestingly, low risk patients are most likely to benefit from PD-1 therapy, while high risk patients may benefit from CTLA-4 therapy. Meanwhile, the estimated IC50 of seven drugs differs significantly between two risk groups, including Cisplatin, Docetaxel, Doxorubicin, Gefitinib, Paclitaxel, Sunitinib and Vinorelbine. Moreover, further experiments indicated that HNMT was overexpressed and can enhance the proliferation ability in lung cancer cells. In summary, our study identified the molecules significantly affecting M2 macrophage infiltration and identified a prognosis signature that robustly indicated patients prognosis. Moreover, we validated the cancer-promoting effect of HNMT using in vitro experiments.

The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer

The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation cohort was obtained from the Gene Expression Omnibus (GEO) dataset (GSE70769). To explore the Gleason score (GS)-based prediction signature, we screened the differentially expressed genes (DEGs) between low- and high-GS groups, and then univariate Cox regression survival analysis and multiple Cox analyses were performed sequentially using the training cohort. The testing cohort was used to evaluate and validate the prognostic model's effectiveness, accuracy, and clinical practicability. In addition, the correlation analyses between the risk score and clinical features, as well as immune infiltration, were performed. We constructed and optimized a valid and credible model for predicting the prognosis of PCa recurrence using four GS-associated genes (SFRP4, FEV, COL1A1, SULF1). Furthermore, ROC and Kaplan-Meier analysis revealed a higher predictive efficiency for biochemical recurrence (BCR). The results showed that the risk model was an independent prognostic factor. Moreover, the risk score was associated with clinical features and immune infiltration. Finally, the risk model was validated in a testing cohort. Our data support that the GS-based four-gene signature acts as a novel signature for predicting BCR in PCa patients.

Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer.

Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data. Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues. A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected via the machine learning and prognostic analysis. RT-PCR, Western blot and IHC indicated that ESM1 was high expressed in tumor than normal with superior predictive performance of CRC survival. A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.

Early physical rehabilitation effectiveness in frail older patients hospitalized for community-acquired pneumonia: analysis of a nationwide database in Japan.

Frailty is a significant predictor of prognosis in older patients with community-acquired pneumonia (CAP). No effective therapy has been reported in frail patients with CAP, with frailty determined using the Hospital Frailty Risk Score (HFRS). To investigate whether early physical rehabilitation intervention would effectively minimize adverse outcomes in frail older patients (determined using the HFRS) hospitalized for CAP. This retrospective cohort analysis involved patients with CAP aged ≥ 65years enrolled in the Japanese Diagnostic Procedure Combination Database between 2014 and 2020 and assessed as being frail. We compared 30-day mortality and readmission rates for patients who did and who did not receive physical rehabilitation within three days of admission and evaluated the association between outcomes and receiving early physical rehabilitation using Cox regression models and inverse probability weighting (IPW) for sensitivity analysis. The analysis involved 31,133 frail older patients hospitalized for CAP (mean age 84.3 ± 6.3years; females, 49.1%), including 11,515 (37.0%) who received early physical rehabilitation. Cox regression analysis showed that early physical rehabilitation intervention was inversely associated with 30-day mortality and readmission rates. The IPW model also showed similar results. Early physical rehabilitation was associated with reduced risks of 30-day mortality, overall in-hospital mortality, and 30-day readmission rates in frail older patients with CAP. Early physical rehabilitation in frail older patients hospitalized for CAP may improve outcomes. This finding highlights the importance of simultaneously introducing the HFRS and early physical rehabilitation intervention into clinical practice for frail older patients with CAP.

Construction and validation of an aging-related gene signature for prognosis prediction of patients with breast cancer.

