TBX21 as an early immune biomarker of sepsis and septic shock: development of a prognostic immune risk score model

Objective: To find the biomarkers that accurately predict the survival of patients with esophageal squamous cell carcinoma (ESCC). Methods: The immune related genes that were significantly related to the overall survival (OS) of patients with ESCC were screened from The Cancer Genome Atlas (TCGA) database to construct a prognostic risk score model. The prognoses of the high-risk and low-risk groups were compared by Kaplan-Meier method. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve. Tumor tissue samples of 83 patients with pathological diagnosis of ESCC were collected from Anyang Cancer Hospital for external verification. Cox regression analysis was used to comprehensively evaluate the effects of prognostic risk score and various clinical characteristics on OS of patients with ESCC. Results: Seven immune-related genes that were significantly related to survival prognosis were selected from the TCGA database and included in the prognostic risk score model, which were S100A12, SLC40A1, FABP9, TNFSF10, IGHA2, IL1F10, and STC2. The 1- and 2-year survival rates of the low-risk group (40 cases) were 94.3% and 82.5%, respectively, while those of the high-risk group (40 cases) were 75.9% and 32.9%, respectively.The prognosis of the high-risk group was worse than that of the low-risk group (P<0.001). The 83 external validation samples obtained consistent results by using the prognostic risk score model. The prognostic risk score was positively correlated with the content of CD4(+) T lymphocytes in ESCC (r(s)=0.259, P=0.020), but not correlated with the content of B lymphocytes, CD8(+) T lymphocytes, neutrophils, macrophages or dendritic cells (P>0.05). Conclusions: S100A12, SLC40A1, FABP9, TNFSF10, IGHA2, IL1F10, and STC2 were risk genes significantly associated with OS of patients with ESCC. The prognostic risk score was an independent prognostic factor for the OS of patients with ESCC, and it was correlated with the content of CD4(+) T lymphocytes in ESCC tissue.

Objective: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor. Tumor mutations and the immune microenvironment play important roles in LUAD development and progression. This study was aimed at elucidating the characteristics of patients with different tumor immune microenvironment and establishing a prediction model of prognoses and immunotherapy benefits for patients with LUAD.Materials and Methods: We conducted a bioinformatics analysis on data from The Cancer Genome Atlas and Gene Expression Omnibus (training and test sets, respectively). Patients in the training set were clustered into different immunophenotypes based on tumor-infiltrating immune cells (TIICs). The immunophenotypic differentially expressed genes (IDEGs) were used to develop a prognostic risk score (PRS) model. Then, the model was validated in the test set and applied to evaluate 42 surgery patients with early LUAD.Results: Patients in the training set were clustered into high (Immunity_H), medium (Immunity_M), and low (Immunity_L) immunophenotype groups. Immunity_H patients had the best survival and more TIICs than Immunity_L patients. Immunity_M patients had the worst survival, characterized by most CD8+ T and Treg cells and highest expression of PD-1 and PD-L1. The PRS model, which consisted of 14 IDEGs, showed good potential for predicting the prognoses of patients in both training and test sets. In the training set, the low-risk patients had more TIICs, higher immunophenoscores (IPSs) and lower mutation rates of driver genes. The high-risk patients had more mutations of DNA mismatch repair deficiency and APOBEC (apolipoprotein B mRNA editing enzyme catalytic polypeptide-like). The model was also a good indicator of the curative effect for immunotherapy-treated patients. Furthermore, the low-risk group out of 42 patients, which was evaluated by the PRS model, had more TIICs, higher IPSs and better progression-free survival. Additionally, IPSs and PRSs of these patients were correlated with EGFR mutations.Conclusion: The PRS model has good potential for predicting the prognoses and immunotherapy benefits of LUAD patients. It may facilitate the diagnosis, risk stratification, and treatment decision-making for LUAD patients.

Purpose: The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment.Methods: High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model.Results: A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs).Conclusion: The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

Objective N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.

