Time-dependent Receiver Operating Characteristic Curve Research Articles

A Predictive Scoring System for Therapy Failure in Persons with Chronic Myeloid Leukemia Receiving Initial a Second-Generation Tyrosine Kinase Inhibitor Therapy

Purposes Develop and validate a predictive scoring system for therapy failure in persons with chronic phase chronic myeloid leukemia (CML) receiving initial a second-generation tyrosine kinase inhibitor (2G-TKI). Methods Data from 329 consecutive subjects with CML receiving initial 2G-TKI therapy at one centre (Beijing, China) in a training dataset were interrogated to develop a predictive model which was then validated in 1,083 subjects from 65 other centers in China. Therapy-failure was defined using the 2020 ELN criteria including progression to accelerated or blast phases or CML-related death. Fine-Gray model was used for uni- and multi-variable analyses (UVA and MVA) to identify co-variates significantly-associated with therapy-failure. Akaike information criterion (AIC) was used to select prognostic co-variate and determine the best model. 1000 bootstrap re-samplings, Fine-Gray test, the minimal p-value approach, Bonferroni correction and kernel density estimator were used to determine optimal cut-offs to classify subjects into risk cohorts. Time-dependent receiver-operator characteristic curves (ROC) were used to estimate prediction accuracy and calibration plots used to determine concordance between predicted and observed cumulative incidences. Decision curve analysis (DCA) was used to calculate the net benefit using the model. Results In the training dataset, 279 subjects (85%) initially received nilotinib; and 50 (15%), dasatinib. Median 2G-TKI therapy-duration was 36 months (interquartile range [IQR], 24-68 months). 75 subjects (23%) had therapy-failure at a median of 9 months (IQR 3-12 months) after therapy start. In MVA, increasing splenomegaly and a higher percentage blood blasts were significantly-associated with the higher cumulative incidences of therapy-failure. We used these data to develop a predictive scoring system: "2G-TKI failure risk score= 0.6405×[(spleen size below the costal margin in cm + 0.2)/10] + 0.1348×(blood blasts + 0.2)" which divided subjects into low- (score ≤ 0.3021; n = 89; 31%), intermediate- (0.3021 < score < 1.4508; n = 155; 54%) and high-risk (score ≥ 1.4508; n = 43; 15%) cohorts with 5-year cumulative incidences of therapy-failure of 3% (95% Confidence Interval [CI], 0, 5%), 26% (20, 32%) and 72% (56, 88%; p-value for trend < 0.001; Figure 1A). Hazard ratios (HRs) for therapy-failure (low-risk cohort as reference) were 4.8 (1.7, 13.6; p = 0.002) and 16.5 (5.7, 47.6; p < 0.001; p for trend < 0.001) for the intermediate- and high-risk cohorts. In the external validation dataset, subjects were classified into the low- (n = 433; 40%), intermediate- (n = 583; 54%) and high-risk (n = 67; 6%) cohorts using our scoring system. 5-year cumulative incidences of therapy failure were 8% (3, 13%), 18% (12, 25%) and 44% (29, 60%; p for trend < 0.001; Figure 1B). HRs (low-risk cohort as reference) were 2.7 (1.9, 3.6; p < 0.001) and 10.6 (5.8, 15.4; p < 0.001; p for trend < 0.001) for the intermediate- and high-risk cohorts. In the training dataset cohort time-dependent AUROC curves for therapy-failure at 1-, 3-, and 5-years in the model were 0.73-0.82 and 0.68-0.75 in the validation dataset. Calibration plots for the 1-, 3- and 5-years cumulative incidences of therapy-failure indicated good concordance between predicted and observed outcomes. DCA curves indicated net benefit from using the predictive scoring system in clinical settings. Conclusions We developed and externally validated a therapy-failure scoring system in persons with chronic phase CML receiving initial 2G-TKI. The model had high AUROCs and performed well in calibration plots and DCAs. Our model may help physicians decide appropriateness of initial the 2G-TKI therapy including possible selection of appropriate haematopoietic cell transplant candidates. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Patterns of care and prognostic evaluation for stage I–III upper esophageal squamous cell carcinoma: a population-based study

