Survival Receiver Operating Characteristic Research Articles

Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome

Background: The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS. Methods: The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan–Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice. Results: FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2. Conclusions: Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

A prognostic risk model based on lactate metabolism and transport-related lncRNAs for gastric adenocarcinoma

Background Increased lactate levels and metastasis in tumours are strongly associated with dismal outcomes. But prognostic value of lactate metabolism and transport-related lncRNAs in gastric adenocarcinoma (GA) patients remains unaddressed. Methods Gene expression data of GA were provided by The Cancer Genome Atlas. Lactate metabolism and transport-related gene data were accessed from GSEA. LncRNAs related to lactate metabolism and transport were identified by correlation analysis. A prognostic model was built by regression analysis. Validity of prognostic model was confirmed through survival analysis and receiver operating characteristic (ROC) curve. Immunity of each risk group was evaluated by immune correlation analysis .LncRNA-mRNA network was built by correlation analysis using Cytoscape software. Results A 12-gene prognostic model based on lactate metabolism and transport-related lncRNAs was built in GA. Median riskscore was utilized to classify GA samples into high- and low-risk groups. Survival analysis and ROC curves demonstrated validity of prognostic model. Most immune checkpoint molecules and TIDE scores were lower in the low-risk group. LINC01303 and LINC01545 may be the key prognostic factors in patients with GA. Conclusion This study successfully built a prognostic model of lactate metabolism and transport-related lncRNAs in GA. The findings guide prognostic management of GA patients.

Construction of a gene signature associated with anoikis to evaluate the prognosis and immune infiltration in patients with colorectal cancer.

Colorectal cancer (CRC) is characterized by a high metastasis rate, leading to poor prognosis and increased mortality. Anoikis, a physiological process, serves as a crucial barrier against metastasis. The objective of this research is to construct a prognostic model for CRC based on genes associated with anoikis. The study involved differential analysis and univariate Cox analysis of anoikis-related genes (ARGs), resulting in the selection of 47 genes closely associated with prognosis. Subsequently, unsupervised k-means clustering analysis was conducted on all patients to identify distinct clusters. Survival analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) analysis were performed on the different clusters to investigate associations within the clusters. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were utilized to assess metabolic pathway enrichment between the identified clusters. Furthermore, single-sample GSEA (ssGSEA) was applied to explore variations in immune infiltration. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk model based on ten signatures, which enabled the grouping of all samples according to their risk scores. The prognostic value of the model was validated using receiver operating characteristic (ROC) curves, area under the curve (AUC) calculations, and survival curves. Additionally, the expression of candidate genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR). Forty-seven survival-related ARGs were screened out. Somatic mutation analysis showed that these genes revealed a high mutation rate. Based on their expression, two clusters were identified. Cluster B patients exhibited a shortened overall survival and higher immune infiltration. A risk scoring model including ten genes was subsequently developed, which exhibited excellent prognostic predictive ability for CRC, as evidenced by the survival curve, ROC curve, and AUC curve. In addition, a nomogram was developed for predicting 3- and 5-year survival probabilities. The qRT-PCR results indicated the dissimilarities among the ten signatures in the tumor tissues and adjacent tissues of patients with CRC were fundamentally consistent with the analytical findings. This study comprehensively evaluated the prognostic significance of ARGs in CRC. It identified two distinct anoikis-related clusters and examined their respective immune microenvironments. Furthermore, an ARGs signature was developed to effectively predict the prognosis of CRC, thereby establishing a solid foundation for investigating the clinical prognostic role of anoikis in CRC.

Presence of tumor deposits is a strong indicator of poor outcome in patients with stage III colorectal cancers undergoing radical surgery

BackgroundTumor deposits (TDs) are emerging as an adverse prognostic factor in colorectal cancers (CRCs). However, TDs are somewhat neglected in the current staging system. It has been proposed either to add the TD count to the number of metastatic lymph nodes or to consider TDs as distant metastases; however, the scientific basis for these proposals seems questionable. This study aimed to investigate a new staging system. MethodsA total of 243 consecutive patients with stage III CRC who were undergoing curative resection and adjuvant chemotherapy were included. Each substage of stage III TNM was split according to the absence or presence of TDs. Receiver operating characteristic (ROC) curves and bootstrap methods were used to compare the current vs the new competing staging system in terms of oncologic outcome prediction. ResultsA high rate of TDs was recorded (124 cases [51%]). TDs were correlated with other adverse prognostic indicators, particularly vascular and perineural invasions, and showed a negative correlation with the number of removed lymph nodes, suggesting a possible multimodal origin. In addition, TDs were confirmed to have a negative impact on oncologic outcome, regardless of their counts. Compared with the current staging system, the new classification displayed higher values at survival ROC analysis, a significantly better stratification of patients, and effective identification of patients at high risk of recurrence. ConclusionsTDs negatively affect the prognosis in CRCs. A revision of the staging system could be useful to optimize treatments. The proposed new classification is easy to implement and more accurate than the current one.This study was registered online on the ClinicalTrials.gov website under the following identifier: NCT05923450.