ABSTRACTBackgroundGastric cancer (GC) stands out as one of the most prevalent forms of malignant tumors globally, characterized by a notably high mortality rate. In spite of progress in medical science and treatment alternatives, the survival rate over 5 years continues to stay under 40%. In the realm of cancer biology, ferroptosis and cuproptosis represent two distinctive forms of programmed cell death that are gaining attention for their roles in tumor progression and treatment response. Ferroptosis is characterized by its dependence on lipid peroxidation and the resultant accumulation of reactive oxygen species (ROS) which ultimately induce cellular demise. On the other hand, cuproptosis operates through a different pathway, characterized by the direct interaction of copper ions with the acylating elements of the tricarboxylic acid cycle which ultimately leads to cell death. Ferroptosis and cuproptosis, that are types of programmed cell death associated with metals, play a significant role in the onset and progression of GC.ObjectiveThis study intends to employ bioinformatics techniques to pinpoint genes that are differentially expressed genes (DEGs) linked to ferroptosis and cuproptosis that are significant for the prognosis of GC. Additionally, the study aims to develop a prognostic risk score model that will facilitate the prediction of patient outcomes based on these identified genetic factors.MethodsTranscriptome and clinical data from tissues of 412 patients with gastric adenocarcinoma and 36 adjacent non‐cancerous tissues were obtained from The Cancer Genome Atlas stomach adenocarcinoma collection (TCGA‐STAD) database. This analysis aimed to identify differentially DEGs associated with ferroptosis and cuproptosis that are linked to the prognosis of GC patients. Furthermore, gene expression data along with clinical details from 433 GC patients in the gene expression omnibus (GEO) database were combined to create a prognostic risk score model. Further investigations were carried out, encompassing pathway enrichment analysis, assessment of tumor mutation burden (TMB), evaluation of tumor immune dysfunction and exclusion (TIDE), single‐sample gene set enrichment analysis (ssGSEA), and analysis of the tumor microenvironment (TME). Validation was achieved through immunohistochemical examination of relevant gene expression in samples collected from GC patients.ResultsTwelve DEGs linked to the prognosis of GC were discovered, leading to the creation of a prognostic risk score model that incorporates four genes involved in ferroptosis (NOX4, GLS2, MYB, NNMT) alongside one gene related to cuproptosis (GCSH). Analysis of pathway enrichment revealed a notable accumulation of the DEGs within several signaling pathways associated with the extracellular matrix. In addition, examinations of immune cell infiltration demonstrated significant variations in TMB, the quantity of immune cells present within the tumor, their functional roles, and the TME when contrasting high‐risk and low‐risk groups. Findings from immunohistochemical studies showed that GCSH and GLS2 show varying levels of expression in gastric cancer tissues and have a correlation with patient prognosis.ConclusionA prognostic risk model specifically designed for gastric cancer was developed, comprising five distinct genes. This model demonstrates a strong ability to accurately forecast both the prognosis of gastric cancer patients and the effectiveness of immunotherapy treatments they may undergo.

BackgroundWe aim to discover some prognostic factors, provide a basis for discovering molecular markers, and provide a basis for molecular features of early lung adenocarcinoma (LUAD) to predict patient prognosis.MethodsSequence data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database to screen out differentially expressed lncRNAs, miRNAs, and mRNAs (DERs). DERs were identified using R software’s limma package. The competitive endogenous RNA (ceRNA) network was constructed based on these RNAs. Univariate and multivariate Cox regression analysis on the RNAs in the ceRNA screened out independent prognostic-related RNAs to construct a prognostic risk score (PS) model. Combined with clinical data, we can calculate the survival rate of patients with early LUAD.ResultsThere were 2,701 differentially expressed mRNAs (DEmRNAs), 47 differentially expressed lncRNAs (DElncRNAs), and 161 differentially expressed miRNAs (DEmiRNAs) identified in early LUAD. Based on these RNAs, 32 lncRNAs, 87 miRNAs, and 174 mRNAs participated in the ceRNA network. Twelve independently prognostic-related RNAs form an optimized combination. We developed a PS model based on these RNAs. Age, tumor recurrence and PS model status were independent survival prognostic clinical factors. Nomogram was established to predict the 3-year and 5-year survival rates.ConclusionsWe successfully constructed a ceRNA regulatory network based on the DERs in early LUAD. It can help us clarify the molecular mechanism of early LUAD. Simultaneously, the prognostic-related RNAs in early LUAD were also screened out. This network could provide new bases for diagnoses and prognoses of patients with LUAD.

