Construction Of Risk Model Research Articles

Model for predicting prognosis and immunotherapy based on CD+8T cells infiltration in neuroblastoma.

Neuroblastoma (NBL) is an extracranial malignant tumor in children deriving from the neural crest in the sympathetic nervous system. Although various immunotherapy interventions have made significant breakthroughs in many adult cancers, the efficacy of these immunotherapies was still limited in NBL. NBL has low immunogenicity which results in a lack of tumor-infiltrating T lymphocytes in the tumor microenvironment (TME). Moreover, tumor cells can wield many immune evasion strategies both in the TME and systemically to impede lymphocyte infiltration and activation. All these factors hamper the anti-tumor effects of CD8+ T cells during immunotherapy and the levels of infiltrating CD8+ T cells correlate with therapy response. In this study, we utilized multidimensional bioinformatic methods to establish a risk model based on CD8+ T cells -related genes (CD8+ TRGs). We obtained 33 CD8+TRGs with well-predictive ability for prognosis in both GSE49711 and E-MTAB-8248 cohorts. Then, 12 CD8+TRGs including HK2, RP2, HPSE, ELL2, GFI1, SLC22A16, FCGR3A, CTSS, SH2D1A, RBP5, ATF5, and ADAM9 were finally identified for risk model construction and validation. This model revealed a stable performance in prognostic prediction of the overall survival (OS) and event-free survival (EFS) in patients with NBL. Additionally, our research indicated that the immune and stromal scores, immune-related pathways, immune cell infiltration, the expression of major histocompatibility complex (MHC) and immune checkpoint molecules, immunotherapy response, and drug susceptibility revealed significant differences between high and low-risk groups. According to our analyses, the constructed CD8+TRGs-based risk model may be promising for the clinical prediction of anti-tumor therapy responses and prognoses in NBL.

Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma.

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

Mitochondrial metabolic reprogramming-mediated immunogenic cell death reveals immune and prognostic features of clear cell renal cell carcinoma.

Mitochondrial metabolic reprogramming (MMR)-mediated immunogenic cell death (ICD) is closely related to the tumor microenvironment (TME). Our purpose was to reveal the TME characteristics of clear cell renal cell carcinoma (ccRCC) by using them. Target genes were obtained by intersecting ccRCC differentially expressed genes (DEGs, tumor VS normal) with MMR and ICD-related genes. For the risk model, univariate COX regression and K-M survival analysis were used to identify genes most associated with overall survival (OS). Differences in the TME, function, tumor mutational load (TMB), and microsatellite instability (MSI) between high and low-risk groups were subsequently compared. Using risk scores and clinical variables, a nomogram was constructed. Predictive performance was evaluated by calibration plots and receiver operating characteristics (ROC). We screened 140 DEGs, including 12 prognostic genes for the construction of risk models. We found that the immune score, immune cell infiltration abundance, and TMB and MSI scores were higher in the high-risk group. Thus, high-risk populations would benefit more from immunotherapy. We also identified the three genes (CENPA, TIMP1, and MYCN) as potential therapeutic targets, of which MYCN is a novel biomarker. Additionally, the nomogram performed well in both TCGA (1-year AUC=0.862) and E-MTAB-1980 cohorts (1-year AUC=0.909). Our model and nomogram allow accurate prediction of patients' prognoses and immunotherapy responses.

Construction of inflammatory associated risk gene prognostic model of NSCLC and its correlation with chemotherapy sensitivity