Breast cancer (BC) is an aging-related disease. Aging-related genes (ARGs) participate in the initiation and development of lung and colon cancer, but the prognosis signature of ARGs in BC has not been clearly studied. This study aimed to construct an ARGs signature to predict the prognosis of patients with breast cancer. Firstly, the expression data of ARGs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were collected. Then COX and least absolulute shrinkage and selection operator(LASSO) were performed to construct the ARGs prognostic signature. The correlation between the signature and immune cell infiltration, immunotherapeutic response and drug sensitivity were subsequently analysed. The TCGA nomogram was constructed by combining the signature with other clinical features, and was validated by using GEO database. After LASSO and COX regression analyses, a prognostic signature based on nine ARGs, namely, HSP90AA1, NFKB2, PLAU, PTK2, RECQL4, CLU, JAK2, MAP3K5, and S100B, was built by using the TCGA dataset. Moreover, this risk signature is closely related to immune cell infiltration, immunotherapeutic response, and responses to chemotherapy and targeted therapy. Subsequently, The calibration curve demonstrates that the nomogram agrees well with practical prediction results. The receiver operating characteristic curve and decision-making curve analysis demonstrate that ARG signature has the better prognosis diagnosis ability and clinical net benefits. Therefore, the proposed ARG prognosis signature is a new prognosis molecular marker of patients with BC, and it can provide good references to individual clinical therapy.

Evaluation of Forkhead BOX M1 (FOXM1) gene expression in colorectal cancer.

Forkhead Box M1 (FOXM1)-a key cell cycle regulator is a member of the Forkhead transcription factor family. It plays a key role in embryogenesis and cell proliferation and has been strongly linked to various solid tumors. We sought to understand the regulation of FOXM1 in colorectal cancer (CRC), as well as if and to what extent other clinicopathological characteristics are associated with FOXM1. The investigation comprised 98 CRC samples and normal tissues (controls). All colon cancer patients had a colonoscopy and targeted biopsy. All rectal cancer patients had a CT and MRI. Real-time PCR, Immunohistochemistry, and Western blotting were used to evaluate FOXM1 expression, and the findings were analyzed using SPSS (v.26). FOXM1 mRNA and protein expression were substantially upregulated in tumor tissues, with the majority of these proteins localized in nucleo-cytoplasm. Elevated protein levels of FOXM1 were strongly correlated with lower education level, larger tumor size, lymph node status, lymphovascular invasion (LVI), perineural invasion (PNI), lymph node metastasis (LNM), tumor invasion depth (subserosal and serosal invasion), late stage (III and IV), localization (nucleo-cytoplasmic), intensity (strong) and recurrence. Based on survival analysis, FOXM1 overexpression and nucleo-cytoplasmic localization were associated with shorter disease-free survival while stage and PNI were linked to poorer overall and disease-free survival. According to the results of the Cox regression analysis, stage and PNI were significant predictors of prognosis in CRC patients. FOXM1 expression was elevated in CRC and was linked to reduced disease-free survival. These findings support prior reports and hence FOXM1 can be an important prognostic marker for CRC and a promising therapeutic target. Additionally, we found a link between poor disease-free survival and FOXM1's nucleo-cytoplasmic localization. However, since the sample size of this study was small, further research is needed to validate our findings.

Identification of a ferroptosis-related long non-coding RNA signature for prognosis prediction of ovarian cancer.

Ovarian cancer is one of the deadliest malignant tumors. Ferroptosis is closely related to various cancers, including ovarian cancer, but the genes involved in regulating ferroptosis in ovarian cancer are still unclear. The aim of this study is to construct a long non-coding RNA (lncRNA) signature related to ferroptosis and evaluate its relationship with the prognosis and clinicopathological characteristics of patients with ovarian cancer. In this study, a prognostic risk model comprising 18 lncRNAs related to ferroptosis was obtained. Compared to the low-risk group, the high-risk group based on the FerRLSig score had significantly poorer overall survival (P < 0.001). The receiver operating characteristics curve supported the accuracy of the model, established a prognostic nomogram combining FerRLSig and clinical characteristics, and showed a good prognosis and survival risk stratification predictive power. In addition, Gene Set Enrichment Analysis (GSEA) showed that FerRLSig was involved in many malignant tumor-related immunomodulatory pathways. Based on the risk model, we found that immune status and immunotherapy, chemotherapy, and targeted therapy were significantly different between the high-risk and low-risk groups. This study provided a more in-depth understanding of the molecular and signaling pathways of ferroptosis in ovarian cancer and showed the impact of tumor microenvironment on ovarian cancer, as well as provided a prognostic model for ovarian cancer patients to guide the clinical treatment of ovarian cancer.

