Gene expression and immune infiltration analysis comparing lesioned and preserved subchondral bone in osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease requiring additional research. This study compared gene expression and immune infiltration between lesioned and preserved subchondral bone. The results were validated using multiple tissue datasets and experiments. Methods: Differentially expressed genes (DEGs) between the lesioned and preserved tibial plateaus of OA patients were identified in the GSE51588 dataset. Moreover, functional annotation and protein-protein interaction (PPI) network analyses were performed on the lesioned and preserved sides to explore potential therapeutic targets in OA subchondral bones. In addition, multiple tissues were used to screen coexpressed genes, and the expression levels of identified candidate DEGs in OA were measured by quantitative real-time polymerase chain reaction. Finally, an immune infiltration analysis was conducted. Results: A total of 1,010 DEGs were identified, 423 upregulated and 587 downregulated. The biological process (BP) terms enriched in the upregulated genes included "skeletal system development", "sister chromatid cohesion", and "ossification". Pathways were enriched in "Wnt signaling pathway" and "proteoglycans in cancer". The BP terms enriched in the downregulated genes included "inflammatory response", "xenobiotic metabolic process", and "positive regulation of inflammatory response". The enriched pathways included "neuroactive ligand-receptor interaction" and "AMP-activated protein kinase signaling". JUN, tumor necrosis factor α, and interleukin-1ß were the hub genes in the PPI network. Collagen XI A1 and leucine-rich repeat-containing 15 were screened from multiple datasets and experimentally validated. Immune infiltration analyses showed fewer infiltrating adipocytes and endothelial cells in the lesioned versus preserved samples. Conclusion: Our findings provide valuable information for future studies on the pathogenic mechanism of OA and potential therapeutic and diagnostic targets.

Identification and Development of a 4-Gene Ferroptosis Signature Predicting Overall Survival for Diffuse Large B-Cell Lymphoma.

Background: Diffuse large B-cell lymphoma (DLBCL) is a well-differentiated disease, which makes the diagnosis and therapeutic strategy a difficult problem. While ferroptosis, as an iron-dependent form of regulated cell death, it plays an important role in causing several types of cancer. This study is aimed at exploring the prognostic value of ferroptosis-related genes in DLBCL. Methods: In our study, mRNA expression and matching clinical data of DLBCL patients were derived from Gene Expression Omnibus (GEO) database. First, multivariate cox regression model and nomogram which can predict the DLBCL patients' prognosis were built and validated. The multigene signature was constructed and optimized by the least absolute shrinkage and selection operator (LASSO) cox regression model. Also, ferroptosis-related subtypes were developed by consistent cluster. Last but not least, we explored the association between categories of infiltrating immune cells and model genes' expression. Results: Our results showed that 27 gene expressions were correlated with overall survival (OS) in the univariate cox regression analysis. A 4-gene signature was constructed through these genes to stratify patients into high-low risk groups using risk score derived from model (model 1:gene expression model). The OS of patients in the high-risk group was shorter than that of patients in the low-risk group in the TNM stage and clinically distinct subtypes (activated B cell [ABC], germinal center B cell [GCB]) (P < .001). Furthermore, it was shown that the risk score was an independent factor in clinical cox regression model for OS (model 2:clinical model) (HR>1, P < .010). Besides, in consistent cluster analysis, ferroptosis prognosis status was different among 3 subtypes. Moreover, the correlation analysis between 4-gene with immune cells showed dendritic cells may be significantly associated with DLBCL. Conclusion: This research constructed an innovative ferroptosis-related gene signature for prognostic estimation of DLBCL patients. Solutions targeting ferroptosis could be an important therapeutic intervention for DLBCL.

Detection rates of adenomas, advanced adenomas, and colorectal cancers among the opportunistic colonoscopy screening population: a single-center, retrospective study.