Renal cell carcinoma (RCC) is the most common kidney cancer in adults. According to the histological features, it could be divided into several subtypes, of which the most common one is kidney renal clear cell carcinoma (KIRC), which contributed to more than 90% of cases for RCC and usually ends with a dismal outcome. Previous studies suggested that basement membrane genes (BMGs) play a pivotal role in tumor development. However, the significance and prognostic value of BMGs in KIRC still wrap in the mist. KIRC data were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A prognostic risk score (PRS) model based on BMGs was established using univariate and least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis was performed for prognostic prediction. The Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curves, nomogram, and calibration curves were utilized to evaluate and validate the PRS model. All KIRC cases were divided into the high-risk score (HRS) group and the low-risk score (LRS) group according to the median risk scores. In addition, single-sample gene set enrichment analysis (ssGSEA), immune analysis, tumor microenvironment (TME) analysis, principal component analysis (PCA), and half-maximal inhibitory concentration (IC50) were also applied. Expression levels of BMGs were confirmed by qRT-PCR in both human renal cancer cell lines and tissues. We established the BMGs-based prognostic model according to the following steps. Within the TCGA cohort, patients' prognosis of the HRS group was significantly worse than that of the LRS group, which was consistent with the analysis results of the GEO cohort. PCA patterns were significantly distinct for LRS and HRS groups and pathological features of the HRS group were more malignant compared with the LRS group. Correlation analysis of the PRS model and TME features, such as immune cell scores, stromal cell scores, and ESTIMATE values, revealed a higher immune infiltration in the HRS group compared with the LRS group. The chemotherapeutic response was also evaluated in KIRC treatment. It showed that the HRS group exhibited stronger chemoresistance to chemotherapeutics like FR-180204, GSK1904529A, KIN001-102, and YM201636. The therapeutic reactivity of the other 27 chemotherapeutic agents was summarized as well. Furthermore, the FREM2 level was measured in both human kidney tissues and associated cell lines, which suggested that lower FREM2 expression prompts a severer pathology and clinical ending. Our study showed that KIRC is associated with a unique BMG expression pattern. The risk scores related to the expression levels of 10 BMGs were assessed by survival status, TME, pathological features, and chemotherapeutic resistance. All results suggested that FREM2 could be a potential candidate for KIRC prognosis prediction. In this study, we established a valid model and presented new therapeutic targets for the KIRC prognosis prediction as well as the clinical treatment recommendation, and finally, facilitated precision tumor therapy for every single individual.

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

Laryngeal cancer is the second most prevalent head and neck tumor and it is one of the most common malignancies of the upper respiratory tract. Fatty acid metabolism affects cancer cell biology in several ways, and alterations in fatty acid metabolism are characteristic of both tumorigenesis and metastasis. Despite advances in laryngeal cancer diagnosis and treatment over the years, there has been no significant improvement in survival or mortality. Studying the role of fatty acid metabolism-related genes in laryngeal cancer will facilitate our search for valuable biomarkers to guide prognostic management and treatment selection. We constructed a prognostic risk score model for fatty acid metabolism-related genes by downloading and analyzing laryngeal cancers from the TCGA and GEO databases. We predicted survival outcomes of laryngeal cancer patients using a prognostic risk score model of fatty acid metabolism-related genes and analyzed the resistance of laryngeal cancer in different individuals to multiple drugs. In addition, the relationship between the prognostic risk score model and cellular infiltration characteristics of the tumor microenvironment were investigated. Through the prognostic risk scoring model, the genes with risk-prompting effect and related to prognosis were screened out for further research. Through the study of gene expression levels in the TCGA database, we screened out 120 differentially expressed fatty acid metabolism genes. LASSO-Cox and Cox regression analyses identified nine genes associated with prognosis to construct a prognostic risk score model for genes related to fatty acid metabolism. Both TCGA and GEO confirmed that samples in the high-risk score group had a worse prognosis than those in the low-risk score group. We found significant differences between the high-risk and low-risk groups for 22 drugs (P < 0.05). In addition, we found differences in immune cell infiltration between the different risk score groups. Finally, through the risk assessment model, combined with multiple databases, THBS1, a high-risk and prognosis-related gene, was screened. We also found that THBS1 could promote the migration, invasion and proliferation of laryngeal cancer cells by constructing THBS1 knockout cell lines. In our study, we identified key fatty acid-related genes differentially expressed in laryngeal carcinoma that can be used to adequately predict prognosis using a comprehensive bioinformatic experimental approach. It was also found that THBS1, a high-risk and prognosis-related gene, may regulate the occurrence and development of laryngeal cancer through fatty acid metabolism, which has further helped us to explore the role of fatty acid metabolism genes in laryngeal cancer.