Retracted: A nomogram for predicting in-hospital overall survival of hypertriglyceridemia-induced severe acute pancreatitis: A single center, cross-sectional study

BackgroundHypertriglyceridemia-induced severe acute pancreatitis (HTG-SAP) is a type of pancreatitis characterized by an abnormal elevation of plasma triglyceride. HTG-SAP has been associated with various complications and a high mortality rate. In this study, we established a nomogram for predicting the overall survival (OS) of HTG-SAP patients during hospitalization. Methods128 HTG-SAP cases hospitalized at the Affiliated Huadu Hospital, Southern Medical University, from 2019 to 2022 were analyzed retrospectively. A nomogram including prognostic factors correlated with OS during hospitalization was established by multivariate Cox regression analysis. We internally validated the nomogram using time-dependent (at 1-, 2-, and 3- months) survival receiver operating characteristic (SROC) and calibration curve with 500 iterations of bootstrap resampling. Time-dependent decision curve analysis (DCA) was employed to validate the clinical value of the nomogram. ResultsMultivariate Cox regression indicated that serum triglyceride, red blood cell distribution width (RDW), lactic acid, and interleukin-6 (IL6) were independent prognostic factors for OS of HTG-SAP patients during hospitalization and were used to construct a nomogram. The time-dependent area under the curve (AUC) values at 1-, 2-, and 3- months were 0.946, 0.913, and 0.929, respectively, and the Concordance index (C-index) of the nomogram was 0.916 (95%CI 0.871–0.961). The time-dependent calibration curves indicated good consistency between the observed and predicted outcomes. The time-dependent DCAs also revealed that the nomogram yielded a high clinical net benefit. After stratifying the included cases into two risk groups based on the risk score obtained from the nomogram, the high-risk group exhibited a significantly inferior overall survival (OS) compared to the low-risk group (p < 0.0001). ConclusionsOur nomogram exhibited good performance in predicting the overall survival of HTG-SAP patients during hospitalization.

Development and validation of a novel anoikis-related gene signature in clear cell renal cell carcinoma.

Despite numerous treatments available, clear cell renal cell carcinoma (ccRCC) remains a deadly and invasive cancer. Anoikis-related genes (ARGs) are essential regulators of tumor metastasis and development. However, the potential roles of ARGs in ccRCC remain unclear. Based on the TCGA-KIRC cohort and GeneCards database, we identified differentially expressed ARGs in ccRCC. Then a 4 ARGs risk model was created by Cox regression and LASSO. The Kaplan-Meier and receiver operating characteristic (ROC) curves were utilized to verify the predictive efficacy of the prognostic signature. Subsequently, the possible molecular mechanism of ARGs was investigated by functional enrichment analysis. To assess the immune infiltration, immune checkpoint genes, and immune function in various risk groups, single sample gene set enrichment (ssGSEA) algorithm was employed. Furthermore, the low-risk and high-risk groups were compared in terms of tumor mutation burden (TMB). Ultimately, we analyzed the protein expression of these four ARGs utilizing the western blot test. Four genes were utilized to create a risk signature that may predict prognosis, enabling the classification of KIRC patients into groups with low or high risk. The reliability of the signature was examined utilizing survival analysis and ROC analysis. According to the multivariate Cox regression result, the risk score was a reliable independent prognostic predictor for KIRC patients. The novel risk model could differentiate between KIRC patients with various clinical outcomes and represent KIRC's specific immune status. An analysis of the correlation of TMB and risk score indicated a positive correlation between them, with high TMB being potentially linked to worse outcomes. Based on our findings, the prognostic signature of ARGs may be employed as an independent prognostic factor for ccRCC patients. It may introduce alternative perspectives on prognosis evaluation and serve as a prominent reference for personalized and precise therapy in KIRC.