BACKGROUND: Colorectal cancer (CRC) screening is effective in reducing CRC incidence and mortality. The aim of this study was to retrospectively determine and compare the detection rate of adenomas, advanced adenomas (AAs) and CRCs, and the number needed to screen (NNS) of individuals in an average-risk Chinese population of different ages and genders. METHODS: This was a retrospective study performed at the Institute of Health Management, Chinese People's Liberation Army General Hospital. Colonoscopy results were analyzed for 53,152 individuals finally enrolled from January 2013 to December 2019. The detection rate of adenomas, AAs, or CRCs was computed and the characteristics between men and women were compared using chi-squared test. RESULTS: The average age was 48.8 years (standard deviation [SD], 8.5 years) for men and 50.0 years (SD, 9.0 years) for women, and the gender rate was 66.27% (35,226) vs . 33.73% (17,926). The detection rates of adenomas, AAs, serrated adenomas, and CRCs were 14.58% (7750), 3.09% (1641), 1.23% (653), and 0.59% (313), respectively. Men were statistically significantly associated with higher detection rates than women in adenomas (17.20% [6058/35,226], 95% confidence interval [CI] 16.74-17.53% vs . 9.44% [1692/17,926], 95% CI 8.94-9.79%, P  < 0.001), AAs (3.72% [1309], 95% CI 3.47-3.87% vs . 1.85% [332], 95% CI 1.61-2.00%, P  < 0.001), and serrated adenomas (1.56% [548], 95% CI 1.43-1.69% vs . 0.59% [105], 95% CI 0.47-0.70%, P  < 0.001). The detection rate of AAs in individuals aged 45 to 49 years was 3.17% (270/8510, 95% CI 2.80-3.55%) in men and 1.69% (69/4091, 95% CI 1.12-1.86%) in women, and their NNS was 31.55 (95% CI 28.17-35.71) in men and 67.11 (95% CI 53.76-89.29) in women. The NNS for AAs in men aged 45 to 49 years was close to that in women aged 65 to 69 years (29.07 [95% CI 21.05-46.73]). CONCLUSIONS: The detection rates of adenomas, AAs, and serrated adenomas are high in the asymptomatic population undergoing a physical examination and are associated with gender and age. Our findings will provide important references for effective population-based CRC screening strategies in the future.

Identifying Patients at Risk of Acute Kidney Injury among Patients Receiving Immune Checkpoint Inhibitors: A Machine Learning Approach.

Background: The benefits of immune checkpoint inhibitors (ICPis) in the treatment of patients with malignancies emerged recently, but immune-related adverse events (IRAEs), including acute kidney injury (AKI), cannot be ignored. The present study established and validated an ICPi-AKI prediction model based on machine learning algorithms to achieve early prediction of AKI events and timely intervention adjustment. Methods: We performed a retrospective study based on data from the First Medical Center of the PLA General Hospital. Patients with malignancy who received at least one dose of ICPi between January 2014 and December 2019 were included in the study. The characteristics of available variables were included after case review, and the baseline characteristics and clinical data of ICPi AKI and non-AKI patients were compared. After variable preprocessing, eight machine learning algorithms were used to construct a full variable availability model. Variable simplification models were constructed after screening important variables using the random forest recursive feature elimination method, and the performance of different machine learning methods and two types of modeling strategies were evaluated using multiple indicators. Results: Among the 1616 patients receiving checkpoint inhibitors, the overall incidence of AKI was 6.9% during the total follow-up time. Sixty-eight patients were associated with ICPi treatment after chart review, primarily in AKI stage 1 (70.5%), with a median time from first ICPi administration to AKI of 12.7 (IQR 2 to 56) weeks. The demographic characteristics, comorbidities, and proportions of malignancy types were similar between the ICPi-AKI and non-AKI groups, but there were significant differences in multiple characteristics, such as concomitant medications and laboratory test indicators. For model performance evaluation and comparison, the AUC values of all 38 variable availability models ranged from 0.7204−0.8241, and the AUC values of the simplicity model constructed using 16 significant variables ranged from 0.7528−0.8315. The neural networks model (NNs) and support vector machine (SVM) model had the best performance in the two types of modeling strategies, respectively; however, there was no significant difference in model performance comparison (p > 0.05). In addition, compared with the full variable availability model, the performance of the variable simplicity model was slightly improved. We also found that concomitant medications contributed more to the model prediction performance by screening the optimal feature combination. Conclusion: We successfully developed a machine learning-based ICPi-AKI prediction model and validated the best prediction performance of each machine model. It is reasonable to believe that clinical decision models driven by artificial intelligence can improve AKI prediction in patients with malignancies treated with ICPi. These models can be used to assist clinicians in the early identification of patients at high risk of AKI, support effective prevention and intervention, and ultimately improve the overall benefit of antitumor therapy in the target population.

