A signature of seven hypoxia-related lncRNAs is a potential biomarker for predicting the prognosis of melanoma.

Melanoma is the most aggressive type of skin cancer and has a high mortality rate once metastasis occurs. Hypoxia is a universal characteristic of the microenvironment of cancer and a driver of melanoma progression. In recent years, long noncoding RNAs (lncRNAs) have attracted widespread attention in oncology research. In this study, screening was performed and revealed seven hypoxia-related lncRNAs AC008687.3, AC009495.1, AC245128.3, AL512363.1, LINC00518, LINC02416 and MCCC1-AS1 as predictive biomarkers. A predictive risk model was constructed via univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Patients were grouped according to the model risk score, and Kaplan-Meier analysis was performed to compare survival between groups. Functional and pathway enrichment analyses were performed to compare gene set enrichment between groups. Moreover, a nomogram was constructed with the risk score as a variable. In both the training and validation sets, patients in the low-risk group had better overall survival than did those in the high-risk group (P<0.001). The 3-, 5- and 10-year area under the curve (AUC) values for the nomogram model were 0.821, 0.795 and 0.820, respectively. Analyses of immune checkpoints, immunotherapy response, drug sensitivity, and mutation landscape were also performed. The results suggested that the low-risk group had a better response to immunotherapeutic. In addition, the nomogram can effectively predict the prognosis and immunotherapy response of melanoma patients. The signature of seven hypoxia-related lncRNAs showed great potential value as an immunotherapy response biomarker, and these lncRNAs might be treatment targets for melanoma patients.

Understanding learners' perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China.

ChatGPT, an artificial intelligence (AI)-driven language model engineered by OpenAI, has experienced a substantial upsurge in adoption within higher education due to its versatile applications and sophisticated capabilities. Although prevailing research on ChatGPT has predominantly concentrated on its technological aspects and pedagogical ramifications, a comprehensive understanding of students' perceptions and experiences regarding ChatGPT remains elusive. To address this gap, this study employed a peer interview methodology, conducting a thematic analysis of 106 first-year undergraduates and 81 first-year postgraduate students' perceptions from diverse disciplines at a comprehensive university in East China. The data analysis revealed that among the four factors examined-grade, age, gender, and major-grade emerged as the most influential determinant, followed by age and major. Postgraduate students demonstrated heightened awareness of the potential limitations of ChatGPT in addressing academic challenges and exhibited greater concern for security issues associated with its application. This research offers essential insights into students' perceptions and experiences with ChatGPT, emphasizing the importance of recognizing potential limitations and ethical concerns associated with ChatGPT usage. Additionally, the findings highlight ethical concerns, as students noted the importance of responsible data handling and academic integrity in ChatGPT usage, underscoring the need for ethical guidance in AI utilization. Moreover, further research is essential to optimize AI use in education, aiming to improve learning outcomes effectively.

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer.

BACKGROUND: Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion. MATERIALS AND METHODS: The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram. RESULTS: A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators. CONCLUSION: The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

Establishment and validation of a ferroptosis-related prognostic signature for hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high heterogeneity. The prognosis of HCC is quite poor and the prognostic prediction also has challenges. Ferroptosis is recently recognized as a kind of iron-dependent cell death, which is involved in tumor progression. However, further study is needed to validate the influence of drivers of ferroptosis (DOFs) on the prognosis of HCC. Methods: The FerrDb database and the Cancer Genome Atlas (TCGA) database were applied to retrieve DOFs and information of HCC patients respectively. HCC patients were randomly divided into training and testing cohorts with a 7:3 ratio. Univariate Cox regression, LASSO and multivariate Cox regression analyses were carried out to identify the optimal prognosis model and calculate the risk score. Then, univariate and multivariate Cox regression analyses were performed to assess the independence of the signature. At last, gene functional, tumor mutation and immune-related analyses were conducted to explore the underlying mechanism. Internal and external databases were used to confirm the results. Finally, the tumor tissue and normal tissue from HCC patients were applied to validate the gene expression in the model. Results: Five genes were identified to develop as a prognostic signature in the training cohort relying on the comprehensive analysis. Univariate and multivariate Cox regression analyses confirmed that the risk score was able to be an independent factor for the prognosis of HCC patients. Low-risk patients showed better overall survival than high-risk patients. Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Furthermore, internal and external cohorts were consistent with our results. There was a higher proportion of nTreg cell, Th1 cell, macrophage, exhausted cell and CD8+T cell in the high-risk group. The Tumor Immune Dysfunction and Exclusion (TIDE) score suggested that high-risk patients could respond better to immunotherapy. Besides, the experimental results showed that some genes were differentially expressed between tumor and normal tissues. Conclusion: In summary, the five ferroptosis gene signature showed potential in prognosis of patients with HCC and could also be regarded as a value biomarker for immunotherapy response in these patients.