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma.

Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056–1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

A Novel Identified Necroptosis-Related Risk Signature for Prognosis Prediction and Immune Infiltration Indication in Acute Myeloid Leukemia Patients.

AML ranks second in the most common types of leukemia diagnosed in both adults and children. Necroptosis is a programmed inflammatory cell death form reported to be an innate immune effector against microbial and viral pathogens and recently has been found to play an eventful role in the oncogenesis, progression, and metastasis of cancer. This study is designed to explore the potential value of necroptosis in predicting prognostic and optimizing the current therapeutic strategies for AML patients. We collected transcriptome and clinical data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases and selected necroptosis-related genes with both differential significance and prognostic value. Six genes (YBX3, ZBP1, CDC37, ALK, BRAF, and BNIP3) were incorporated to generate a risk model with the implementation of multivariate Cox regression. The signature was proven to be an independent prognostic predictor in both training and validation cohorts with hazard ratios (HRs) of 1.51 (95% CI: 1.33–1.72) and 1.57 (95% CI: 1.16–2.12), respectively. Moreover, receiver operating characteristic (ROC) curve was utilized to quantify the predictive performance of the signature and satisfying results were shown with the area under the curve (AUC) up to 0.801 (3-year) and 0.619 (3-year), respectively. In addition, the subtyping of AML patients based on the risk signature demonstrated a significant correlation with the immune cell infiltration and response to immunotherapy. Finally, we incorporated risk signature with the classical clinical features to establish a nomogram which may contribute to the improvement of clinical management. To conclude, this study identified a necroptosis-related signature as a novel biomarker to improve the risk stratification, to inform the immunotherapy efficacy, and to indicate the therapeutic option of targeted therapy.

A metabolism-associated gene signature for prognosis prediction of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. In this study, we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 and 7 pathways were found to be significantly downregulated and upregulated, respectively, in HCC, revealing that tumor tissue lacks the ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found that metabolic heterogeneity correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by the Cox proportional hazards model, a six-gene-based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 was associated with an unfavorable prognosis but that high ADPGK, GOT2, MTHFS, and FTCD expression was associated with a favorable prognosis. Patients with higher metabolic scores had poor prognoses (p value = 2.19e-11, hazard ratio = 3.767, 95% CI = 2.555–5.555). By associating the score level with clinical features and genomic alterations, it was found that NAT had the lowest metabolic score and HCC with tumor stage III/IV the highest. qRT‒PCR results for HCC patients also revealed that tumor samples had higher score levels than NAT. Regarding genetic alterations, patients with higher metabolic scores had more TP53 gene mutations than those with lower metabolic scores (p value = 8.383e-05). Validation of this metabolic score model was performed using another two independent HCC cohorts from the Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting that this model may be used as a reliable tool for predicting the prognosis of HCC patients.

A Novel Ferroptosis-Related Signature for Prediction of Prognosis, Immune Profiles and Drug Sensitivity in Hepatocellular Carcinoma Patients.