Incidence, risk factors and prognosis of acute kidney injury in patients treated with immune checkpoint inhibitors: a retrospective study.

Immune checkpoint inhibitors (ICIs) change the prognosis of many cancer patients. With the increasing use of ICIs, immune-related adverse events are occurring, including acute kidney injury (AKI). This study aimed to assess the incidence of AKI during ICI treatment and its risk factors and impact on mortality. Patients treated with ICIs at the First Medical Center of the Chinese PLA General Hospital from January 1, 2014, to December 30, 2019, were consecutively enrolled, and risk factors affecting AKI development in patients treated with ICIs were analyzed using univariate and multivariate logistic regression. Medical record surveys and telephone inquiry were used for follow-up, and Kaplan-Meier survival analysis and Cox regression were used to analyze independent risk factors for death. Among 1615 patients, 114 (7.1%) had AKI. Multivariate logistic regression analysis showed that anemia, Alb < 30 g/L, antibiotic use, diuretic use, NSAID use and proton pump inhibitor use were independent risk factors for AKI development in patients treated with ICIs. Stage 2 or 3 AKI was an independent risk factor for nonrecovery of renal function after AKI onset. Multivariate Cox regression analysis showed that anemia, Alb < 30 g/L, AKI occurrence, and diuretic use were independent risk factors for death in patients treated with ICIs, while high baseline BMI, other tumor types, ACEI/ARB use, and chemotherapy use were protective factors for patient death. AKI occurs in 7.1% of patients treated with ICIs. Anemia, Alb < 30 g/L, and combined medication use are independent risk factors for AKI in patients treated with ICIs. Anemia, Alb < 30 g/L, AKI occurrence, and diuretic use were independent risk factors for death in patients treated with ICIs.

Dynamic Predictive Models With Visualized Machine Learning for Assessing Chondrosarcoma Overall Survival.

Chondrosarcoma is a malignant bone tumor with a low incidence rate. Accurate risk evaluation is crucial for chondrosarcoma treatment. Due to the limited reliability of existing predictive models, we intended to develop a credible predictor for clinical chondrosarcoma based on the Surveillance, Epidemiology, and End Results data and four Chinese medical institutes. Three algorithms (Best Subset Regression, Univariate and Cox regression, and Least Absolute Shrinkage and Selector Operator) were used for the joint training. A nomogram predictor including eight variables-age, sex, grade, T, N, M, surgery, and chemotherapy-is constructed. The predictor provides good performance in discrimination and calibration, with area under the curve ≥0.8 in the receiver operating characteristic curves of both internal and external validations. The predictor especially had very good clinical utility in terms of net benefit to patients at the 3- and 5-year points in both North America and China. A convenient web calculator based on the prediction model is available at https://drwenle029.shinyapps.io/CHSSapp, which is free and open to all clinicians.

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study.

Background: Currently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma. Methods: Clinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers. Results: Eight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients. Conclusions: The clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

External verification and improvement of the Neo-Bioscore staging system in a Chinese cohort.

