Entire Cohorts Research Articles

A Comprehensive Analysis of Pyroptosis-Related lncRNAs Signature Associated With Prognosis and Tumor Immune Microenvironment of Pancreatic Adenocarcinoma.

Background: Globally, pancreatic adenocarcinoma (PAAD) is a common and highly devastating gastrointestinal malignancy that seriously threatens human health. Pyroptosis refers to an emerging form of programmed cell death that has been discovered in recent years, and studies have demonstrated that long non-coding RNA (lncRNA) may act as a moderator in the pyroptosis process of cancer cells. However, relevant explorations about lncRNAs and pyroptosis are still insufficient in PAAD. Therefore, our research is designed to make a comprehensive analysis of the potential values of pyroptosis-related lncRNAs in PAAD. Methods: By integrating the RNA-sequencing, somatic mutation, and copy number variation (CNV) datasets, as well as the clinicopathological features, we established and validated a risk signature based on pyroptosis-related lncRNAs, and comprehensively analyzed its clinical significance and the potential connection with the tumor immune microenvironment (TIME). Consequences: The genetic variation landscape displayed that the somatic mutations were rare while CNV changes were general and mainly concentrated on copy number amplification of these 52 pyroptosis-related genes. Subsequently, a risk signature consisting of 10 lncRNAs (TRAF3IP2-AS1, LINC00519, LINC01133, LINC02251, AC005332.6, AL590787.1, AC090114.2, TRPC7-AS1, MIR223HG, and MIR3142HG) was constructed and patients were divided into different subgroups according to the median risk score; patients with high-risk scores presented worse outcomes compared to those with low-risk scores in the training, testing, and entire cohorts. Furthermore, patients at low-risk scores possessed a higher infiltration abundance of immune cells compared with high-risk patients, which was consistent with the expression levels of lncRNAs between the high/low-risk groups. Drug sensitivity analysis showed that low-risk scores were related to anti-cancer agents like AICAR and Axitinib, whereas high-risk scores were connected with certain drugs such as AUY922. These results demonstrated that our risk signature could be used for prognosis prediction; additionally, it was also related to the TIME that might act as a potential indicator to instruct immunotherapeutic strategies. Conclusion: This work explored the significance of the risk model constructed by pyroptosis-related lncRNAs in prognosis prediction and its internal link with the immune microenvironment of PAAD. The results are expected to assist in the diagnosis, prognostic assessment, and management of patients with PAAD.

A Ten-Year Radiostereometric Analysis of Polyethylene Wear Between Oxidized Zirconium and Cobalt Chrome Articulations in Total Hip Arthroplasty.

Oxidized zirconium (OxZi) femoral heads combine the decreased abrasive properties of ceramics with the toughness of metal alloys to lower wear and increase the durability of total hip arthroplasty. The purpose of this study was to compare the 10-year polyethylene wear rates between OxZi/XLPE and cobalt chrome (CoCr)/XLPE articulations. A radiostereometric analysis (RSA) was performed on two cohorts of 23 patients who underwent total hip arthroplasty using either OxZi/XLPE or CoCr/XLPE at a minimum of 10-year follow-up. Cohorts were matched for age, gender, body mass index (BMI), and diagnosis. Polyethylene wear was measured using RSA to determine total and steady-state wear rates for both cohorts. Preoperative and postoperative patient-reported outcome measures (SF12, HHS, and Western Ontario and McMaster Universities Arthritis Index scores) were compared. The mean total head penetration rate was found to be statistically different between the entire cohorts (OxZi 0.048±0.021 mm/y, CoCr 0.035±0.017 mm/y, P= .02) but not when 28-mm heads only (OxZi 0.045±0.016 mm/y, CoCr 0.034±0.017 mm/y, P= .066) were directly compared. The mean steady-state wear rate was not significantly different between the entire cohorts (OxZi 0.031±0.021 mm/y, CoCr 0.024±0.019 mm/y, P= .24) or 28-mm head cohorts (OxZi 0.028±0.019 mm/y, CoCr 0.024±0.019 mm/y, P= .574). Outcome measures showed no statistical difference except for the Harris Hip Score where the OxZi cohort demonstrated higher median scores. Using RSA to evaluate the 10-year in-vivo head penetration, there was no statistically significant difference in steady-state wear rates between OxZi and CoCr articulations. Both bearing combinations demonstrated wear rates well below the threshold for osteolysis.