The predictive significance of a 5-m6A RNA methylation regulator signature in colorectal cancer

Colorectal cancer attacks the colon or rectum, with increasing morbidity and mortality globally. The RNA modification 6-methyladenine (m6A) is related to RNA modifications, playing a critical role in colorectal cancer. We aimed to identify prognostic signatures for colorectal cancer using risk prediction algorithms, and to validate these signatures using independent datasets and clinical samples. In this study, 175 cases in GSE17536 were assigned into two clusters using consistent clustering and PCA analysis. A multivariate Cox risk regression model revealed that among 21 m6A RNA methylation regulators, RBM15B, FTO, IGF2BP2, ZCCHC4, and KIAA1429 were remarkably associated with colorectal cancer patients' overall survival (OS); however, Kaplan–Meier (KM) survival assessment showed no significant association between these five regulators and colorectal cancer patients' prognosis. A 5-m6A RNA methylation regulator signature was established using LASSO algorithm. Risk scores of cases in GSE17536, GSE17537 and GSE75500 were calculated, and lower risk scores were associated with better DSS/OS. receiver operating characteristic (ROC) curve and the nomogram revealed the satisfactory predictive efficiency of the risk score model. The risk score could distinguish cases in Cluster1 and Cluster2 and normal and tumor tissues based on GSE37182. The prognostic variables for colorectal cancer patients were assessed using both univariate and multivariate Cox's proportional hazard regression models, which revealed that the stage and risk score were significant risk factors. In this study, a comprehensive set of integrative bioinformatics analyses was conducted to investigate the prognostic and diagnostic potential of a panel of 5 m6A RNA methylated regulators in colorectal cancer patients. The conducted studies included the use of several statistical methods, such as the LASSO regression model, KM survival evaluation, ROC curve, and univariate and multivariate Cox's proportional hazard regression analyses. The findings from these analyses collectively established the prognostic marker, highlighting its significance in predicting patient outcomes and diagnosing colorectal cancer.

A Ferroptosis-Related lncRNAs Signature Predicts Prognosis of Colon Adenocarcinoma.

(1) Ferroptosis is a type of cellular death caused by lipid-dependent iron peroxide, which plays a major role in cancer. Long noncoding RNAs (lncRNAs) are increasingly recognized as key regulating substances in ferroptosis; (2) RNA sequencing expressions and clinical data of 519 patients with colon adenocarcinoma (COAD) were downloaded from The Cancer Genome Atlas (TCGA) database. The expression levels of lncRNAs related to ferroptosis were screened with Pearson correlation analysis. Differential genes were enriched with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. LncRNAs related to ferroptosis were determined with univariate Cox regression and multivariate Cox regression analyses, and patients with COAD were classified into high- and low-risk subgroups according to their median risk score. The prognostic value was further examined, and the association between ferroptosis-related lncRNAs (frlncRNAs) and survival in patients with high and low risks of COAD was validated. A TCGA-COAD data set was used for receiver operating characteristic (ROC) analysis and detrended correspondence analysis (DCA) to assess prediction accuracy. Finally, a nomogram was constructed to predict survival probability; (3) We obtained a model consisting of a five-frlncRNAs signature comprising AP003555.1, AP001469.3, ITGB1-DT, AC129492.1, and AC010973.2 for determining the overall survival (OS) of patients with COAD. The survival analysis and ROC curves showed that the model had good robustness and predictive performance on the TCGA training set; (4) We found that a five-frlncRNAs signature may play a potential role in anti-COAD immunity. Risk characteristics based on frlncRNAs can accurately predict the prognosis and immunotherapy response of patients with COAD.

Development and Evaluation of a Novel Cuproptosis-Related lncRNA Signature for Gastric Cancer Prognosis.

According to a growing body of research, long noncoding RNAs (lncRNAs) participate in the progress of gastric cancer (GC). Cuproptosis is a distinct kind of programmed cell death, separating it from several other forms of programmed cell death that may be caused by genetic programming. Consequently, it is crucial to examine cuproptosis-related lncRNAs (CRLs) prognostic importance for the prognosis and treatment response in GC. The Cancer Genome Atlas (TCGA) database was used to retrieve RNA-seq data, pertinent clinical information, and somatic mutation data. A list of cuproptosis-related genes (CRGs) was obtained from prior work. We can distinguish prognostic CRLs using coexpression and univariate Cox analysis. Then, using CRLs, we developed a risk prediction model using multivariate Cox regression analysis and the least absolute shrinkage selection operator (LASSO) technique. To evaluate the diagnostic accuracy of this model, a Kaplan-Meier (K-M) survival analysis and a receiver operating characteristic (ROC) analysis were used. Moreover, the relationships between the risk model and immunological function, somatic mutation, and drug sensitivity were also investigated. Using the multivariate Cox analysis technique, we developed a signature based on cuproptosis-related four lncRNAs. We then classified patients into high-risk and low-risk groups based on the likelihood of unfavorable outcomes. The model was subjected to further testing, including K-M survival analysis, ROC analysis, and multivariate Cox regression analysis, all of which proved the model's exceptional robustness and predictive capacity. In addition, a nomogram that has a strong capacity for prediction ability was built. This nomogram included age, gender, clinical grade, pathologic stage, T stage, and risk score. Furthermore, we discovered substantial disparities in immune function and the number of mutations carried by tumors between the high-risk and low-risk groups. Moreover, this research also found that the IC50 values for 27 chemotherapeutic drugs varied widely across patients within high- and low-risk groups. The proposed 4-CRLs signature is a promising biomarker to predict clinical outcomes in GC.