Background Inflammation is an important pathogenic factor of most malignant tumors. It is essential to understand mechanism underlying inflammation and cancer development, so as to formulate and develop anti-cancer treatment strategies. However, inflammatory-related gene characterization as well as risk model construction in prognosis and response chemotherapy or immunotherapy in NSCLC are still remain unclear. Methods A total of 1014 lung cancer samples with RNA-seqencing results were download from The Cancer Genome Atlas (TCGA) database. The patient cohort was randomized as a training and test cohorts, and 200 inflammatory-related genes were selected based on previously published data. Consensus clustering and Enrichment and immune function analyses base on Differential expression genes (DEGs) were performed. Prognosis Prediction Model were Constructed and Chemotherapy and immunotherapy sensitivity base on this model were performed. At last, H1299 and HCC827 cells were used to tested the mitoxantrone and oxal iplatin sensitivity after KRT6A knockdown. Results We identified the inflammatory-related genes from NSCLC datasets to build one prognosis prediction signature based on cluster inflammatory-related genes to lay a certain foundation for distinguishing high-risk NSCLC cases with dismal prognostic outcome. The nomogram provides the AUC values for 1-, 3-, and 5-year overall survival were 0.831, 0.853, and 0.86 in validation cohort. Morover, different sensitivity of immunotherapy or chemotherapy also were classified base on the different risk groups in NSCLC patients, which provided potent clinical reference. At last, targeting KRT6A sensitive to mitoxantrone and oxaliplatin in H1299 and HCC827 cells. Conclusions Inflammatory-related gene risk-score is the potential chemotherapeutic and immunotherapeutic biomarker for NSCLC, and targeting KRT6A sensitive to mitoxantrone and oxaliplatin in NSCLC. Highlights Inflammatory-related genes can lay a certain foundation for distinguishing high-risk NSCLC cases with dismal prognostic outcome. Risk-score base on inflammatory-related genes is positive correlated with CD274, TGFBR1 and TGFB1 expression. Targeting KRT6A sensitive to mitoxantrone and oxaliplatin in H1299 and HCC827 cells.

A prognostic risk model, tumor immune environment modulation, and drug prediction of ferroptosis and amino acid metabolism-related genes in hepatocellular carcinoma.

The prognosis of hepatocellular carcinoma (HCC) is challenging due to its heterogeneity. Ferroptosis and amino acid metabolism have been shown to be closely related to HCC. We obtained HCC-related expression data from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. We then crossed differentially expressed genes (DEGs), amino acid metabolism genes, and ferroptosis-related genes (FRGs) to obtain amino acid metabolism-ferroptosis-related differentially expressed genes (AAM-FR DEGs). Moreover, we developed a prognostic model using Cox analysis, followed by a correlation analysis of risk scores with clinical characteristics. We also performed an immune microenvironment analysis and drug sensitivity analysis. Finally, the expression levels of model genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical assays. We found that the 18 AAM-FR DEGs were mainly enriched to the alpha-amino acid metabolic process and amino acid biosynthesis pathways. Cox analysis identified CBS, GPT2, SUV39H1, and TXNRD1 as prognostic biomarkers for the risk model construction. Our results showed that the risk scores differed between pathology stage, pathology T stage, and HBV, and the number of HCC patients in the two groups. In addition, the expression of PD-L1 and CTLA-4 was high in the high-risk group, and the half-maximal inhibitory concentration (IC50) of sorafenib also differed between the two groups. Finally, the experimental validation demonstrated that the expression of biomarkers was consistent with the study analysis. Therefore, in this study, we constructed and validated a prognostic model (CBS, GPT2, SUV39H1, and TXNRD1) related to ferroptosis and amino acid metabolism and examined their prognostic value for HCC.

High-throughput sequencing approach for the identification of lncRNA biomarkers in hepatocellular carcinoma and revealing the effect of ZFAS1/miR-150-5p on hepatocellular carcinoma progression.