Robotic versus thoraco-laparoscopic minimally invasive Ivor Lewis esophagectomy, a matched-pair single-center cohort analysis.

Minimally invasive esophagectomy (MIE) is becoming more widespread with a documented improvement in postoperative morbidity based on level I evidence. However, there is a lack of consensus regarding the optimal MIE approach, conventional thoracoscopy/laparoscopy vs robotics as well as the ideal anastomotic technique. All patients who underwent MIE via an Ivor Lewis approach with a side-to-side stapled anastomosis were included. The thoracoscopy-laparoscopy (TL) group was compared to the robotic group with respect to perioperative outcomes using the entire cohorts and after 1:1 propensity score matching. Comparisons were made using the Mann-Whitney U and Fisher's exact tests. Between July 2013 and November 2020, 72 TL and 67 robotic Ivor Lewis MIE were performed. After comparing the two unadjusted cohorts and 51 propensity matched pairs, there was a decrease in Clavien-Dindo Grade 2 or above complications in the robotic vs TL group (59.7% vs 41.8% [P = 0.042], (62.7% vs 39.2% [P = 0.029]), respectively. In both analyses, there was a reduction in hospital length of stay (median of 8 vs 7days, P< 0.001) and a trend toward less anastomotic leaks in the robotic group (Unadjusted: 12.5 vs 3% [P = 0.057], Propensity-matched analysis: 13.7% vs 3.9% [P = 0.16]), respectively. A clinically significant decrease in overall morbidity, cardiac complications and hospital length of stay was observed in the robotic Ivor Lewis cohort when compared with the TL group at a high volume MIE program. Side-to-side stapled thoracic anastomoses utilizing a robotic platform provides the best outcomes in this single institution experience.

Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score.

BackgroundOncotype DX Recurrence Score (RS) has been widely used to predict chemotherapy benefits in patients with estrogen receptor-positive breast cancer. Studies showed that the features used in Magee equations correlate with RS. We aimed to examine whether deep learning (DL)-based histology image analyses can enhance such correlations.MethodsWe retrieved 382 cases with RS diagnosed between 2011 and 2015 from the Emory University and the Ohio State University. All patients received surgery. DL models were developed to detect nuclei of tumor cells and tumor-infiltrating lymphocytes (TILs) and segment tumor cell nuclei in hematoxylin and eosin (H&E) stained histopathology whole slide images (WSIs). Based on the DL-based analysis, we derived image features from WSIs, such as tumor cell number, TIL number variance, and nuclear grades. The entire patient cohorts were divided into one training set (125 cases) and two validation sets (82 and 175 cases) based on the data sources and WSI resolutions. The training set was used to train the linear regression models to predict RS. For prediction performance comparison, we used independent variables from Magee features alone or the combination of WSI-derived image and Magee features.ResultsThe Pearson’s correlation coefficients between the actual RS and predicted RS by DL-based analysis were 0.7058 (p-value = 1.32 × 10–13) and 0.5041 (p-value = 1.15 × 10–12) for the validation sets 1 and 2, respectively. The adjusted R2 values using Magee features alone are 0.3442 and 0.2167 in the two validation sets, respectively. In contrast, the adjusted R2 values were enhanced to 0.4431 and 0.2182 when WSI-derived imaging features were jointly used with Magee features.ConclusionOur results suggest that DL-based digital pathological features can enhance Magee feature correlation with RS.