There is no strong evidence regarding the optimal treatment and specific prognosis prediction model for upper esophageal squamous cell carcinoma (UESCC). This study aimed to investigate the real-world treatment patterns and develop models to predict overall survival (OS) and esophageal cancer-specific survival (ECSS) in patients with stage I-III UESCC. Patients with T1-4N0-3M0 UESCC in the Surveillance, Epidemiology, and End Results (SEER) database were identified from 2010 to 2017, and randomized to a training cohort and a validation cohort. The effect of treatment patterns on survival were comprehensively analyzed. Nomograms were developed by incorporating independent prognostic factors analyzed by Cox regression in the training cohort and evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) in two cohorts. A total of 677 patients were identified, including 452 in the training cohort and 225 in the validation cohort. Among all populations, 71.9% (487) received chemoradiotherapy without surgery, and chemoradiotherapy or/and surgery showed better survival than other treatments. However, surgery was rarely carried out for patients with stage II-III. T stage, N stage, surgery, chemotherapy, and radiotherapy were independent risks for both OS and ECSS, while age was also an independent risk for OS. The C-indexes for nomograms to predict OS (0.71 and 0.72) and ECSS (0.70 and 0.73) were greater than 7th AJCC staging system to predict OS (0.61 and 0.64) and ECSS (0.64 and 0.64) in both the training cohort and the validation cohort. Time-dependent ROC curves and DCA also suggested that nomograms performed consistently better than 7th AJCC staging system. The calibration curves demonstrated good consistency in predicting survival. Chemoradiotherapy was a major treatment with preferable survival for patients with stage I-III UESCC. We have firstly developed and validated prognostic nomograms in patients with stage I-III UESCC, which would play a supplementary role in the current staging system.

A nomogram for predicting overall survival in patients with parathyroid cancer: A novel web-based calculator

BackgroundParathyroid carcinoma is a rare endocrine malignancy. Considering that clinicians develop appropriate treatment strategies based on patients' survival expectations. Therefore, the present study aimed to develop a survival prediction model to guide clinical decision-making. MethodsWe retrospectively analyzed 362 parathyroid carcinoma patients diaagnosed in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Correlations between outcome events and variables were analyzed using univariate and multifactorial Cox regression, and variables screened by the multifactorial Cox risk proportional model were used to construct a survival prediction model. The model was evaluated using Receiver Operating Characteristic (ROC) curves, decision curve analysis (DCA), and C-index and calibration curves. ResultsUnivariate and multifactorial COX analyses revealed five independent prognostic factors for parathyroid carcinoma patients, which were subsequently used to develop the nomogram prediction model. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.747 (0.686–0.808), the area under the receiver operator characteristics curve(AUC)values of the nomogram in prediction of the 3, 5, and 10-year OS were 0.718 (0617-0.819), 0.711 (0.614–0.808) and 0.706 (0.610–0.803), respectively. In the validation cohort, the C-index was 0.740 (0.645–0.835), The AUC for 3, 5, and 10-years OS were 0.736 (0.584–0888), 0.698 (0.551–0.845) and 0.767 (0.647–0.887), respectively. The C-index, time-dependent ROC curve, calibration curve, and DCA showed that the Nomogram had a clear advantage. ConclusionThe developed nomogram can be applied in clinical practice to help clinicians to assess patient prognosis.

Development of a 4-miRNA prognostic signature for endometrial cancer.

To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan–Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.

A pyroptosis-related lncRNA signature in bladder cancer.

Pyroptosis, a type of programmed cell death, is implicated in the tumorigenesis, development and migration of cancer, which can be regulated by long non-coding RNAs (lncRNAs). Our research aimed to investigate the prognostic role of pyroptosis-related lncRNAs and the relationship to the tumor immune microenvironment through bioinformatics analysis. The clinical and RNA-sequencing data of bladder cancer patients were downloaded from The Cancer Genome Atlas (TCGA). And 412 bladder cancer subjects with clinical information were divided into training and testing cohort. And 52 reported pyroptosis-related genes were used to screen pyroptosis-related lncRNAs. A pyroptosis-related lncRNA signature was constructed based on Cox regression analyses. A 9-pyroptosis-related-lncRNA signature was identified to separate patients with bladder cancer into two groups. The prognosis of bladder cancer patients in the high-risk group was significantly inferior compared with those in the low-risk group. Risk scores were validated to develop an independent prognostic indicator based on multivariate Cox regression analysis. Receiver operating characteristic curve (ROC) analysis examined the signature on overall survival. The area under time-dependent ROC curve (AUC) at 1-, 3, and 5-years measured 0.747, 0.783, and 0.768, respectively. Analysis of the immune landscape and PD-L1 expression showed that PD-L1 is upregulated in the high-risk group. The immunocyte subtypes of the two groups were different. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets.