Identification and validation of a novel prognostic model based on platinum resistance-related genes in bladder cancer

The depth of response to platinum in urothelial neoplasm tissues varies greatly. Biomarkers that have practical value in prognosis stratification are increasingly needed. Our study aimed to select a set of BC (bladder cancer)-related genes involved in both platinum resistance and survival, then use these genes to establish the prognostic model. Platinum resistance-related DEGs (differentially expressed genes) and tumorigenesis-related DEGs were identified. Ten most predictive co-DEGs were acquired followed by building a risk score model. Survival analysis and ROC (receiver operating characteristic) plot were used to evaluate the predictive accuracy. Combined with age and tumor stages, a nomogram was generated to create a graphical representation of survival rates at 1-, 3-, 5-, and 8-year in BC patients. The prognostic performance was validated in three independent BC datasets with platinum-based chemotherapy. The potential mechanism was explored by enrichment analysis. PPP2R2B, TSPAN7, ATAD3C, SYT15, SAPCD1, AKR1B1, TCHH, AKAP12, AGLN3, and IGF2 were selected for our prognostic model. Patients in high- and low-risk groups exhibited a significant survival difference with HR (hazard ratio) = 2.7 (p < 0.0001). The prognostic nomogram of predicting 3-year OS (overall survival) for BC patients could yield an AUC (area under the curve) of 0.819. In the external validation dataset, the risk score also has a robust predictive ability. A prognostic model derived from platinum resistance-related genes was constructed, we confirmed its value in predicting platinum-based chemotherapy benefits and overall survival for BC patients. The model might assist in therapeutic decisions for bladder malignancy.

A five-pseudouridylation-associated-LncRNA classifier for primary prostate cancer prognosis prediction.

Background: Prostate cancer (PCa) is one of the most common cancers in males around the globe, and about one-third of patients with localized PCa will experience biochemical recurrence (BCR) after radical prostatectomy or radiation therapy. Reportedly, a proportion of patients with BCR had a poor prognosis. Cumulative studies have shown that RNA modifications participate in the cancer-related transcriptome, but the role of pseudouridylation occurring in lncRNAs in PCa remains opaque. Methods: Spearman correlation analysis and univariate Cox regression were utilized to determine pseudouridylation-related lncRNAs with prognostic value in PCa. Prognostic pseudouridylation-related lncRNAs were included in the LASSO (least absolute shrinkage and selection operator) regression algorithm to develop a predictive model. KM (Kaplan-Meier) survival analysis and ROC (receiver operating characteristic) curves were applied to validate the constructed model. A battery of biological cell assays was conducted to confirm the cancer-promoting effects of RP11-468E2.5 in the model. Results: A classifier containing five pseudouridine-related lncRNAs was developed to stratify PCa patients on BCR and named the "ψ-lnc score." KM survival analysis showed patients in the high ψ-lnc score group experienced BCR more than those in the low ψ-lnc score group. ROC curves demonstrated that ψ-lnc score outperformed other clinical indicators in BCR prediction. An external dataset, GSE54460, was utilized to validate the predictive model's efficacy and authenticity. A ceRNA (competitive endogenous RNA) network was constructed to explore the model's potential molecular functions and was annotated through GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses. RP11-468E2.5 was picked for further investigation, including pan-cancer analysis and experimental validation. Preliminarily, RP11-468E2.5 was confirmed as a tumor promoter. Conclusion: We provide some evidence that pseudouridylation in lncRNA played a role in the development of PCa and propose a novel prognostic classifier for clinical practice.