To screen abnormal lncRNAs and diagnostic biomarkers in the progression of hepatocellular carcinoma through high-throughput sequencing and explore the underlying mechanisms of abnormal lncRNAs in the progression of hepatocellular carcinoma. The transcriptome sequencing was used to analyze the RNA expression profile and identify differentially expressed RNAs. Hub lncRNAs were screened by combining (WGCNA, ceRNA regulatory network, PPI, GO and KEGG analyses, Kaplan-Meier curve analysis, Cox analysis, risk model construction and qPCR). Thereafter, the correlation between the expression of hub lncRNAs and tumor clinicopathological parameters was analyzed, and the hub lncRNAs were analyzed by GSEA. Finally, the effects of hub RNAs on the proliferation, migration and invasion of HepG2 cells were investigated in vitro. Compared with the control group, a total of 610 lncRNAs, 2,593 mRNAs and 26 miRNAs were screened in patients with hepatocellular carcinoma. Through miRNA target prediction and WGCNA, a ceRNA was constructed, comprising 324 nodes and 621 edges. Enrichment analysis showed that mRNAs in ceRNA were involved mainly in cancer development progression. Then, the ZFAS1/miR-150-5p interaction pair was screened out by Kaplan Meier curve analysis, Cox analysis and qPCR analysis. Its expression was related to tumor stage, TNM stage and patient age. ROC curve analysis showed that it has a good predictive value for the risk of hepatocellular carcinoma. GSEA showed that ZFAS1 was also enriched in the regulation of immune response, cell differentiation and proliferation. Loss-of-function experiments revealed that ZFAS1 inhibition could remarkably suppress HepG2 cell proliferation, migration and invasion in vitro. Bioinformatic analysis and luciferase reporter assays revealed that ZFAS1 directly interacted with miR-150-5p. Rescue experiments showed that a miR-150-5p inhibitor reversed the cell proliferation, migration and invasion functions of ZFAS1 knockdown in vitro. ZFAS1 is associated with the malignant status and prognosis of patients with hepatocellular carcinoma, and the ZFAS1/miR-150-5p axis is involved in hepatocellular carcinoma progression.

The risk model construction of the genes regulated by H3K36me3 and H3K79me2 in breast cancer.

Abnormal histone modifications (HMs) can promote the occurrence of breast cancer. To elucidate the relationship between HMs and gene expression, we analyzed HM binding patterns and calculated their signal changes between breast tumor cells and normal cells. On this basis, the influences of HM signal changes on the expression changes of breast cancer-related genes were estimated by three different methods. The results showed that H3K79me2 and H3K36me3 may contribute more to gene expression changes. Subsequently, 2109 genes with differential H3K79me2 or H3K36me3 levels during cancerogenesis were identified by the Shannon entropy and submitted to perform functional enrichment analyses. Enrichment analyses displayed that these genes were involved in pathways in cancer, human papillomavirus infection, and viral carcinogenesis. Univariate Cox, LASSO, and multivariate Cox regression analyses were then adopted, and nine potential breast cancer-related driver genes were extracted from the genes with differential H3K79me2/H3K36me3 levels in the TCGA cohort. To facilitate the application, the expression levels of nine driver genes were transformed into a risk score model, and its robustness was tested via time-dependent receiver operating characteristic curves in the TCGA dataset and an independent GEO dataset. At last, the distribution levels of H3K79me2 and H3K36me3 in the nine driver genes were reanalyzed in the two cell lines and the regions with significant signal changes were located.

A six lipid metabolism related gene signature for predicting the prognosis of hepatocellular carcinoma