Abnormal expression of RNA binding proteins (RBPs) has been reported across various cancers. However, the potential role of RBPs in colorectal cancer (CRC) remains unclear. In this study, we performed a systematic bioinformatics analysis of RBPs in CRC. We downloaded CRC data from The Cancer Genome Atlas (TCGA) database. Our analysis identified 242 differentially expressed RBPs between tumor and normal tissues, including 200 upregulated and 42 downregulated RBPs. Next, we found eight RBPs (RRS1, PABPC1L, TERT, SMAD6, UPF3B, RP9, NOL3, and PTRH1) related to the prognoses of CRC patients. Among these eight prognosis-related RBPs, four RBPs (NOL3, PTRH1, UPF3B, and SMAD6) were selected to construct a prognostic risk score model. Furthermore, our results indicated that the prognostic risk score model accurately predicted the prognosis of CRC patients [area under the receiver operating characteristic curve (AUC)for 3- and 5-year overall survival (OS) and was 0.645 and 0.672, respectively]. Furthermore, we developed a nomogram based on a prognostic risk score model. The nomogram was able to demonstrate the wonderful performance in predicting 3- and 5-year OS. Additionally, we validated the clinical value of four risk genes in the prognostic risk score model and identified that these risk genes were associated with tumorigenesis, lymph node metastasis, distant metastasis, clinical stage, and prognosis. Finally, we used the TIMER and Human Protein Atlas (HPA)database to validate the expression of four risk genes at the transcriptional and translational levels, respectively, and used a clinical cohort to validate the roles of NOL3 and UPF3B in predicting the prognosis of CRC patients. In summary, our study demonstrated that RBPs have an effect on CRC tumor progression and might be potential prognostic biomarkers for CRC patients.

Melanoma is a highly lethal cancer with a poor prognosis. T-cells and melanoma cells play crucial role in shaping the tumor microenvironment, yet their role and impact on prognosis in melanoma is still unclear. We analyzed single-cell RNA sequencing (scRNA-seq) data for melanoma (GSE200218 and GSE215121) from the Gene Expression Omnibus (GEO) and gene expression data from GSE65904 and TCGA-Skin Cutaneous Melanoma (SKCM). We correlated cells with survival outcomes to identify cell subpopulations linked to melanoma prognosis using Scissor. Based on 108 prognostic genes, melanoma patients were stratified into two subgroups. A novel prognostic risk score (PRS) model was constructed using differentially expressed genes (DEGs) from these subgroups. Our analysis revealed specific T-cell and melanoma subpopulations influencing melanoma prognosis, validated in an independent cohort. Notably, our study was the first to identify MITF + T-cell and M2-cell sub-populations associated with melanoma prognosis. Using 108 prognostic gene markers, we stratified TCGA-SKCM patients into two groups with distinct clinical outcomes, immune cell scores, and carcinogenic profiles. Additionally, we employed 72 machine-learning algorithm combinations to develop a consensus prognosis model based on 174 DEGs from the two prognosis-related subgroups. Ultimately, we created a novel PRS model using 11 genes, which demonstrated accurate prognostic predictive ability in the GSE65904 validation cohort. This study identified MITF + T-cells and M2-cells as key factors in melanoma prognosis and developed a novel PRS model for accurate prediction. These findings could help guide clinical decision-making for melanoma patients.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration. ALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients. In this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, and GTF2H5) with the results of bioinformatics analysis. This study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic.

Objective: To investigate long-term prognosis and impact factors in children with vasovagal syncope (VVS) receiving metoprolol therapy. Methods: This retrospective study included children with VVS who underwent metoprolol therapy at the Pediatric Syncope Unit of Peking University First Hospital between January 2012 and November 2023. Baseline demographic data, pre-treatment indices, including head-up tilt test (HUTT) and 24 h Holter monitoring, were collected. All participants received standardized metoprolol therapy for a minimum duration of one month. Follow-up was conducted between June and July 2025, with syncope recurrence as the primary endpoint. Multivariable Cox proportional hazards regression analysis was performed to identify independent impact factors of prognosis and to construct a Prognostic Risk Score (PRS) model. The model's performance was rigorously validated through receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and Bootstrap resampling (1000 iterations). Furthermore, children were stratified into high- and low-risk groups based on median PRS values. Kaplan-Meier survival analysis was then performed to assess the model's discriminative efficacy. Results: This study included 97 children diagnosed with VVS. The median duration of metoprolol therapy was 2.5 months (interquartile range [IQR]: 2.0-3.0 months), with a median follow-up period of 59 months (IQR: 25.5-72 months). During follow-up, syncope recurrence was observed in 37 patients, while 60 patients remained symptom-free. COX regression analysis showed that time-domain indices of heart rate variability (HRV), including the standard deviation of all NN intervals (SDNN) and the triangular index (TR), as well as the frequency-domain index of HRV very low frequency (VLF), were relative factors of the long-term prognosis in children with VVS treated with metoprolol. Based on the above three identified factors, the PRS model was calculated as: PRS = 0.03 × SDNN - 0.02 × VLF - 0.1 × TR. ROC showed that the area under the curve (AUC) for discriminative power related to long-term prognosis was 0.808 (p < 0.01). The cumulative recurrence rate of symptoms in the high-risk score group was significantly higher than that in the low-risk score group (p < 0.01). The DCA curve demonstrated the clinical applicability of the model. Bootstrap internal verification indicated high stability, with the bias-corrected and accelerated (Bca) confidence interval (CI) of the C index ranging from 0.71 to 0.89. Conclusions: After metoprolol treatment, 38.1% of children with VVS experienced syncope recurrence during a median follow-up period of 59 months. Baseline HRV index, SDNN, TR, and VLF were identified as factors associated with the long-term prognosis of children with VVS treated with metoprolol. The PRS model based on the above indices demonstrated good value in linking to the individual long-term prognosis.