Mast cell marker gene signature in head and neck squamous cell carcinoma

BackgroundMast cells can reshape the tumour immune microenvironment and greatly affect tumour occurrence and development. However, mast cell gene prognostic and predictive value in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study was conducted to identify and establish a prognostic mast cell gene signature (MCS) for assessing the prognosis and immunotherapy response of patients with HNSCC.MethodsMast cell marker genes in HNSCC were identified using single-cell RNA sequencing analysis. A dataset from The Cancer Genome Atlas was divided into a training cohort to construct the MCS model and a testing cohort to validate the model. Fluorescence in-situ hybridisation was used to evaluate the MCS model gene expression in tissue sections from patients with HNSCC who had been treated with programmed cell death-1 inhibitors and further validate the MCS.ResultsA prognostic MCS comprising nine genes (KIT, RAB32, CATSPER1, SMYD3, LINC00996, SOCS1, AP2M1, LAT, and HSP90B1) was generated by comprehensively analysing clinical features and 47 mast cell-related genes. The MCS effectively distinguished survival outcomes across the training, testing, and entire cohorts as an independent prognostic factor. Furthermore, we identified patients with favourable immune cell infiltration status and immunotherapy responses. Fluorescence in-situ hybridisation supported the MCS immunotherapy response of patients with HNSCC prediction, showing increased high-risk gene expression and reduced low-risk gene expression in immunotherapy-insensitive patients.ConclusionsOur MCS provides insight into the roles of mast cells in HNSCC prognosis and may have applications as an immunotherapy response predictive indicator in patients with HNSCC and a reference for immunotherapy decision-making.

Comprehensive Analysis of Ferroptosis- and Immune-Related Signatures to Improve the Prognosis and Diagnosis of Kidney Renal Clear Cell Carcinoma.

BackgroundAlmost 40% of patients with kidney renal clear cell carcinoma (KIRC) with advanced cancers eventually develop to metastases, and their 5-year survival rates are approximately 10%. Aberrant DNA methylations are significantly associated with the development of KIRC. The aim of our present study was to identify suitable ferroptosis- and immune-related (FI) biomarkers correlated with aberrant methylations to improve the prognosis and diagnosis of KIRC.MethodsChAMP and DESeq2 in R (3.6.2) were used to screen the differentially expressed methylation probes and differentially expressed genes, respectively. Univariate and multivariate Cox regression were used to identify the overall survival (OS)–related biomarkers.ResultsWe finally identified five FI biomarkers (CCR4, CMTM3, IFITM1, MX2, and NR3C2) that were independently correlated with the OS of KIRC. The area under the curve value of the receiver operating characteristic value of prognosis model was 0.74, 0.68, and 0.72 in the training, validation, and entire cohorts, respectively. The sensitivity and specificity of the diagnosis model were 0.8698 and 0.9722, respectively. In addition, the prognosis model was also significantly correlated with several immune cells and factors.ConclusionOur present study suggested that these five FI-DEGs (CCR4, CMTM3, IFITM1, MX2, and NR3C2) could be used as prognosis and diagnosis biomarkers for patients with KIRC, but further cross-validation clinical studies are still needed to confirm them.

Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data.

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis. A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan-Meier curve was used for survival analysis. A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome. We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.

It took a pandemic: Enduring lessons for improving accessibility in medical education

In March of 2020, the rapid onset of the COVID‐19 pandemic halted the gears of undergraduate medical education programs in the U.S. and in many locations across the globe. Disruptions to usual practice provided the impetus for educators to creatively re‐envision medical education with approaches that shared a common feature: flexibility. Out of the chaos of the pandemic rose a collective willingness to think creatively and adapt (see https://bit.ly/3x91Y8H). Although this type of creativity had been used prior to the pandemic to develop accommodations for individual students with disabilities, these changes occurred on a larger scale and increased flexibility for entire cohorts and programs.

Identification of a Solute Carrier Family-Based Signature for Predicting Overall Survival in Osteosarcoma.