Lower bile acids as an independent risk factor for renal outcomes in patients with type 2 diabetes mellitus and biopsy-proven diabetic kidney disease.

AimsAbnormalities of glucolipid metabolism are critical mechanisms involved in the progression of diabetic kidney disease (DKD). Bile acids have an essential role in regulating glucolipid metabolism. This study investigated the clinicopathological characteristics of DKD patients with different bile acid levels and explored the relationship between bile acids and renal outcomes of DKD patients.MethodsWe retrospectively reviewed and evaluated the histopathological features and clinical features of our cohort of 184 patients with type 2 diabetes mellitus and biopsy-proven DKD. Patients were divided into the lower bile acids group (≤2.8 mmol/L) and higher bile acids group (>2.8 mmol/L) based on the cutoff value of bile acids obtained using the time-dependent receiver-operating characteristic curve. Renal outcomes were defined as end-stage renal disease (ESRD). The influence of bile acids on renal outcomes and correlations between bile acids and clinicopathological indicators were evaluated.ResultsBile acids were positively correlated with age (r = 0.152; P = 0.040) and serum albumin (r = 0.148; P = 0.045) and negatively correlated with total cholesterol (r = -0.151; P = 0.041) and glomerular class (r = -0.164; P =0.027). During follow-up, 64 of 184 patients (34.78%) experienced progression to ESRD. Lower levels of proteinuria, serum albumin, and bile acids were independently associated with an increased risk of ESRD (hazard ratio, R=5.319; 95% confidence interval, 1.208–23.425).ConclusionsBile acids are an independent risk factor for adverse renal outcomes of DKD patients. The serum level of bile acids should be maintained at more than 2.8 mmol/L in DKD patients. Bile acid analogs or their downstream signaling pathway agonists may offer a promising strategy for treating DKD.

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma

ObjectiveThe incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients.MethodsOverall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan–Meier (K–M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment.ResultsAccording to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723–0.797) in the development cohort and 0.796 (95% CI 0.737–0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K–M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively.ConclusionFor NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

Identification of ATP1B1, a key copy number driver gene in diffuse large B-cell lymphoma and potential target for drugs.

Copy number variations (CNVs) participate in the development and progression of cancer by altering the expression levels of genes. However, it is unclear whether this correlation exists in diffuse large B-cell lymphoma (DLBCL). Differentially expressed genes (DEGs) were identified from the GSE25638 and GSE56315 datasets. Modules that were highly related to DLBCL prognosis were obtained by Weighted Gene Co-expression Network Analysis (WGCNA). We performed an integrated analysis between CNV and differential gene expression in The Cancer Genome Atlas (TCGA) DLBCL. The DEGs were then overlapped with the module genes and expression-copy number variations-related (Exp-CNV-related) genes to obtain the common key genes. Time-dependent receiver operating characteristic (ROC) analysis was utilized to evaluate the accuracy of the key gene in predicting the prognosis of DLBCL. Next, we conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to explore the key gene. The potential molecule drugs of the key gene were identified by Connectivity Map (Cmap) analysis. A turquoise module with 160 genes was identified as the signature module. ATP1B1 is overexpressed in DLBCL cell lines, compared to Cluster of Differentiation 19+B (CD19+B) cells. The ROC curve indicated that ATP1B1 could be a biomarker for diagnosing DLBCL, and the forest map suggested that ATP1B1 gene expression levels had a greater impact on the prognosis of patients with DLBCL. The area under curve (AUC) value of the time-dependent ROC curve with values based on the 1-, 3-, and 5-year survivability were 0.576, 0.663, and 0.706, respectively. Pathway analysis demonstrated the relationship between ATP1B1 and focal adhesion, etc. The inhibitory effects of ATP1B1 downregulation on DLBCL cell proliferation, cell migration, invasion, and cell adhesion were also examined. We found out that the higher proliferation ability in ATP1B1-overexpression cells was rescued with roxithromycin. ATP1B1 is a copy number driver gene that could potentially be adopted as a diagnostic biomarker and therapeutic target of DLBCL.