ObjectiveTo investigate the differential expression of RBPs in cervical squamous cell carcinoma (CESC), analyze the regulatory effect of narcotic drugs on RBPs, and establish the prognostic risk model of CESC patients.MethodsRNA-SEQ data and clinical case data of cancer and normal samples from CESC patients were obtained from the Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database. Differentially expressed RBPs were screened by R language and enriched. The CMAP database is used to predict the anesthetic drugs that regulate the differential expression of RBPs. The prognostic risk score model was constructed by COX regression analysis. Risk score of each CESC patient was calculated and divided into high-risk group and low-risk group according to the median risk score. The prediction efficiency of prognostic risk model was evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve, and the correlation between prognostic risk model and clinical characteristics was analyzed. Immunohistochemistry was used to detect the expression of RNASEH2A and HENMT1 in tissues.ResultsThere were 65 differentially expressed RBPs in CESC. Five anesthetics, including benzocaine, procaine, pentoxyverine, and tetracaine were obtained to regulate RBPs. Survival analysis showed that seven genes were related to the prognosis of patients, and the CESC risk score model was constructed by COX regression. The risk score can be used as an independent prognostic factor. RNASEH2A and HENMT1 are up-regulated in tumors, which can effectively distinguish normal tissues from tumor tissues.ConclusionIt is found that different anesthetic drugs have different regulatory effects on the differential expression of RBPs. Based on the differentially expressed RBPs, the prognostic risk score model of CESC patients was constructed. To provide ideas for the formulation of individualized precise anesthesia scheme and cancer pain analgesia scheme, which is helpful to improve the perioperative survival rate of cancer patients.

Neuroblastoma (NB) is the most common solid malignancy in children. MYCN gene amplification is the most relevant genetic alteration in patients with NB and is associated with poor prognosis. Autophagy plays specific roles in the occurrence, development, and progression of NB. Here, we aimed to identify and assess the prognostic effects of autophagy-related genes (ARGs) in patients with NB and MYCN gene amplification. Differentially expressed ARGs were identified in patients with NB with and without MYCN gene amplification, and the ARG expression patterns and related clinical data from the Therapeutically Applicable Research to Generate Effective Treatments database were used as the training cohort. Least absolute shrinkage and selection operator analyses were used to identify prognostic ARGs associated with event-free survival (EFS), and a prognostic risk score model was developed. Model performance was assessed using the Kaplan–Meier method and receiver operating characteristic (ROC) curves. The prognostic ARG mode l was verified using the validation cohort dataset, GSE49710. Finally, a nomogram was constructed by combining the ARGbased risk score with clinicopathological factors. Three ARGs (GABARAPL1, NBR1, and PINK1) were selected to build a prognostic risk score model. The EFS in the low-risk group was significantly better than that in the high-risk group in both the training and validation cohorts. A nomogram incorporating the prognostic risk score, age, and International Neuroblastoma Staging System stage showed a favorable predictive ability for EFS rates according to the area under the ROC curve at 3 years (AUC = 0.787) and 5 years (AUC = 0.787). The nomogram demonstrated good discrimination and calibration. Our risk score model for the 3 ARGs can be used as an independent prognostic factor in patients with NB and MYCN gene amplification. The model can accurately predict the 3- and 5-year survival rates.