Hepatocellular carcinoma (HCC) is a malignant disease with an increasing incidence and a high mortality rate. Ferroptosis, a novel type of cell death, has been reported to be closely associated with the progression of HCC. The aim of our study was to construct a novel ferroptosis-related signature (nFRGs) for prediction of prognosis, immune features and drug sensitivity of HCC patients. Data were obtained from the TCGA, ICGC, GSE104580, CCLE and IMvigor210 datasets, and the least absolute shrinkage and selection operator (LASSO) was used to construct nFRGs. In addition, the analyses involved in prognoses, molecular function, stemness indices, somatic mutation, responses to immunologic therapy, efficacy of transcatheter arterial chemoembolization (TACE) therapy and drug sensitivity were performed using diverse packages of R 4.1.3 between the low- and high-risk groups. The nFRGs included seven ferroptosis-related genes. Our results showed that nFRGs was an independent risk factor for prognoses of HCC patients, and HCC patients in the high-risk group presented with worse prognosis. Compared with the results of other studies, nFRGs was superior to other promising signatures in predicting prognoses of patients with HCC. In addition, most of the enriched pathways of differentially expressed genes (DEGs) between these subgroups were related to immune features. The molecular functions, genetic mutation and mRNAsi were varied between the high- and low-risk groups. Moreover, we observed significant immunosuppression state in the high-risk group. Patients in the high-risk group might benefit from immunotherapy, whereas patients in the low-risk group may be susceptible to TACE therapy. Finally, five sensitive drugs and four sensitive drugs were screened for patients in the high- and low-risk groups, respectively. nFRGs may served as a novel biomarker of prognosis and aid in personalized therapeutic strategies for patients with HCC.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.

A Noval Established Cuproptosis-Associated LncRNA Signature for Prognosis Prediction in Primary Hepatic Carcinoma.

The copper ion content in the body maintains homeostasis, and when dysregulated, it can produce cytotoxicity and induce cell death through a variety of pathways. Cuproptosis refers to copper ions combining directly with acylated molecules, leading to the accumulation of oligomerization of lipoylated protein and subsequent downregulation of iron-sulfur cluster proteins; this induces proteotoxic stress and cell death. This study on the relationship between cuproptosis-related lncRNAs (CRLns) and the prognosis of primary hepatic carcinoma (PHC) has important clinical guiding significance for the diagnosis and treatment of PHC. Prognosis-related CRLRs were identified via rank-sum tests, correlational analyses, and univariate Cox regression, and a CRLR risk-scoring model (CRLRSM) was constructed using LASSO Cox regression. Patients were divided into high-risk and low-risk groups based on the median CRLRSM scores. Variance analysis for cuproptosis-related genes, gene set enrichment analysis, and correlational analysis for risk and immunity were performed using boxplots. Quantitative polymerase chain reactions were used to verify the CRLR levels in PHC cell lines. The study results showed that patients in the CRLRSM high-risk group had worse survival rates than those in the low-risk group. The PHC stage and risk score were independent prognostic factors for hepatocellular carcinoma. There were 7 CRLRs (MIR210HG, AC099850.3, AL031985.3, AC012073.1, MKLN1-AS, KDM4A-AS1, and PLBD1-AS1) associated with PHC prognosis, primarily through cellular metabolism, growth, proliferation, apoptosis, and immunity. In conclusion, the overexpression of 7 CRLRs in patients with PHC indicates a poor prognosis.

Comprehensive Analysis of the Immune Implication of AKAP12 in Stomach Adenocarcinoma.

A kinase anchor protein 12 (AKAP12) as a tumor suppressor in various cancers has been extensively studied and confirmed. However, its immune implication in stomach adenocarcinoma (STAD) remains uncertain. Here, using The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), Cancer Cell Line Encyclopedia (CCLE), integrated repository portal for tumor-immune system interactions (TISIDB), and Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) database, we systematically analyzed the immune correlation of AKAP12 from three aspects including immune infiltration cells, immune-related pathways, and immunomodulators and developed a AKAP12-related 4-gene signature for prognosis prediction. Our results showed that AKAP12 mRNA and protein levels were downregulated in STAD patients, and its expression was positively related to CD4+ T cells and macrophages. In addition, the immune cell infiltration levels were associated with AKAP12 gene copy number deletion in STAD. Based on CCLE database, we found that AKAP12 coexpressed genes were enriched in several immune- and cancer-related pathways, which was further validated by Gene Set Enrichment Analysis (GSEA). Moreover, we identified 46 immunomodulators that were significantly related to AKAP12 expression using TISIDB database, and these immunomodulators were involved in immune-related pathways including Th17 cell differentiation and natural killer cell-mediated cytotoxicity. Additionally, based on the 46 AKAP12-related immunomodulators, a 4-gene risk prediction signature was developed using the Cox regression model. The risk signature was identified as an independent prognostic factor, which can accurately predict the prognosis of patients with STAD, showing good predictive performance. Furthermore, we constructed a prognostic nomogram and calibration to predict and assess patient survival probabilities by integrating the risk score and other clinical factors. In conclusion, our study provides strong evidence that AKAP12 is closely related to tumor immunity in STAD from three aspects: immune infiltration cells, immune pathways, and immunomodulators. More importantly, the AKAP12-related prognostic signature may have a good application prospect for clinical practice.