Globally, hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors. Studies have shown that alterations in the tumor immune microenvironment (TIME) play a significant role in the pathogenesis and progression of HCC, and notably, lipid metabolism has been shown to regulate TIME. Therefore, in predicting the prognosis and efficacy of immunotherapy in patients with HCC, lipid metabolism-related prognostic factors are highly relevant. mRNA expression data of HCC were obtained from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and gene expression omnibus (GEO) databases. and lipid metabolism-related genes were also obtained from the GSEA databases. Least absolute shrinkage and selection operator regression analysis, univariate and multivariate Cox proportional hazards analysis were used to explore lipid metabolism-related prognostic genes and further construct a prognostic signature in the training set, ICGC and GSE54236 were used to validate the accuracy of the signature. qRT-PCR was used to detect the mRNA levels of lipid metabolism-related prognostic genes in HCC tissues and their paired adjacent tissues. Nile red staining was used to demonstrate lipid content in HCC tissues. Immunofluores-cence and ELISA were used to detect immune cells and immune responses in HCC tissues and serum. Six lipid metabolism-related genes (ADH1C, APEX1, ME1, S100A10, ACACA and CYP2C9) were identified as independent prognostic factors, which were used for risk model construction for HCC patients. The areas under the 1-, 2-, and 3-year ROC curves for the TCGA cohort were 0.758, 0.701 and 0.671, respectively. Compared with paired paracancerous tissues, qRT-PCR revealed that APEX1, ME1, S100A10 and ACACA were up-regulated in HCC tissues, whereas ADH1C and CYP2C9 were down-regulated in HCC tissues. Nile red staining indicated that this study showed that both the HCC tissue and serum of patients in the high-risk group exhibited lipid accumulation. Our identified prognostic model comprising six lipid metabolism-related genes could provide survival prediction. Moreover, HCC drug therapy target selection and molecular marker research can be guided by our predictive model.

A cancer-associated fibroblast gene signature predicts prognosis and therapy response in patients with pancreatic cancer.

Pancreatic cancer is a lethal malignancy with a 5-year survival rate of about 10% in the United States, and it is becoming an increasingly prominent cause of cancer death. Among pancreatic cancer patients, pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all cases and has a very poor prognosis with an average survival of only 1 year in about 18% of all tumor stages. In the past years, there has been an increasing interest in cancer-associated fibroblasts (CAFs) and their roles in PDAC. Recent data reveals that CAFs in PDAC are heterogeneous and various CAF subtypes have been demonstrated to promote tumor development while others hinder cancer proliferation. Furthermore, CAFs and other stromal populations can be potentially used as novel prognostic markers in cancer. In the present study, in order to evaluate the prognostic value of CAFs in PDAC, CAF infiltration rate was evaluated in 4 PDAC datasets of TCGA, GEO, and ArrayExpress databases and differentially expressed genes (DEGs) between CAF-high and CAF-low patients were identified. Subsequently, a CAF-based gene expression signature was developed and studied for its association with overall survival (OS). Additionally, functional enrichment analysis, somatic alteration analysis, and prognostic risk model construction was conducted on the identified DEGs. Finally, oncoPredict algorithm was implemented to assess drug sensitivity prediction between high- and low-risk cohorts. Our results revealed that CAF risk-high patients have a worse survival rate and increased CAF infiltration is a poor prognostic indicator in pancreatic cancer. Functional enrichment analysis also revealed that "extracellular matrix organization" and "vasculature development" were the top enriched pathways among the identified DEGs. We also developed a panel of 12 genes, which in additional to its prognostic value, could predict higher chemotherapy resistance rate. This CAF-based panel can be potentially utilized alone or in conjunction with other clinical parameters to make early predictions and prognosticate responsiveness to treatment in PDAC patients. Indeed, it is necessary to conduct extensive prospective investigations to confirm the clinical utility of these findings.

A novel risk model construction and immune landscape analysis of gastric cancer based on cuproptosis-related long noncoding RNAs.

Recent studies have identified cuproptosis, a new mechanism of regulating cell death. Accumulating evidence suggests that copper homeostasis is associated with tumorigenesis and tumor progression, however, the clinical significance of cuproptosis in gastric cancer (GC) is unclear. In this study, we obtained 26 prognostic cuproptosis-related lncRNAs (CRLs) based on 19 cuproptosis-related genes (CRGs) via Pearson correlation analysis, differential expression analysis, and univariate Cox analysis. A risk model based on 10 CRLs was established with the least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox proportional hazards model to predict the prognosis and immune landscape of GC patients from The Cancer Genome Atlas (TCGA). The risk model has excellent accuracy and efficiency in predicting prognosis of GC patients (Area Under Curve (AUC) = 0.742, 0.803, 0.806 at 1,3,5 years, respectively, P < 0.05). In addition, we found that the risk score was negatively correlated with the infiltration of natural killer (NK) cells and helper T cells, while positively correlated with the infiltration of monocytes, macrophages, mast cells, and neutrophils. Moreover, we evaluated the difference in drug sensitivity of patients with different risk patterns. Furthermore, low-risk patients showed higher tumor mutation burden (TMB) and better immunotherapy response than high-risk patients. In the end, we confirmed the oncogenic role of AL121748.1 which exhibited the highest Hazard Ratio (HR) value among 10 CRLs in GC via cellular functional experiments. In conclusion, our risk model shows a significant role in tumor immunity and could be applied to predict the prognosis of GC patients.