Given the important role of SLC family in essential physiological processes including nutrient uptake, ion transport, and waste removal, and that their dysregulation was found in distinct forms of cancer, here we identified a novel gene signature of SLC family for patient risk stratification in osteosarcoma. Gene expression data and relevant clinical materials of osteosarcoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Prognosis-related SLC genes were identified by performing univariate Cox regression analysis and were utilized to construct a four-SLC gene signature in osteosarcoma. It allowed patients to be classified into high- and low-risk groups, and Kaplan-Meier survival analysis in the training, testing, entire, and external GSE21257 cohorts suggested that the overall survival of patients in high-risk group was consistently worse than that in low-risk group, suggesting the promising accuracy and generalizability of the SLC-based signature in predicting the prognosis of patients with osteosarcoma. Moreover, univariate and multivariate Cox regression analyses indicated that the derived risk score was the only independent prognostic factor for osteosarcoma patients in TCGA and GSE21257 cohorts. Besides, a prognostic nomogram comprising the derived risk score and clinical features including gender and age was developed for clinical decision-making. Functional enrichment analyses of the differentially expressed genes between high- and low-risk group revealed that immune-related biological processes and pathways were significantly enriched. Estimation of tumor immune microenvironment using ESTIMATE algorithm revealed that patients with lower risk score had higher stromal, immune, and ESTIMATE score, and lower tumor purity. ssGSEA analyses indicated that the scores of various immune subpopulations including CD8+ T cells, DCs, and TIL were lower in high-risk group than these in low-risk group in both cohorts. As for the related immune functions, the scores of APC co-inhibition, CCR, check-point, T cell co-stimulation, and Type II IFN response were lower in high-risk group than these in low-risk group in both cohorts. In all, we identified a novel prognostic signature based on four SLC family genes that accurately predicted overall survival in osteosarcoma patients. Furthermore, the signature is linked to differences in immunological status and immune cell infiltrations in the tumor microenvironment.

A Novel Pyroptotic and Inflammatory Gene Signature Predicts the Prognosis of Cutaneous Melanoma and the Effect of Anticancer Therapies.

PurposeThe purpose of this study was to construct a gene signature comprising genes related to both inflammation and pyroptosis (GRIPs) to predict the prognosis of patients with cutaneous melanoma patients and the efficacy of immunotherapy, chemotherapy, and targeted therapy in these patients.MethodsGene expression profiles were collected from The Cancer Genome Atlas. Weighted gene co-expression network analysis was performed to identify GRIPs. Univariable Cox regression and Lasso regression further selected key prognostic genes. Multivariable Cox regression was used to construct a risk score, which stratified patients into high- and low-risk groups. Areas under the ROC curves (AUCs) were calculated, and Kaplan-Meier analyses were performed for the two groups, following validation in an external cohort from Gene Expression Omnibus (GEO). A nomogram including the GRIP signature and clinicopathological characteristics was developed for clinical use. Gene set enrichment analysis illustrated differentially enriched pathways. Differences in the tumor microenvironment (TME) between the two groups were assessed. The efficacies of immune checkpoint inhibitors (ICIs), chemotherapeutic agents, and targeted agents were predicted for both groups. Immunohistochemical analyses of the GRIPs between the normal and CM tissues were performed using the Human Protein Atlas data. The qRT-PCR experiments validated the expression of genes in CM cell lines, Hacat, and PIG1 cell lines.ResultsA total of 185 GRIPs were identified. A novel gene signature comprising eight GRIPs (TLR1, CCL8, EMP3, IFNGR2, CCL25, IL15, RTP4, and NLRP6) was constructed. The signature had AUCs of 0.714 and 0.659 for predicting 3-year overall survival (OS) in the TCGA entire and GEO validation cohorts, respectively. Kaplan-Meier analyses revealed that the high-risk group had a poorer prognosis. Multivariable Cox regression showed that the GRIP signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The nomogram showed good accuracy and reliability in predicting 3-year OS (AUC = 0.810). GSEA and TME analyses showed that the high-risk group had lower levels of pyroptosis, inflammation, and immune response, such as lower levels of CD8+ T-cell infiltration, CD4+ memory-activated T-cell infiltration, and ICI. In addition, low-risk patients whose disease expressed PD-1 or CTLA-4 were likely to respond better to ICIs, and several chemotherapeutic and targeted agents. Immunohistochemical analysis confirmed the distinct expression of five out of the eight GRIPs between normal and CM tissues.ConclusionOur novel 8-GRIP signature can accurately predict the prognosis of patients with CM and the efficacies of multiple anticancer therapies. These GRIPs might be potential prognostic biomarkers and therapeutic targets for CM.

Prediction of Survival Rate and Chemotherapy Effect by an Immune Score Model in Colorectal Cancer.