Epithelioid Hemangioendothelioma: Incidence, Mortality, Prognostic Factors, and Survival Analysis Using the Surveillance, Epidemiology, and End Results Database.

Background Although epithelioid hemangioendothelioma (EHE) is a rare and aggressive vascular tumor, its demographic characteristics remain unclear. We used the surveillance, epidemiology, and end results (SEER) database to determine the clinical features, incidence, and prognostic factors associated with overall survival in patients with EHE. Methods The demographic and clinical data of patients with EHE were extracted from the SEER database (1975-2019) to calculate the incidence of EHE and survival rate in these patients. The Cox proportional hazards model and Kaplan-Meier method were used to analyze the prognostic factors of overall survival in these patients. A nomogram and time-dependent receiver operating characteristic (ROC) curve were employed to predict the 3- and 5-year survival rate. Results The overall incidence rate (IR) of EHE was 0.230 (95%confidence interval [CI] = 0.201–0.263) per 1,000,000 person-years. According to the age-stratified IR, the highest age-adjusted IR was observed in patients aged 60–79 years (0.524 per 1,000,000 person-years, 95%CI = 0.406–0.665). The majority (30.8%) of the tumors were located in the soft tissue and skin, followed by lesions in the abdomen (28%), respiratory system (19%), bone and joint (8.6%), and others. The 5-year overall survival rate was 55.6% (95%CI = 32.8–73.5%). Multiple Cox regression analysis revealed that age >80 years (hazard ratio [HR] = 8.57, 95%CI = 2.32–31.63, P < 0.001), African-American race (HR = 2.52, 95%CI = 1.31–4.85, P < 0.01), “American Indian/Alaska Native” or “Asian or Pacific Islander” (HR = 2.99, 95%CI = 1.5–5.96, P < 0.01) race, and respiratory tumors (HR = 2.55, 95%CI = 1.37–4.75, P < 0.01) were distinctly related to worse overall survival. The calibration plots demonstrated good consistency between nomogram-predicted and actual survival. The area under the time-dependent ROC curve was 0.721 (95%CI = 0.63–0.81) and 0.719 (95%CI = 0.63–0.81) for the 3- and 5-year survival, respectively. For the convenience of researchers and clinicians, we designed an online dynamics nomogram to predict the survival rate. Conclusion EHE is a relatively rare vascular tumor, which principally occurs in the soft tissue and skin. It most commonly occurs in patients aged 60–79 years and its incidence has increased in recent years. Age at diagnosis, race, and tumor location may affect the overall survival outcomes.

Predicting the Immune Microenvironment and Prognosis with a NETosis-Related lncRNA Signature in Head and Neck Squamous Cell Carcinoma.

Background The mechanistic aspects of the involvement of long noncoding RNAs (lncRNAs) in NETosis, the process of neutrophil extracellular trap (NET) formation in head and neck squamous cell carcinoma (HNSCC), lack comprehensive elucidation. The involvement of these molecules in the immune microenvironment and plausible HNSCC prognosis remain to see the light of the day. The plausible functioning of NETosis-related lncRNAs with their plausible prognostic impact in HNSCC was probed in this work. Methods The scrutiny of lncRNAs linked to NETosis entailed the probing of twenty-four genes associated with the process employing Pearson's correlation analysis on HNSCC patients' RNA sequencing data from The Cancer Genome Atlas (TCGA) database. The application of univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses yielded a NETosis-related lncRNA signature that was subjected to probing for its suitability in prognosis employing survival and nomogram analyses. Results The NETosis-related lncRNA signature inclusive of five lncRNAs facilitated patients to be segregated as high-risk and low-risk groups with the former documenting a poor prognosis. Regression unearthed that the risk score was an independent factor for prognosis. The receiver operating characteristic (ROC) or receiver operating characteristic curve analysis documented a one-year area under time-dependent ROC curve (AUC) value of 0.711 that is corroborative of the accuracy of this signature. Additional probing documented an evident enriching of immune-linked pathways in the low-risk patients, while the high-risk patients documented an immunologically “cold” profile as per the infiltration of immune cells. We verified lncRNA expression from our NETosis-related lncRNA signature in vitro, which reflects the reliability of our model to a certain extent. Moreover, we also verified the function of the lncRNA. We found that LINC00426 contributes to the innate immune cGAS-STING signaling pathway, which explain to some extent the role of our prognostic model in predicting “hot” and “cold” tumors. Conclusions The plausible prognostic relevance of the NETosis-related lncRNA signature (with five lncRNAs) emerges that is suggestive of its promise in targeting HNSCC.