ncRNA-mediated upregulation of FAM83A is associated with poor prognosis and immune infiltration in pancreatic cancer.

Introduction: Malignant pancreatic cancer has poor long-term survival. Increasing evidence shows that FAM83A (family with sequence similarity 83 member A) plays a vital role in tumorigenesis and malignant progression in some human cancer types. The present study explored the potential mechanism of FAM83A in improving the prognosis of pancreatic cancer patients. Methods: Transcriptomic and clinical data from patients were obtained from The Cancer Genome Atlas while FAM83A expression was measured in tumorous pancreatic tissue compared with normal controls by quantitative real-time PCR and immunohistochemistry. Results: FAM83A is a vital prognostic indicator and potential oncogene in pancreatic cancer via pan-cancer analysis. In silico analysis revealed that AL049555.1/hsa-miR-129-5p axis was the pivotal upstream ncRNA- mediated pathway of FAM83A in pancreatic cancer. Furthermore, FAM83A expression was related to immune cell infiltration through vital immune-related genes including programmed cell death 1 (PDCD1), and tumorigenesis through common mutation genes including KRAS protooncogene GTPase (KRAS), and SMAD family member 4 (SMAD4). In summary, ncRNA-mediated upregulation of FAM83A is associated with poor long-term survival and immune cell infiltration in pancreatic cancer. Discussion: FAM83A may be used as a novel survival-related and immune-related biomarker. This information suggests that FAM83A may be a novel therapeutic target for combined or individual treatment for patients with pancreatic cancer.

Development of a metabolism-related signature for predicting prognosis, immune infiltration and immunotherapy response in breast cancer.

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients. By analyzing RNA-seq data of BRCA samples from public databases via bioinformatic approaches, we developed a prognostic signature based on seven metabolic genes (PLA2G2D, GNPNAT1, QPRT, SHMT2, PAICS, NT5E and PLPP2). Low-risk patients showed better overall survival in all five cohorts (TCGA cohort, two external validation cohorts and two internal validation cohorts). There was a higher proportion of tumor-infiltrating CD8+ T cells, CD4+ memory resting T cells, gamma delta T cells and resting dendritic cells and a lower proportion of M0 and M2 macrophages in the low-risk group. Low-risk patients also showed higher ESTIMATE scores, higher immune function scores, higher Immunophenoscores (IPS) and checkpoint expression, lower stemness scores, lower TIDE (Tumor Immune Dysfunction and Exclusion) scores and IC50 values for several chemotherapeutic agents, suggesting that low-risk patients could respond more favorably to immunotherapy and chemotherapy. Two real-world patient cohorts receiving anti-PD-1 therapy were applied for validating the predictive results. Molecular subtypes identified based on these seven genes also showed different immune characteristics. Immunohistochemical data obtained from the human protein atlas database demonstrated the protein expression of signature genes. This research may contribute to the identification of metabolic targets for BRCA and the optimization of risk stratification and personalized treatment for BRCA patients.

Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department.

Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution's electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system's 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.