Identification of drug targets and prognosis projection for uterine carcinosarcoma based on alternative splicing events

BackgroundUterine carcinosarcoma (UCS) is an invasive variant of endometrial cancer. The complicated heterogeneity and low frequency of UCS suggest the relevant research is lack. There is an urgent need to further explore the pathogenic mechanism and identify new biomarkers of UCS from different angels to improve its diagnosis and prognosis. ObjectiveThis study is to explore the importance of alternative splicing (AS) events in UCS, construct AS-based prognosis model and excavate key splicing factors (SFs). MethodUCS related gene transcriptome data and AS events data were collected from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq database. The AS events related to survival were determined by Cox regression analysis, Least absolute shrinkage and selection operator (Lasso) regression analysis and optimal subset analysis. The corresponding risk score was calculated and its efficiency on prognosis was evaluated by Kaplan-Meier (K-M) survival estimate and validated by the receiver operating characteristic (ROC) curve. The prognosis model was constructed with risk score and clinic characters as independent variables to predict patients’ survival. On the other hand, Kendall test was applied to inspect the correlation between the SFs and the prognosis-related AS events and a AS-SF network was constructed. Finally, the key SFs were screened through network nodes analysis and survival analysis. ResultSeven AS events the most related to survival were detected and the risk score was obtained. K-M survival estimate and ROC curve validation suggested the risk score was effective. Then Cox model was constructed based on the risk score and a nomogram model was obtained which provided the highest prediction accuracy of 95%. Through the AS-SF network analysis, 16 SFs were screened, among which four survival-related SFs were eventually obtained. ConclusionThe prognosis model could predict the survival rate of UCS patients by their clinical characters and AS-based risk score. And four newly discovered SFs could reveal the molecular mechanism of UCS and act as the potential drug targets and prognosis biomarkers.

Fibrinogen and antithrombin III are associated with in-hospital mortality among critically ill patients with acute kidney injury

Objectives Coagulation factors participates in the inflammatory cascade, known to play a crucial role in the development of acute kidney injury (AKI). Thus, it’s likely that some factors may be associated with AKI. Among them, low levels of fibrinogen and antithrombin III (ATIII) activity have been proved to increase mortality in patients with sepsis. Moreover, they are also reported to be associated with higher incidence of AKI. However, the association between coagulation parameters, especially fibrinogen and ATIII, and prognosis of AKI has not been examined. Methods Data were acquired from Multiparameter Intelligent Monitoring in Intensive Care Database IV (MIMIC-IV) version 1.0. Cox proportional hazards regression model was used to estimate the relationship between coagulation parameters and in-hospital mortality in critically ill patients with AKI. Subgroup analysis was also conducted to assess the robustness of the association. Restricted cubic spline (RCS) curve was utilized to examine the nonlinear relationships between fibrinogen or ATIII and in-hospital mortality. Kaplan–Meier method was used to estimate cumulative incidence of mortality by fibrinogen or ATIII levels. Receiver-operating characteristic (ROC) curve was plotted and area under curve was calculated to evaluate predictive ability of fibrinogen or ATIII. Results A total of 5914 eligible patients were enrolled in fibrinogen cohort study and 115 patients were eligible for ATIII cohort study. The baseline of low fibrinogen (<150 mg/dL) or ATIII (<80%) activity was associated with significantly higher in-hospital mortality (fibrinogen HR [95% CIs] 2.01 [1.79, 2.27]; ATIII 3.73 [1.11, 12.54]). The HR [95% CIs] of low fibrinogen remained significant 1.29 (1.13, 1.48) in multivariate analysis. The RCS curve showed nearly linear relationship. Subgroup analysis also proved the robustness of the association between fibrinogen and in-hospital mortality. Kaplan–Meier survival curve and ROC demonstrated the predictive capability of fibrinogen and ATIII. Conclusion Low fibrinogen is an independent predictor of in-hospital mortality in critically ill patients with AKI. Low ATIII activity is also likely to impact the risk of in-hospital death.

Pyroptosis-Related lncRNA Prognostic Model for Renal Cancer Contributes to Immunodiagnosis and Immunotherapy.