Establishment of an endoplasmic reticulum stress-related signature predicting outcomes of gastric adenocarcinoma patients.

Background: Gastric adenocarcinoma (GAC) is a common clinical malignancy with a poor prognosis. Endoplasmic reticulum (ER) stress plays important roles in the progression, immune filtration, and chemoresistance of cancers. However, whether ER stress-related gene signatures can predict the prognosis of GAC patients remains unknown. Methods: GAC patient RNA-seq data downloaded from The Cancer Genome Atlas and gastric cancer patient microarray data from Gene Expression Omnibus datasets were analyzed using LASSO regression to construct an ER stress-related signature. Survival analysis, time-dependent receiver operating characteristic (ROC) curves, and Cox regression analysis were used to verify the efficacy of the signature. Immune infiltration, somatic mutation, immune checkpoint, and copy number variation analyses were utilized to explore the potential biological significance of the signature. Results: In the present study, eight ER stress-related gene signatures were constructed. Survival analysis showed that patients in the high-risk group had a significantly worse prognosis. The area under the time-dependent ROC curves was 0.65, 0.70, and 0.63 at 1, 3, and 5 years, respectively, in the training cohort. Cox regression analysis showed that the signature is an independent prognostic factor. To predict GAC patients’ prognosis meeting individual needs, a nomogram was constructed with good accuracy. In addition, gene set enrichment and immune infiltration analyses showed that the ER stress-related signature is associated with cancer-related pathway activation and an immunosuppressive tumor microenvironment in GAC. Conclusion: In the current study, we established an ER stress-related signature. This prognostic signature has good predictive power and could facilitate the development of novel strategies for the clinical treatment of GAC.

Risk stratification and prognosis prediction based on inflammation-related gene signature in lung squamous carcinoma.

Inflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation-related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients. The RNA-sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations-counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis. A two-IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low-risk group had more anti-tumor immune cells infiltrated while patient with high-risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568-0.651) in the training set and 0.652 (95% CI: 0.580-0.724) in validation set. Time-dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone. The IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival.

Six MicroRNA Prognostic Models for Overall Survival of Lung Adenocarcinoma.

Objective The purpose of this study is to screen for microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) and to explore its prognosis and effects on the tumor microenvironment in patients with LUAD. Methods Gene expression data, miRNA expression data, and clinical data for two different databases, TCGA-LUAD and CPTAC-3 LUAD, were downloaded from the GDC database. The miRNA prognosis of LUAD was filtered by the Cox proportional hazard model and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The performance of the model was validated by time-dependent receiver operating characteristics (ROC) curves. Possible biological processes associated with the miRNAs target gene were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the prognostic model was scored by risk, divided into high- and low-risk groups by median, and the differences in the immersion level of 21 immune cells in the high- and low-risk groups were assessed. To gain a deeper understanding of the underlying mechanism behind the model, the two most important miRNAs in the model, miR-195-3p and miR-5571-5p, were selected for HPA database validation and ceRNA network construction. Results Of the 209 variance expressions identified in the screening analysis, 145 were upregulated and 64 were downregulated by miRNAs. The prognostic models of six miRNA genes were obtained: miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293. These six genes were significantly associated with survival rates in LUAD patients. In particular, miR-1293, miR-195-3p, and miR-5571-5p are highly correlated with OS. The higher expression of miR-195-3p and miR-5571-5p, the better survival of LUAD OS is, and these two miRNA expressions contribute the most to the model. Finally, after sorting the risk scores calculated from low to high using the prognostic model, the patients with higher scores had shorter survival time and higher frequency of death, and there were significant differences in the immersion levels of 21 immune cells in the high- and low-risk groups. ceRNA network analysis found that TM9SF3 was regulated by miR-195-3p and was highly expressed in the tissues of LUAD patients, and the prognosis of the patients was poor. Conclusions miR-195-3p, miR-5571-5p, miR-584-3p, miR-494-3p, miR-4664-3p, and miR-1293 may be used as new biomarkers for prognosis prediction of LUAD. Our results also identified a lncRNA MEG3/miR-195-3p/RAB1A/TM9SF3 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