Colorectal cancer is the third most common cancer and the second leading cause of cancer-related deaths. Immune cells in the tumor microenvironment play an important role in the development of tumors. In this study, CIBERSORT was used to estimate the subset of the immune cells using bulk gene expression data (i.e., TCGA, GEO, and cBioPortal databases). 1,087 samples were included in the analysis. The results revealed that among the 22 immune cell subsets that were evaluated, resting and activated NK cells, macrophage M1 and M2, and resting mast cells are associated with significant improvements in patient survival of colorectal cancer. The 15-year survival rates for the training cohort showed 49.1% and 32.5%, respectively, for the low- and high-risk groups. Likewise, the validation and entire cohorts showed 77.3% versus 47.2% and 65.3% versus 46.5%, respectively, for the low- and high-risk groups. Also, the prognostic immune score in predicting the chemotherapy effects showed that the low-risk group had a better survival superiority over the high-risk group, whether patients received chemotherapy or not. The gene set enrichment analysis showed that the low-risk group was highly enriched in pathways or processes related to immune response. The immune checkpoint assessment revealed significantly higher mRNA expressions of CTLA4 in the lower risk group than in the higher risk group. Altogether, this study offers information that could improve the prognosis of colorectal cancer.

Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics.

The differentiation of soft tissue lipomas from atypical lipoma tumors (ALTs) of the extremities is important because of the distinction of the cytogenetic profiles and the treatment decisions. To investigate a radiomics method to differentiate between lipomas and ALTs of the extremities. Retrospective. Imaging data of 122 patients including 90 cases of lipomas and 32 cases of ALTs. Axial T1-weighted imaging and fat suppressed T2-weighted imaging at 3.0T MRI. Analysis of variance and the least absolute shrinkage and selection operator methods were used for feature selection and the random forest method was used to build three radiomics models based on T1WI, FS T2WI, and their combination (T1&T2WI). Three independent radiologists classified the tumors based on the subjective assessments. The area under the curve (AUC) of the receiver operating characteristic curve, accuracy, F1-score, specificity, and sensitivity were employed. The differences of the classifiers and discriminating ability of the radiologists and the radiomics model were compared by Delong test. A P value <0.05 was considered significant. Kappa test was used to determine the inter-reader agreements between the radiologists. The AUCs were 0.952 (95% confidence interval [CI]: 0.785-0.998), 0.944 (95% CI: 0.774-0.997), and 0.968 (95% CI: 0.809-1) for T1WI, FS T2WI, and T1&T2WI models in testing sets respectively. Delong test showed there were no significant difference between the different radiomics models (P > 0.05). The AUCs of the radiologists were 0.893 (95% CI: 0.824-0.942), 0.831 (95% CI: 0.752-0.893), and 0.893 (95% CI: 0.824-0.94), respectively. There were significant difference between radiomics model and radiologists' model in the training and entire cohorts (P < 0.05) while there were no significant difference in the testing sets (P > 0.05). Radiomics has the potential to distinguish between lipomas and ALTs of the extremities and their discrimination ability is no weaker than the senor radiologists. 3 TECHNICAL EFFICACY STAGE: 2.

A Novel Gene Signature of Tripartite Motif Family for Predicting the Prognosis in Kidney Renal Clear Cell Carcinoma and Its Association With Immune Cell Infiltration.

Given the importance of tripartite motif (TRIM) proteins in diverse cellular biological processes and that their dysregulation contributes to cancer progression, we constructed a robust TRIM family signature to stratify patients with kidney renal clear cell carcinoma (KIRC). Transcriptomic profiles and corresponding clinical information of KIRC patients were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. Prognosis-related TRIM family genes were screened and used to construct a novel TRIM family-based signature for the training cohort. The accuracy and generalizability of the prognostic signature were assessed in testing, entire, and external ICGC cohorts. We analyzed correlations among prognostic signatures, tumor immune microenvironment, and immune cell infiltration. The results of univariate Cox regression and Kaplan-Meier survival analyses revealed 27 TRIMs that were robustly associated with the prognosis of patients with KIRC. We applied Lasso regression and multivariate Cox regression analyses to develop a prognostic signature containing the TRIM1, 13, 35, 26, 55, 2, 47, and 27 genes to predict the survival of patients with KIRC. The accuracy and generalizability of this signature were confirmed in internal and external validation cohorts. We also constructed a predictive nomogram based on the signature and the clinicopathological characteristics of sex, age, and T and M status to aid clinical decision-making. We analyzed immune cell infiltration analysis and found that CD8 T cells, memory resting CD4 T cells, and M2 macrophages were the most enriched components in the KIRC tumor immune microenvironment. A higher level of immune infiltration by plasma cells, follicular helper T cells, and activated NK cells, and a lower level of immune infiltration by memory resting CD4 T cells, M1 and M2 macrophages, and resting dendritic cells were associated with higher risk scores. Overall, our eight-gene TRIM family signature has sufficient accuracy and generalizability for predicting the overall survival of patients with KIRC. Furthermore, this prognostic signature is associated with tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