BackgroundRenal clear cell cancer (ccRCC) is one of the most common cancers in humans. Thus, we aimed to construct a risk model to predict the prognosis of ccRCC effectively.MethodsWe downloaded RNA sequencing (RNA-seq) data and clinical information of 539 kidney renal clear cell carcinoma (KIRC) patients and 72 normal humans from The Cancer Genome Atlas (TCGA) database and divided the data into training and testing groups randomly. Pyroptosis-related lncRNAs (PRLs) were obtained through Pearson correlation between pyroptosis genes and all lncRNAs (p < 0.05, coeff > 0.3). Univariate and multivariate Cox regression analyses were then performed to select suitable lncRNAs. Next, a novel signature was constructed and evaluated by survival analysis and ROC analysis. The same observation applies to the testing group to validate the value of the signature. By gene set enrichment analysis (GSEA), we predicted the underlying signaling pathway. Furthermore, we calculated immune cell infiltration, immune checkpoint, the T-cell receptor/B-cell receptor (TCR/BCR), SNV, and Tumor Immune Dysfunction and Exclusion (TIDE) scores in TCGA database. We also validated our model with an immunotherapy cohort. Finally, the expression of PRLs was validated by quantitative PCR (qPCR).ResultsWe constructed a prognostic signature composed of six key lncRNAs (U62317.1, MIR193BHG, LINC02027, AC121338.2, AC005785.1, AC156455.1), which significantly predict different overall survival (OS) rates. The efficiency was demonstrated using the receiver operating characteristic (ROC) curve. The signature was observed to be an independent prognostic factor in cohorts. In addition, we found the PRLs promote the tumor progression via immune-related pathways revealed in GSEA. Furthermore, the TCR, BCR, and SNV data were retrieved to screen immune features, and immune cell scores were calculated to measure the effect of the immune microenvironment on the risk model, indicating that high- and low-risk scores have different immune statuses. The TIDE algorithm was then used to predict the immune checkpoint blockade (ICB) response of our model, and subclass mapping was used to verify our model in another immunotherapy cohort data. Finally, qPCR validates the PRLs in cell lines.ConclusionThis study provided a new risk model to evaluate ccRCC and may be pyroptosis-related therapeutic targets in the clinic.

Significant correlation between HSPA4 and prognosis and immune regulation in hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Heat shock proteins (HSPs) are involved in the occurrence, progression, and immune regulation of tumors. Therefore, HSPs have been considered potential therapeutic targets. Here, we aimed to elucidate the value of HSP family A (Hsp70) member 4 (HSPA4) in the diagnosis and predicting prognosis of HCC, and its relationship with immune cell infiltration, immune cell biomarkers, and immune checkpoints. Gene mutation, DNA methylation, and the pathway involved in HCC were also analyzed.MethodsThe gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to compare HSPA4 expression, and the results were confirmed by immunohistochemical staining of clinical samples. R package was used to analyze the correlation between HSPA4 and cancer stage, and to establish receiver operating characteristic (ROC) curve of diagnosis, time-dependent survival ROC curve, and a nomogram model. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation, and their effect on prognosis. The Tumor Immune Estimation Resource (TIMER) was used to analyze immune cell infiltration, immune cell biomarkers, and immune checkpoints. The STRING database was used to analyze protein–protein interaction network information. Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of HSPA4 and its functional partner genes.ResultsOverexpression of HSPA4 was identified in 25 cancers. Overexpression of HSPA4 considerably correlated with cancer stage and alpha-fetoprotein (AFP) level in HCC. Patients with higher HSPA4 expression showed poorer prognosis. HSPA4 expression can accurately identify tumor from normal tissue (AUC = 0.957). The area under 1-, 3-, and 5-year survival ROCs were above 0.6. The HSPA4 genetic alteration rate was 1.3%. Among the 14 DNA methylation CpG sites, seven were related to the prognosis of HCC. HSPA4 was positively related to immune cell infiltration and immune checkpoints (PD-1 and CTLA-4) in HCC. The KEGG pathway enrichment analysis revealed HSPA4 enrichment in antigen processing and presentation together with HSPA8 and HSP90AA1. We verified the value of HSPA4 in the diagnosis and predicting prognosis of HCC. HSPA4 may not only participate in the occurrence and progression but also the immune regulation of HCC. Therefore, HSPA4 can be a potential diagnostic and prognostic biomarker and a therapeutic target for HCC.