A Nomogram Based on Preoperative Lipiodol Deposition after Sequential Retreatment with Transarterial Chemoembolization to Predict Prognoses for Intermediate-Stage Hepatocellular Carcinoma.

(1) Background: Conventional transarterial chemoembolization (cTACE) is the mainstay treatment for patients with Barcelona Clinic Liver Cancer (BCLC) B-stage hepatocellular carcinoma (HCC). However, BCLC B-stage patients treated with cTACE represent a prognostically heterogeneous population. We aim to develop and validate a lipiodol-deposition-based nomogram for predicting the long-term survival of BCLC B-stage HCC patients after sequential cTACE. (2) Methods: In this retrospective study, 229 intermediate-stage HCC patients from two hospitals were separately allocated to a training cohort (n = 142) and a validation cohort (n = 87); these patients underwent repeated TACE (≥4 TACE sessions) between May 2010 and May 2017. Lipiodol deposition was assessed by semiautomatic volumetric measurement with multidetector computed tomography (MDCT) before cTACE and was characterized by two ordinal levels: ≤50% (low) and >50% (high). A clinical lipiodol deposition nomogram was constructed based on independent risk factors identified by univariate and multivariate Cox regression analyses, and the optimal cutoff points were obtained. Prediction models were assessed by time-dependent receiver-operating characteristic curves, calibration curves, and decision curve analysis. (3) Results: The median number of TACE sessions was five (range, 4–7) in both cohorts. Before the TACE-3 sessions, the newly constructed nomogram based on lipiodol deposition achieved desirable diagnostic performance in the training and validation cohorts with AUCs of 0.72 (95% CI, 0.69–0.74) and 0.71 (95% CI, 0.68–0.73), respectively, and demonstrated higher predictive ability compared with previously published prognostic models (all p < 0.05). The prognostic nomogram obtained good clinical usefulness in predicting the patient outcomes after TACE. (4) Conclusions: Based on each pre-TACE lipiodol deposition, two sessions are recommended before abandoning cTACE or combining treatment for patients with intermediate-stage HCC. Furthermore, the nomogram based on pre-TACE-3 lipiodol deposition can be used to predict the prognoses of patients with BCLC B-stage HCC.

A cuproptosis and copper metabolism-related gene prognostic index for head and neck squamous cell carcinoma.

BackgroundThe purpose of this study was to identify the prognostic value of cuproptosis and copper metabolism–related genes, to clarify their molecular and immunological characteristics, and to elucidate their benefits in head and neck squamous cell carcinoma (HNSCC).MethodsThe details of human cuproptosis and copper metabolism–related genes were searched and filtered from the msigdb database and the latest literature. To identify prognostic genes associated with cuproptosis and copper metabolism, we used least absolute shrinkage and selection operator regression, and this coefficient was used to set up a prognostic risk score model. HNSCC samples were divided into two groups according to the median risk. Afterwards, the function and immune characteristics of these genes in HNSCC were analyzed.ResultsThe 14-gene signature was constructed to classify HNSCC patients into low-risk and high-risk groups according to the risk level. In the The Cancer Genome Atlas (TCGA) cohort, the overall survival (OS) rate of the high-risk group was lower than that of the low-risk group (P < 0.0001). The area under the curve of the time-dependent Receiver Operator Characteristic (ROC) curve assessed the good performance of the genetic signature in predicting OS and showed similar performance in the external validation cohort. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment assays and Protein-Protein Interaction (PPI) protein networks have been used to explore signaling pathways and potential mechanisms that were markedly active in patients with HNSCC. Furthermore, the 14 cuproptosis and copper metabolism-related genes were significantly correlated with the immune microenvironment, suggesting that these genes may be linked with the immune regulation and development of HNSCC.ConclusionsOur results emphasize the significance of cuproptosis and copper metabolism as a predictive biomarker for HNSCC, and its expression levels seem to be correlated with immune- related features; thus, they may be a possible biomarker for HNSCC prognosis.