Customer base analysis with recurrent neural networks

One of the primary goals that researchers look to achieve through customer base analysis is to leverage historical records of individual customer transactions and related context factors to forecast future behavior, and to link these forecasts with actionable characteristics of individuals, managerially significant customer sub-groups, and entire cohorts. This paper presents a new approach that helps firms leverage the automatic feature extraction capabilities of a specific type of deep learning models when applied to customer transaction histories in non-contractual business settings (i.e., when the time at which a customer becomes inactive is unobserved by the firm). We show how the proposed deep learning model improves on established models both in terms of individual-level accuracy and overall cohort-level bias. It also helps managers in capturing seasonal trends and other forms of purchase dynamics that are important to detect in a timely manner for the purpose of proactive customer-base management. We demonstrate the model performance in eight empirical real-life settings which vary broadly in transaction frequency, purchase (ir)regularity, customer attrition, availability of contextual information, seasonal variance, and cohort size. We showcase the flexibility of the approach and how the model further benefits from taking into account static (e.g., socio-economic variables, demographics) and dynamic context factors (e.g., weather, holiday seasons, marketing appeals). We make an open-source reference implementation of the newly developed method available at https://github.com/valendin/rfm2lstm.

Differentiation between combined hepatocellular cholangiocarcinoma and hepatocellular carcinoma: comparison of diagnostic performance between ultrasomics-based model and CEUS LI-RADS v2017

BackgroundThe imaging findings of combined hepatocellular cholangiocarcinoma (CHC) may be similar to those of hepatocellular carcinoma (HCC). CEUS LI-RADS may not perform well in distinguishing CHC from HCC. Studies have shown that radiomics has an excellent imaging analysis ability. This study aimed to establish and confirm an ultrasomics model for differentiating CHC from HCC.MethodsBetween 2004 and 2016, we retrospectively identified 53 eligible CHC patients and randomly included 106 eligible HCC patients with a ratio of HCC:CHC = 2:1, all of whom were categorized according to Contrast-Enhanced (CE) ultrasonography (US) Liver Imaging Reporting and Data System (LI-RADS) version 2017. The model based on ultrasomics features of CE US was developed in 74 HCC and 37 CHC and confirmed in 32 HCC and 16 CHC. The diagnostic performance of the LI-RADS or ultrasomics model was assessed by the area under the curve (AUC), accuracy, sensitivity and specificity.ResultsIn the entire and validation cohorts, 67.0% and 81.3% of HCC cases were correctly assigned to LR-5 or LR-TIV contiguous with LR-5, and 73.6% and 87.5% of CHC cases were assigned to LR-M correctly. Up to 33.0% of HCC and 26.4% of CHC were misclassified by CE US LI-RADS. A total of 90.6% of HCC as well as 87.5% of CHC correctly diagnosed by the ultrasomics model in the validation cohort. The AUC, accuracy, sensitivity of the ultrasomics model were higher though without significant difference than those of CE US LI-RADS in the validation cohort.ConclusionThe proposed ultrasomics model showed higher ability though the difference was not significantly different for differentiating CHC from HCC, which may be helpful in clinical diagnosis.

Predictors of time to death among cervical cancer patients at Tikur Anbesa specialized hospital from 2014 to 2019: A survival analysis.