CCR7-mediated T follicular helper cell differentiation is associated with the pathogenesis and immune microenvironment of spinal cord injury-induced immune deficiency syndrome.

Spinal cord injury-induced immune deficiency syndrome (SCI-IDS) is a disorder characterized by systemic immunosuppression secondary to SCI that dramatically increases the likelihood of infection and is difficult to treat. T follicular helper (Tfh) cells regulated by chemokine receptor CCR7 are associated with SCI-IDS after acute SCI. The present study explored the roles of CCR7 in SCI-IDS occurrence and immune microenvironment composition. Gene expression profile data of peripheral blood leukocytes from SCI and non-SCI subjects were collected from the Gene Expression Omnibus database. According to differential gene expression analysis, a protein-protein interaction (PPI) network, and risk model construction, the CCR7 expression level was prominently related to acute SCI and CCR7 expression was significantly downregulated after acute SCI. Next, we constructed a clinical prediction model and used it to identify patients with acute SCI. Using Gene Ontology (GO) analysis and gene set enrichment analysis (GSEA), we discovered that immune-related biological processes, such as T cell receptor signaling pathway, were suppressed, whereas chemokine-related signaling pathways were activated after acute SCI. Immune infiltration analysis performed using single sample GSEA and CIBERSORT suggested that Tfh cell function was significantly correlated with the CCR7 expression levels and was considerably reduced after acute SCI. Acute SCI was divided into two subtypes, and we integrated multiple classifiers to analyze and elucidate the immunomodulatory relationships in both subtypes jointly. The results suggested that CCR7 suppresses the immunodeficiency phenotype by activating the chemokine signaling pathway in Tfh cells. In conclusion, CCR7 exhibits potential as a diagnostic marker for acute SCI.

PLXDC1 Can Be a Biomarker for Poor Prognosis and Immune Evasion in Gastric Cancer

BackgroundResearch has revealed that Plexin domain containing 1 (PLXDC1) is correlated with the prognosis of a variety of tumors, but its role in the tumor microenvironment (TME) of gastric cancer has not been reported.MethodsIn this study, we analyzed PLXDC1 expression in gastric cancer using the Oncomine and the Cancer Genome Atlas (TCGA) databases and immunohistochemical staining experiments, and performed prognostic assessment with data from the TCGA and Kaplan–Meier Plotter databases. The immunomodulatory role of PLXDC1 in the gastric cancer TME was analyzed by signaling pathway enrichment, immune cell correlation analysis, immunomodulator risk model construction and immunohistochemical staining experiments of immune cells.ResultsThe results indicated that PLXDC1 was overexpressed in gastric cancer and that its overexpression was associated with poor prognosis. Multivariate Cox analysis revealed that PLXDC1 could be an independent biomarker of the risk of gastric cancer. Signaling pathway enrichment revealed that high PLXDC1 expression was involved in signaling pathways related to immune activation and stromal activation, and Tumor Immune Dysfunction and Exclusion (TIDE) assessment indicated that high PLXDC1 expression was associated with a significantly higher risk of immune evasion than low PLXDC1 expression. A Cox risk model based on PLXDC1-associated immunomodulators also presented poor prognosis, and immune evasion was significantly higher in the high-risk group than in the low-risk group. In addition, immunohistochemical staining of CD8/CD3/CD4+ T cells in the high and low PLXDC1 expression groups also observed immune cell distribution characteristics of immune evasion.ConclusionThis study analyzed PLXDC1 from multiple biological perspectives and revealed that PLXDC1 can be a biomarker for poor prognosis and immune evasion in gastric cancer.