A ferroptosis-related gene signature and immune infiltration patterns predict the overall survival in acute myeloid leukemia patients.

Targeted therapy for acute myeloid leukemia (AML) is an effective strategy, but currently, there are very limited therapeutic targets for AML treatment. Ferroptosis is strongly related to drug resistance and carcinogenesis. However, there are few reports about ferroptosis in AML. This article explores the relationship between ferroptosis-related gene (FRG) expression and prognosis in AML patients from the FerrDb and the Cancer Genome Atlas (TCGA) databases. The ferroptosis-related gene ARNTL was observed to have high expression and poor prognosis in AML. Receiver operating characteristic curve (ROC) analysis revealed the predictive accuracy of the signature. The area under the time-dependent ROC curve (AUC) was 0.533 at one year, 0.619 at two years, and 0.622 at three years within the training cohort. Moreover, we found that the ARNTL expression is closely associated with tumor-infiltrating immune cells like the macrophages and NK cells. Inhibiting the ARNTL expression suppressed colony formation and induced ferroptosis in AML cells. Overall, the survival prediction model constructed based on ARNTL accurately predicted the survival in AML patients, which could be a potential candidate for diagnosing and treating AML.

The prognostic significance of inflammation-immunity-nutrition score on postoperative survival and recurrence in hepatocellular carcinoma patients.

BackgroundInflammation, immunity, and nutrition status play important roles in tumorigenesis, progression, and metastasis. This study aimed to evaluate the prognostic value of Inflammation-Immunity-Nutrition Score (IINS) for overall survival (OS) and progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) undergoing radical surgery.MethodsA total of 204 HCC patients who met the criteria were included in this retrospective study: 144 in the prediction model and 60 in the validation model. IINS was constructed based on the sum of classification scores of preoperative high-sensitivity C-reactive protein (hsCRP), lymphocyte (LYM), and albumin (ALB). The associations between the IINS group and the clinicopathologic characteristics were analyzed using Pearson’s χ2 test or Fisher’s exact test. Multivariate Cox regression analysis was used to evaluate variables significant on univariate analysis. Kaplan-Meier survival curves were conducted to investigate the prognostic values of IINS, Alpha-fetoprotein (AFP) and IINS-AFP classification. The prognostic performances of all the potential prognostic factors were further compared by receiver operating characteristic (ROC) curve, and time-dependent ROC curve. The internal validation and external validation were used to ensure the credibility of this prediction model.ResultsThe patients were divided into low and high IINS groups according to the median of IINS. According to multivariate Cox regression analyses, the Barcelona Clinic Liver Cancer (BCLC) Stage (P=0.003), AFP (P=0.013), and IINS (P=0.028) were independent prognostic factors for OS, and BCLC Stage (P=0.009), microvascular invasion (P=0.030), and IINS (P=0.031) were independent prognostic factors for PFS. High IINS group were associated with significantly worse OS and PFS compared with low IINS group (P<0.001; P=0.004). In terms of clinical prognosis, IINS-AFP classification was good in group I, moderate in group II, and poor in group III. Group I had a longer OS (P<0.001) and PFS (P=0.008) compared with group II and III. ROC analysis revealed that IINS-AFP classification had a better prognostic performance for OS (AUC: 0.767) and PFS (AUC: 0.641) than other predictors, excluding its slightly lower predictive power for PFS than IINS. The time-dependent ROC curves also showed that both IINS (12-month AUC: 0.650; 24-month AUC: 0.670; 36-month AUC: 0.880) and IINS-AFP classification (12-month AUC: 0.720; 24-month AUC: 0.760; 36-month AUC: 0.970) performed well in predicting OS for HCC patients. Furthermore, the internal validation and external validation proved that IINS had good predictive performance, strong internal validity and external applicability, and could be used to establish the prediction model.ConclusionInflammation-immunity-nutrition score could be a powerful clinical prognostic indicator in HCC patients undergoing radical surgery. Furthermore, IINS-AFP classification presents better prognostic performance than IINS or AFP alone, and might serve as a practical guidance to help patients adjust treatment and follow-up strategies to improve future outcomes.