Cervical cancer (CC) is the 4th most prevalent cancer among females globally. In Ethiopia, around 7,095 new CC cases are diagnosed every year and it is the second common cause of cancer deaths in women. There is limited evidence on survival status as well as about predictors of time to death among CC patients in Ethiopia. Thus, this study investigated the five-year survival status and predictors of time to death among CC patients who had been admitted at Tikur Anbesa specialized Hospital (TASH) from 2014-2019. Facility-based, retrospective-cohort study was conducted at Tikur Anbesa specialized Hospital among 348 patients from June 2014 to June 2019. A systematic random sampling method was employed to select the study participants. Data were extracted from the patient card, and through phone calls. The data was collected using the android version CS-Entry tool. Data was analyzed by SPSS version 23. Kaplan and Meier's method was used to estimate survival functions and Cox-proportional hazard regression analysis was carried out in order to identify the independent predictors of time to death. The overall incidence of death was 31 per 100 person-years of follow up. The median (IQR) follow-up time of the entire cohorts was 18.55 (8.96-49.65) months. The independent predictors for time to death included; age older than 50 years [AHR: 1.4; 95% CI: 1.1-1.9], late stage of CC at diagnosis [AHR: 2.2; 95% CI: 1.7-2.9], No CC treatment [AHR: 2.1; 95% CI: 1.5-3.1] and HIV positive [AHR: 2.3; 95% CI: 1.4-3.8]. The death rate of CC patients was high. The significant predictors associated with shorten time to death of CC patients were older age, advanced cancer stage at diagnosis, HIV infection and not receiving cancer treatment. Therefore, improving early detection and initiation of treatment for all CC patients is necessary in order to improve patient's survival status. The government needs to strengthen the routine CC screening programs to address high-risk women such as elderly and HIV positive women in Ethiopia.

N6-Methyladenosine-Related lncRNAs Are Potential Prognostic Biomarkers and Correlated With Tumor Immune Microenvironment in Osteosarcoma.

N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play vital roles in the prognostic value and immune microenvironment of malignant tumors. Here, we constructed a m6A-related lncRNA signature in osteosarcoma samples from TCGA dataset and analyzed the association of the signature with tumor immune microenvironment. m6A-related lncRNAs were identified by performing Pearson’s correlation analysis and were used to construct a novel m6A-related lncRNA signature in osteosarcoma. Validation in testing and entire cohorts confirmed the satisfactory accuracy of the risk signature. Principal-component analysis verifies the grouping ability of the risk signature. Functional enrichment analyses connected immune with the risk signature based on the six m6A-related lncRNAs. When patients were separated into high- and low-risk group based on their risk scores, we found that patients in the high-risk group had lower stromal scores, immune scores, and ESTIMATE scores, while the tumor purity was higher in the high-risk group than that in the low-risk group. As for immune cell infiltration, the proportion of monocytes was significantly higher in the low-risk group than that in the high-risk group. Of the six lncRNAs, AC004812.2 was a protective factor in osteosarcoma and low expression of AC004812.2 predicted worse overall survival. Overexpression of AC004812.2 inhibited 143B cell proliferation and increased the expression levels of IGF2BP1 and YTHDF1. In all, our m6A-related lncRNA signature was a potential prognostic biomarker and correlated with tumor immune microenvironment and immune cell infiltration, and AC004812.2 might be an important regulator of m6A modification and a promising therapeutic target in osteosarcoma.

Size-dependent mortality of corals during marine heatwave erodes recovery capacity of a coral reef.

For many long-lived taxa, such as trees and corals, older, and larger individuals often have the lowest mortality and highest fecundity. However, climate change-driven disturbances such as droughts and heatwaves may fundamentally alter typical size-dependent patterns of mortality and reproduction in these important foundation taxa. Working in Moorea, French Polynesia, we investigated how a marine heatwave in 2019, one of the most intense marine heatwaves at our sites over the past 30years, drove patterns of coral bleaching and mortality. The marine heatwave drove island-wide mass coral bleaching that killed up to 76% and 65% of the largest individuals of the two dominant coral genera, Pocillopora and Acropora, respectively. Colonies of Pocillopora and Acropora ≥30cm diameter were ~3.5× and ~1.3×, respectively, more likely to die than colonies <30-cm diameter. Typically, annual mortality in these corals is concentrated on the smallest size classes. Yet, this heatwave dramatically reshaped this pattern, with heat stress disproportionately killing larger coral colonies and equalizing annual mortality rates across the size spectrum. This shift in the size-mortality relationship reduced the overall fecundity of these genera by >60% because big corals are disproportionately important for reproduction on reefs. Additionally, the survivorship of microscopic coral recruits, critical for the recovery of corals following disturbances, declined to 2%, over an order of magnitude lower compared to a year without elevated thermal stress, where 33% of coral recruits survived. While other research has shown that larger corals can bleach more frequently than smaller corals, we show the severe impact this phenomenon can have at the reef-wide scale. As marine heatwaves become more frequent and intense, disproportionate mortality of the largest, most fecund corals and near-complete loss of entire cohorts of newly-settled coral recruits will likely reduce the recovery capacity of these iconic ecosystems.