The emerging roles and mechanism of m6a in breast cancer progression.

Breast cancer (BC) has continued to be the leading cause of cancer deaths in women, accompanied by highly molecular heterogeneity. N6-methyladenosine (m6A), a methylation that happens on adenosine N6, is the most abundant internal mRNA modification type in eukaryotic cells. Functionally, m6A methylation is a reversible modification process and is regulated by 3 enzymes with different functions, namely “writer”, “reader”, and “eraser”. Abnormal m6A modifications trigger the expression, activation, or inhibition of key signaling molecules in critical signaling pathways and the regulatory factors acting on them in BC. These m6A-related enzymes can not only be used as markers for accurate diagnosis, prediction of prognosis, and risk model construction, but also as effective targets for BC treatment. Here, we have emphasized the roles of different types of m6A-related enzymes reported in BC proliferation, invasion, and metastasis, as well as immune regulation. The comprehensive and in-depth exploration of the molecular mechanisms related to m6A will benefit in finding effective potential targets and effective stratified management of BC.

Identification of the three subtypes and the prognostic characteristics of stomach adenocarcinoma: analysis of the hypoxia-related long non-coding RNAs.

Stomach adenocarcinoma (STAD) is one of the most commonly diagnosed cancers. This study analyzed the subtypes and characteristics of STAD subtypes by analyzing hypoxia pathway-related lncRNAs. Potential hub lncRNAs were found and a prognostic model was constructed. Expression profiling data and clinical information of STAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Metabolic pathway scores were calculated using single-sample gene set enrichment analysis (ssGSEA) method. Tumor immune microenvironment scores of the samples were assessed by ESTIMATE, MCP-counter, and ssGSEA. Functional analysis of lncRNAs, construction of risk models, and drug sensitivity analysis were performed. Pathway analysis revealed that the hypoxia pathway was a prognostic risk factor. Molecular subtypes were developed based on the hypoxia score-related lncRNAs. Three molecular subtypes (C1, C2, and C3) for gastric STAD were determined. The worst prognosis was in the C2, which was also characterized by the maximum hypoxia pathway-related scores and the maximum immune score. A majority of the immune checkpoints and chemokines were high-expressed in the C2 subtype. Mutations in the C2 subtype were significantly lower than the C1 and C3 subtypes. The subtypes differed in terms of functional and metabolic pathways. Eight hub indicator lncRNAs (MSC-AS1, AC037198.1, LINC00968, AL139393.3, LINC02544, BOLA3-AS1, MIR1915HG, and AC107021.2) capable of predicting patient prognosis were identified. Three hypoxia lncRNA-related molecular subtypes characterized by different prognostic and immune conditions were identified. The results maybe can provide a theoretical basis to improve the clinical diagnosis and treatment of STAD.

Development of a Risk Predictive Model for Evaluating Immune Infiltration Status in Invasive Thyroid Carcinoma.