A Novel Oxidative Phosphorylation-Associated Gene Signature for Prognosis Prediction in Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common type of malignant tumor with high morbidity and mortality. The oxidative phosphorylation (OXPHOS) metabolic pathway produces adenosine triphosphate (ATP) by delivering electrons to transmembrane protein complexes in the mitochondria. This research was dedicated to identifying an OXPHOS-associated signature for the assessment of prognosis of HCC patients. A total of 371 HCC patients from the Cancer Genome Atlas (TCGA) and 231 HCC patients from the International Cancer Genome Consortium (ICGC) with RNA expression data and clinical data were employed as construction and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to establish a multigene signature in the TCGA cohort, and the ICGC cohort was used for validation. The prognostic value of the risk signature was evaluated using univariate and multivariate Cox regression, Kaplan–Meier curves, and receiver operating characteristic (ROC) curves. The potential enrichment of biological functions was investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Meanwhile, we analyzed the correlation between the risk score and the tumor microenvironment (TME). A five-gene signature including ATP6V0B, ATP6V1C1, ATP6V1E1, TIMM9, and UQCRH was identified by LASSO Cox regression to classify patients into low- and high-risk groups. ROC curve analysis indicated that the five-gene signature is a prospective prognostic factor in HCC patients. Univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent prognostic factor for overall survival (OS). Functional analysis showed that differentially expressed genes (DEGs) between the low- and high-risk groups were enriched in mitosis and the cell cycle pathway. In addition, the five-gene signature was associated with innate immune cell infiltration, immune subtypes, and tumor stemness. A novel OXPHOS-associated gene signature can be used for prognostic prediction for patients with HCC.

A Novel Ferroptosis-Related Gene Signature for Prognosis Prediction in Ewing Sarcoma.

Ferroptosis, as a form of programmed cell death independent of apoptosis, has been demonstrated that plays a major role in tumorigenesis and cancer treatment. A comprehensive analysis of ferroptosis-related genes (FRGs) may lead to a novel choice for the treatment of Ewing sarcoma (ES). Here, 148 differentially expressed FRGs (DEFRGs) were identified between normal and ES tissue. And the GO and KEGG analyses of DEFRGs indicated that these genes were enriched in cancer and immune-related signaling pathways. Then, the GSE17679 cohort was randomly divided into train and test cohorts. Based on the train cohort, AURKA, RGS4, and RIPK1 were identified as key genes through the univariate Cox regression analysis, the random survival forest algorithm, and the multivariate Cox regression analysis and utilized to establish a prognostic FRG signature. The validation results demonstrated that the gene signature has not only excellent prediction performance and generalization ability but is also good at predicting the response of immunotherapy and chemotherapy. Subsequent analysis indicated that all 3 key genes play key roles in tumor immunity and prognosis of ES. Of these, AURKA was highly associated with EWSR1, which was verified by a single-cell dataset (GSE130019). Therefore, the 3 genes may be potential therapeutic targets for ES. At the end of this study, we also constructed an accurate nomogram that helps clinicians to assess the survival time of ES patients. In conclusion, our study constructed an excellent gene signature, which is helpful in improving the prognosis of ES patients.