Identification and validation of an autophagy-related signature for predicting survival in lower-grade glioma

ABSTRACT Abnormal levels of autophagy have been implicated in the pathogenesis of multiple diseases, including cancer. However, little is known about the role of autophagy-related genes (ARGs) in low-grade gliomas (LGG). Accordingly, the aims of this study were to assess the prognostic values of ARGs and to establish a genetic signature for LGG prognosis. Expression profile data from patients with and without primary LGG were obtained from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression databases, respectively, and consensus clustering was used to identify clusters of patients with distinct prognoses. Nineteen differentially expressed ARGs were selected with threshold values of FDR < 0.05 and |log2 fold change (FC)| ≥ 2, and functional analysis revealed that these genes were associated with autophagy processes as expected. An autophagy-related signature was established using a Cox regression model of six ARGs that separated patients from TCGA training cohort into high- and low-risk groups. Univariate and multivariate Cox regression analysis indicated that the signature-based risk score was an independent prognostic factor. The signature was successfully validated using the TCGA testing, TCGA entire, and Chinese Glioma Genome Atlas cohorts. Stratified analyses demonstrated that the signature was associated with clinical features and prognosis, and gene set enrichment analysis revealed that autophagy- and cancer-related pathways were more enriched in high-risk patients than in low-risk patients. The prognostic value and expression of the six signature-related genes were also investigated. Thus, the present study constructed and validated an autophagy-related prognostic signature that could optimize individualized survival prediction in LGG patients.

The Clinical Impact of Unilateral Versus Bilateral Invasion Into the Seminal Vesicle in Patients With Prostate Cancer Undergoing Radical Prostatectomy.

Seminal vesicle involvement by prostate cancer has generally been considered as a key prognosticator. To assess the clinical significance of unilateral (Uni) versus bilateral (Bil) seminal vesicle invasion (SVI). We compared radical prostatectomy findings and long-term oncologic outcomes in 248 patients showing Uni-SVI (n = 139) versus Bil-SVI (n = 109). Tumor grade was significantly higher in Bil-SVI cases than in Uni-SVI cases. Additionally, Bil-SVI was significantly associated with a higher incidence of lymphovascular invasion, lymph node metastasis, or positive surgical margin, and larger estimated tumor volume. When the histopathologic features at SVI foci were compared, Grade Group (GG) 3-5/4-5/5 and cribriform morphology were significantly more often seen in Bil-SVI. Outcome analysis revealed that patients with Bil-SVI had a significantly higher risk of disease progression (P < .001) than patients with Uni-SVI. Significantly worse progression-free survival in patients with Bil-SVI was also observed in all subgroups examined, including those with no immediate adjuvant therapy (IAT) (n = 139; P = .01), IAT (n = 109; P = .001), pN0 disease (n = 153; P = .002), or pN1 disease (n = 93; P = .006). In multivariate analysis, Bil-SVI (versus Uni-SVI) showed significance for progression in the entire (hazard ratio [HR] = 1.83, P = .01), IAT (HR = 2.90, P = .006), and pN0 (HR = 2.05, P = .01) cohorts. Meanwhile, tumor grade at SVI (eg, GG4, GG5), as an independent predictor, was significantly associated with patient outcomes. Bil-SVI was found to be strongly associated with worse histopathologic features on radical prostatectomy and poorer prognosis. Pathologists may thus need to report Uni-SVI versus Bil-SVI, along with other histopathologic findings, such as Gleason score, at SVI in prostatectomy specimens.