Aims This study aimed to reveal the molecular characteristics and potential biomarker of immune-activated and immunosuppressive invasive thyroid carcinoma. Methods Expression and clinical data for invasive thyroid carcinoma were obtained from the TCGA database. Tumor samples were divided into immune-activated or immunosuppressive groups based on the immune enrichment score calculated by ssGSEA. Differentially expressed genes (DEGs) between tumor vs. normal groups or between immune-activated vs. immunosuppressive groups were screened, followed by functional enrichment. Immune infiltration was evaluated using the ESTIMATE, CIBERSORTx, and EPIC algorithms, respectively. A random forest algorithm and Lasso cox analysis were used to identify gene signatures for risk model construction. Results Totally 1171 DEGs were screened between tumor vs. normal groups, and multiple tumorigenesis-associated pathways were significantly activated in invasive thyroid carcinoma. Compared to immune-activated samples, immunosuppressive samples showed higher tumor purity, lower immune/stromal scores, and lower expression of immune markers, as well as lower infiltration abundance of CD4+ T cells and CD8+ T cells. A risk model based on a 12-immune signature (CCR7, CD1B, CD86, CSF2RB, HCK, HLA-DQA1, LTA, LTB, LYZ, NOD2, TNFRSF9, and TNFSF11) was developed to evaluate the immune infiltration status (AUC = 0.998; AUC of 0.958 and 0.979 in the two external validation datasets), which showed a higher clinical benefit and high accuracy. Immune-activated samples presented lower IC50 value for bortezomib, MG.132, staurosporine, and AZD8055, indicating sensitivity to these drugs. Conclusion A 12-gene-based immune signature was developed to predict the immune infiltration status for invasive thyroid carcinoma patients and then to identify the subsets of invasive thyroid carcinoma patients who might benefit from immunotherapy.

Risk Factors and Risk Model Construction of Stroke in Patients with Vertigo in Emergency Department.

Objective We aimed to explore the risk factors of stroke in patients with vertigo in the emergency department and establish a risk prediction model for stroke patients. Methods A total of 301 patients experiencing vertigo in our hospital from January 2020 to January 2021 were retrospectively included. Patients were divided into the stroke group (n = 56) and the nonstroke group (n = 245). The clinical characteristics of patients in both groups were collected and compared, followed by binary logistic regression that was employed to determine the risk factors that affect stroke diagnosis. The receiver operating characteristic (ROC) curve was used to clarify the effectiveness of the constructed model. Results Patients in the stroke group were older and had higher systolic and diastolic blood pressure on admission than the nonstroke group. Meanwhile, they demonstrated a higher proportion of diabetes and atrial fibrillation and focal muscle weakness, dysarthria, dysphagia, or ataxia in neurological examinations compared to the nonstroke group (all P < 0.05). The proportion of patients in the nonstroke group who had a history of vertigo or inner ear disease was significantly higher than that in the stroke group (P < 0.05). The patient's age ≥ 60 years old (OR = 3.57), diabetes (OR = 4.57), atrial fibrillation (OR = 4.26), previous history of vertigo or inner ear disease (OR = 0.16), focal muscle weakness (OR = 4.34), and dysphagia or ataxia (OR = 4.08) were associated with a higher risk of stroke. The area under the curve for stroke was 0.87, and the sensitivity and specificity were 98.2% and 57.6%, respectively, as the sum of the assigned scores was greater than 3. Conclusions Age ≥ 60 years old, diabetes, atrial fibrillation, previous history of vertigo or inner ear disease, focal muscle weakness, dysphagia, or ataxia were associated with a higher risk of stroke. The risk model constructed based on our findings may help to assess the risk of stroke in patients with vertigo in the emergency department.

The Prognostic Role of m6A-Related Genes in Paediatric Neuroblastoma Patients.

Methods The gene expression data were extracted from the “Therapeutically Applicable Research to Generate Effective Treatments” (TARGET) database. The differentially expressed genes (DEGs) were identified, and the relationships between DEGs and m6A genes were explored. Then, the correlations among the m6A genes in neuroblastoma were investigated. Finally, the prognostic role of the m6A genes was studied, and the risk model was constructed. Results 81 NB patients were extracted from the TARGET database. After comparing the gene expression between unfavorable and favorable outcome groups, 73 DEGs were identified, including 54 downregulated genes and 19 upregulated genes. In NB patients, we found that IGF2BP3, METTL14, and METTL16 are prognostic factors for disease-free survival (DFS) while IGF2BP3, METTL14, and METTL16 are prognostic factors for overall survival (OS). Besides, after the risk model construction, the OS between the two risk groups was drawn (log-rank p = 1.64e − 08, HR = 3.438, 95% CI 2.24-5.278). The 1-, 3-, and 5-year time-dependent receiving operating characteristic (ROC) curves were also illustrated, and the areas under the receiver operating characteristic curves (AUCs) attained 0.75, 0.798, and 0.768, respectively. Conclusions IGF2BP3, METTL14, and METTL16 were identified as the significant factors for DFS and OS in NB patients.