Association Between Neutrophil to Lymphocyte Ratio and Outcomes for Metastatic Pediatric Sarcoma Patients Treated With Stereotactic Body Radiotherapy

<h3>Purpose/Objective(s)</h3> The relationship between immune parameters and oncologic outcomes after stereotactic body radiotherapy (SBRT) is an area of active investigation. The neutrophil to lymphocyte ratio (NTLR) has been observed to be a prognostic marker in cancer survival across many cancers. We hypothesized that the NLTR is prognostic for patients undergoing SBRT for metastatic sarcoma. <h3>Materials/Methods</h3> From our IRB-approved single-institution registry, all patients treated with SBRT for metastatic sarcoma between 2014 and 2020 were identified. We evaluated treatment variables, complete blood count (CBC) data to calculate NLTR values, local control for each lesion, and survival. Wilcox Signed Rank Test and Friedman Test were used to compare intragroup NTLR changes between lesions experiencing local failure vs. local control. Overall survival was estimated using the Kaplan-Meier (KM) method, and univariate (UVA) and multivariable (MVA) Cox models were calculated. A receiver operating characteristic (ROC) curve was generated to determine a cut point for NTLR values to stratify low and high-risk groups. KM Curves were drawn to compare the two groups, and log rank statistics were calculated. <h3>Results</h3> Forty-two patients with 138 total lesions met inclusion criteria. Median follow up time of all patients was 9.7 months (range, 0.5-34.5). The median SBRT dose was 30 Gy (range, 15-60) given in 4 fractions (range, 1-5). Patients were heavily pre-treated with systemic therapy. Local control for all lesions was 66.7% (95% CI, 56.6-78.5%) at 1 year, and 50.2% (95% CI, 38.2-66.1) at 2-years. Median NTLR for controlled lesions before and after SBRT were 5.05 (IQR: 2.70 - 13.4) and 4.79 (IQR: 2.32 - 9.44), respectively. Median NTLR for failed lesions before and after SBRT were 3.31 (IQR: 2.24-4.98) and 3.51 (IQR: 3-6.13), respectively, and at local failure were 5.62 (IQR: 3.83 - 5.89). The change in median NTLR within the controlled group was not significant (<i>P</i> = .30) but was significant within the failed group (<i>P</i> = .027). Higher NLTR before SBRT was associated with poorer survival in both the univariate (HR = 1.11, 95% CI: 1.04-1.19) and multivariable (HR: 1.12, 95% CI: 1.03-1.21). The optimal NTLR cut point by survival ROC analysis was 5 (Youden index: .418). 1 year survival in the entire cohort was 47.6% (34.3%-66.1%). Patients with an NTLR above 5 at time of treatment had a 1-year survival of 37.7% (21.44%-66.3%), while patients with an NTLR below 5 had a significantly improved 1 year survival of 63% (43.3%-91.6%, <i>P</i> = .014). <h3>Conclusion</h3> Neutrophil to lymphocyte ratio at the time of treatment was prognostic for survival in patients undergoing stereotactic body radiotherapy for metastatic pediatric sarcoma. Change in the NTLR over time was a significant predictor of local failure. Further investigation into the interactions between immune parameters and sarcoma SBRT is warranted.

Comprehensive Analysis of HDAC Family Identifies HDAC1 as a Prognostic and Immune Infiltration Indicator and HDAC1-Related Signature for Prognosis in Glioma.

Background: The histone deacetylase (HDAC) family limited accessibility to chromatin containing tumor suppressor genes by removing acetyl groups, which was deemed a path for tumorigenesis. Considering glioma remained one of the most common brain cancers with a dichotomy prognosis and limited therapy responses, HDAC inhibitors were an area of intensive research. However, the expression profiles and prognostic value of the HDACs required more elucidation.Methods: Multiple biomedical databases were incorporated, including ONCOMINE, GEPIA, TCGA, CGGA, GEO, TIMER, cBioPortal, and Metascape, to study expression profiles, prognostic value, immune infiltration, mutation status, and enrichment of HDACs in glioma. STRING and GeneMANIA databases were used to identify HDAC1-related molecules. LASSO regression, Cox regression, Kaplan-Meier plot, and receiver operating characteristic (ROC) analyses were performed for HDAC1-related signature construction and validation.Results:HDAC1 was significantly overexpressed in glioma, while HDAC11 was downregulated in glioblastoma. Except for HDAC 6/9/10, the HDAC family expression was significantly associated with glioma grade. Most of the HDAC family also correlated with glioma genetic mutations. Higher HDAC1 expression level predicted more dismal overall survival (OS) (p < 0.0001) and disease-free survival (DFS) (p < 0.0001), but a higher level of HDAC11 held more favorable OS (p = 2.1e−14) and DFS (p = 4.8e−08). HDAC4 displayed the highest mutation ratio, at 2.6% of the family. The prognostic value of HDAC1 was validated with ROC achieving 0.70, 0.77, 0.75, and 0.80 as separability for 1-, 3-, 5-, and 10-years OS predictions in glioma, respectively. Moreover, HDAC1 expression positively correlated with neutrophil (r = 0.60, p = 2.88e-47) and CD4+ T cell infiltration (r = 0.52, p = 3.96e-35) in lower-grade glioma. The final HDAC1-related signature comprised of FKBP3, HDAC1 (Hazard Ratio:1.49, 95%Confidence Interval:1.20–1.86), PHF21A, RUNX1T1, and RBL1, and was verified by survival analysis (p < 0.0001) and ROC with 0.80, 0.84, 0.83, and 0.88 as separability for 1-, 3-, 5-, and 10-years OS predictions, respectively. The signature was enriched in chromatin binding.Conclusion: HDAC family was of clinical significance for glioma. Most of the HDAC family significantly correlated with the glioma grade, IDH1 mutation, and 1p/19q codeletion. HDAC1 was both a prognostic and immune infiltration indicator and a central component of the HDAC1-related signature for precise prognosis prediction in glioma.