Development of a Nomogram Model to Predict in-Hospital Survival in Patients with Multiple Trauma.

Background Herein, we purposed to establish a nomogram model capable of assessing the probability of in-hospital survival in patients with multiple trauma. Methods Our retrospective study is associated with 286 multiple trauma patients with 21 variables from 2017 to 2021 in The Second Affiliated Hospital, Hengyang Medical School, University of South China. We performed the univariate and multivariate logistic regression analyses for investigating the risk factors of multiple trauma. Further, we constructed a novel nomogram model, and this nomogram was evaluated by a calibration plot. Based on the multivariate analysis or the nomogram prediction model, we calculated the risk score of each patient for multiple trauma. Moreover, we compared the survival probability between the high-risk score and low-risk score groups. Finally, we assessed the discrimination of the risk score by using the C-index and the time-dependent receiver operating characteristics (ROC) curve. Results Multivariate regression analysis revealed that the age and ISS scores were the independent risk factors, while the GCS score had protective effects on in-hospital survival. The high C-index and area under the curve (AUC) of the ROC curve confirmed reasonable discrimination for the multivariate analysis and the nomogram prediction model. Further, the calibration plot indicated reasonable accuracy of the nomogram predicting 30-day and 60-day survival probabilities. Conclusion The nomogram model established here has good predictive efficacy for in-hospital survival of patients with multiple injuries.

Risk of non-colorectal cancer-related death in elderly patients with the disease: A comparison of five preoperative risk assessment indices.

A considerable number of elderly patients with colorectal cancer (CRC) die of non-CRC-related causes. The Controlling Nutritional Status (CONUT) score, American Society of Anesthesiologists Physical Status classification, Charlson Comorbidity Index, National Institute on Aging, and National Cancer Institute Comorbidity Index, and Adult Comorbidity Evaluation-27 score are all known predictors of survival in patients with CRC. However, the utility of these indices for predicting non-CRC-related death in elderly CRC patients is not known. The study population comprised 364 patients aged 80 years or more who received curative resection for stage I-III CRC between 2000 and 2016. The association of each index with non-CRC-related death was compared by competing-risks analysis such as the cumulative incidence function and proportional subdistribution hazards regression analysis as well as time-dependent receiver-operating characteristic (ROC) analysis. There were 85 deaths (40 CRC-related and 45 non-CRC-related) during a median observation period of 53.2months. Cumulative incidence function analysis identified CONUT score as the most suitable for risk stratification for non-CRC-related death. In proportional subdistribution hazards regression, risk of non-CRC-related death increased significantly as CONUT score worsened (2/3/4 vs. 0/1, hazard ratio 1.73, 95% confidence interval [CI] 0.91-3.15; ≥5 vs. 2/3/4, hazard ratio 2.71, 95% CI 1.08-6.81). Time-dependent ROC curve analysis showed that CONUT score were consistently superior to other indices during the 5-year observation period. The majority of deaths in elderly patients with CRC were not CRC-related. CONUT score was the most useful predictor of non-CRC-related death in these patients.

Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway-Related Prognostic Prediction Model in Gastric Cancer.

Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism. Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus (GEO) database was used as the validation set. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and multivariate Cox regression analyses; and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments were used to predict the model-related potential pathways, m6A modifications, and the association between model genes and m6A. Results: A four-gene prognostic model (GHR, PIM1, IFNA8, and IFNB1) was constructed, namely, riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than those with low riskScore expression (p = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p < 0.001; HR = 3.342, 95%, CI = 1.834–6.088). The 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes. Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.