ETF Risk Models

We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of https://ssrn.com/abstract=2600798 (for binary classifications) or general risk model construction of https://ssrn.com/abstract=2722093 (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings.

A risk model based on autophagy-related lncRNAs for predicting prognosis and efficacy of immunotherapy and chemotherapy in gastric cancer patients.

Long non-coding RNAs (lncRNAs) are a class of non-protein-coding RNAs essential to the occurrence and development of gastric cancer (GC). We aimed to identify critical lncRNA pairs to construct a prognostic model and assess its performances in prognosis and efficacy prediction in GC patients receiving immunotherapy and chemotherapy. We searched transcriptome and clinical data of GC patients from The Cancer Genome Atlas (TCGA) database. Autophagy-related lncRNAs were identified using co-expression network analysis, and lncRNA pairs with prognostic value were selected using pairwise transcriptome analysis. The gene pairs were subjected to LASSO algorithm for identification of optimal gene pairs for risk model construction. Patients were classified into the low-risk and high-risk groups with the RiskScore as a cutoff. Finally, 9 optimal gene pairs were identified in the LASSO algorithm model for construction of a lncRNA prognostic risk model. For predictive performances, it successfully predicted a shorter survival of high-risk patients than that obtained in low-risk individuals (P < 0.001). It showed moderate AUC (area under the curve) values for 1-, 2-, and 3-year overall survival prediction of 0.713 and could serve as an independent predictor for GC prognosis. Compared to the low-risk group, high-risk patients had higher expressions of marker genes for immune checkpoint inhibitors (ICIs) and showed higher sensitivity to the chemotherapy agents, rapamycin, bexarotene, and bicalutamide. Our findings demonstrate a robust prognostic model based on nine autophagy-related lncRNA pairs for GC. It acts as an independent predictor for survival and efficacy prediction of immunotherapy and chemotherapy in GC patients.

How Does Online e-cigarette Advertisement Promote Youth’s e-cigarettes Use? The Mediating Roles of Social Norm and Risk Perceptions

ABSTRACT The adoption of the internet, social media, and e-cigarettes are on the rise among U.S. youth. Uses of social media and online platforms increase the probability for youth to encounter e-cigarette advertisements. Departing from this line of reasoning, we examine the underlying mechanisms of how online e-cigarettes exposure promotes youth’s e-cigarette use. Drawing on insights from the social construction of risk model, this study looks at how perceived social norms and risk perception mediate the link between online e-cigarette advertisement exposure and e-cigarette use. Youth aged 12–17 from the Public Assessment of Tobacco and Health (PATH) Study Waves 2–4 (2014–2018) were included (N = 6,067). Controlling for demographic and other known risk factors for e-cigarette use, respondents who had been exposed to online e-cigarette ads at Wave 2 perceived higher levels of positive social norms of e-cigarette use at Wave 3 (β = 0.13, p < .001) which was associated with lower e-cigarette risk perception at Wave 3 (β = −0.22, p < .001). Lower e-cigarette risk perception at Wave 3 resulted in a higher likelihood of e-cigarette use at Wave 4 (AOR = 0.51, p < .001). Online e-cigarettes ads exposure at Wave 2 predicted e-cigarette use at Wave 4 (AOR = 1.87, p < .001). The results indicate that norm perception associated with online e-cigarettes ads may twist youth’s e-cigarette risk perception that is associated with subsequent usage. Interventions to curb youth’s e-cigarette use can target social norms of e-cigarette use and restrict e-cigarette advertisement exposure to youth.