Autophagy-Related Gene Pairs Signature for the Prognosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786—0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.

Prostate cancer in young men represents a distinct clinical phenotype: gene expression signature to predict early metastases.

Aim:Several genomic signatures are available to predict Prostate Cancer (CaP) outcomes based on gene expression in prostate tissue. However, no signature was tailored to predict aggressive CaP in younger men. We attempted to develop a gene signature to predict the development of metastatic CaP in young men.Methods:We measured genome-wide gene expression for 119 tumor and matched benign tissues from prostatectomies of men diagnosed at ≤ 50 years and > 70 years and identified age-related differentially expressed genes (DEGs) for tissue type and Gleason score. Age-related DEGs were selected using the improved Prediction Analysis of Microarray method (iPAM) to construct and validate a classifier to predict metastasis using gene expression data from 1,232 prostatectomies. Accuracy in predicting early metastasis was quantified by the area under the curve (AUC) of receiver operating characteristic (ROC), and abundance of immune cells in the tissue microenvironment was estimated using gene expression data.Results:Thirty-six age-related DEGs were selected for the iPAM classifier. The AUC of five-year survival ROC for the iPAM classifier was 0.87 (95%CI: 0.78–0.94) in young (≤ 55 years), 0.82 (95%CI: 0.76–0.88) in middle-aged (56–70 years), and 0.69 (95%CI: 0.55–0.69) in old (> 70 years) patients. Metastasis-associated immune responses in the tumor microenvironment were more pronounced in young and middle-aged patients than in old ones, potentially explaining the difference in accuracy of prediction among the groups.Conclusion:We developed a genomic classifier with high precision to predict early metastasis for younger CaP patients and identified age-related differences in immune response to metastasis development.

Clonal haematopoiesis in chronic ischaemic heart failure: prognostic role of clone size for DNMT3A- and TET2-driver gene mutations.

Somatic mutations of the epigenetic regulators DNMT3A and TET2 causing clonal expansion of haematopoietic cells (clonal haematopoiesis; CH) were shown to be associated with poor prognosis in chronic ischaemic heart failure (CHF). The aim of our analysis was to define a threshold of variant allele frequency (VAF) for the prognostic significance of CH in CHF. We analysed bone marrow and peripheral blood-derived cells from 419 patients with CHF by error-corrected amplicon sequencing. Cut-off VAFs were optimized by maximizing sensitivity plus specificity from a time-dependent receiver operating characteristic (ROC) curve analysis from censored data. 56.2% of patients were carriers of a DNMT3A- (N = 173) or a TET2- (N = 113) mutation with a VAF >0.5%, with 59 patients harbouring mutations in both genes. Survival ROC analyses revealed an optimized cut-off value of 0.73% for TET2- and 1.15% for DNMT3A-CH-driver mutations. Five-year-mortality was 18% in patients without any detected DNMT3A- or TET2 mutation (VAF < 0.5%), 29% with only one DNMT3A- or TET2-CH-driver mutations above the respective cut-off level and 42% in patients harbouring both DNMT3A- and TET2-CH-driver mutations above the respective cut-off levels. In carriers of a DNMT3A mutation with VAF ≥ 1.15%, 5-year mortality was 31%, compared with 18% mortality in those with VAF < 1.15% (P = 0.048). Likewise, in patients with TET2 mutations, 5-year mortality was 32% with VAF ≥ 0.73%, compared with 19% mortality with VAF < 0.73% (P = 0.029). The present study defines novel threshold levels for clone size caused by acquired somatic mutations in the CH-driver genes DNMT3A and TET2 that are associated with worse outcome in patients with CHF.