Background: Accurately predicting outcomes for patients with breast cancer receiving neoadjuvant chemotherapy (NAC) is critical for clinical decisions. Prognostic models applicable to the Chinese population remain limited. The Neo-Bioscore staging system has been utilized as a predictive model for survival of breast cancer patients after NAC. This study aimed to validate the applicability of Neo-Bioscore in a Chinese population and develop an improved staging system based on it to predict prognosis of Chinese patients more accurately. Methods: This study retrospectively collected clinicopathological and survival data in patients receiving NAC from February 2005 to August 2018 in PLA General Hospital. Discrimination, calibration and clinical usefulness were used to assess model performance. Univariate and multivariate analyses assessed relationships between clinicopathological factors and disease-specific survival. For model modification, postoperative pathological staging in the Neo-Bioscore was substituted with the posttreatment pathological tumor (ypT) stage and posttreatment pathological lymph node (ypN) stage. Neo-Bioscore and Modified Neo-Bioscore were compared with the American Joint Committee on Cancer (AJCC) staging system. Results: A total of 436 patients with a median follow-up of 67 months were included. Five-year disease-specific survival (DSS), overall survival, and disease-free survival rates were 88.0%, 87.9%, and 76.8%, respectively. The concordance index (C-index) of the Neo-Bioscore staging system, posttreatment pathological stage (PS), and pretreatment clinical stage (CS) for DSS were 0.78 [95% confidence interval (CI): 0.72-0.83], 0.75 (95% CI: 0.69-0.82), and 0.68 (95% CI: 0.62-0.74), respectively. No significant difference between the Neo-Bioscore and PS was observed in the C-index (P=0.399). ypT and ypN were included in Neo-Bioscore to replace PS and create a modified staging system named MNeo-Bioscore. The C-index for DSS of the MNeo-Bioscore was 0.82 (95% CI: 0.78-0.87), and the calibration curve and decision curve analysis (DCA) curve performed well in internal validation. Conclusions: The Neo-Bioscore staging system provided precise prognostic stratification for Chinese breast cancer patients receiving NAC; ypN and ypT stage may be substituted for PS to add significant prognostic value for Neo-Bioscore.

Laparoscopic-assisted vs open transhiatal gastrectomy for Siewert type II adenocarcinoma of the esophagogastric junction: A retrospective cohort study.

BACKGROUND: The studies of laparoscopic-assisted transhiatal gastrectomy (LTG) in patients with Siewert type II adenocarcinoma of the esophagogastric junction (AEG) are scarce. AIM: To compare the surgical efficiency of LTG with the open transhiatal gastrectomy (OTG) for patients with Siewert type II AEG. METHODS: We retrospectively evaluated a total of 578 patients with Siewert type II AEG who have undergone LTG or OTG at the First Medical Center of the Chinese People's Liberation Army General Hospital from January 2014 to December 2019. The short-term and long-term outcomes were compared between the LTG (n = 382) and OTG (n = 196) groups. RESULTS: Compared with the OTG group, the LTG group had a longer operative time but less blood loss, shorter length of abdominal incision and an increased number of harvested lymph nodes (P < 0.05). Patients in the LTG group were able to eat liquid food, ambulate, expel flatus and discharge sooner than the OTG group (P < 0.05). No significant differences were found in postoperative complications and R0 resection. The 3-year overall survival and disease-free survival performed better in the LTG group compared with that in the OTG group (88.2% vs 79.2%, P = 0.011; 79.7% vs 73.0%, P = 0.002, respectively). In the stratified analysis, both overall survival and disease-free survival were better in the LTG group than those in the OTG group for stage II/III patients (P < 0.05) but not for stage I patients. CONCLUSION: For patients with Siewert type II AEG, LTG is associated with better short-term outcomes and similar oncology safety. In addition, patients with advanced stage AEG may benefit more from LTG in the long-term outcomes.

Identification of a Four-Gene Metabolic Signature to Evaluate the Prognosis of Colon Adenocarcinoma Patients.

Background: Colon adenocarcinoma (COAD) is a highly heterogeneous disease, thus making prognostic predictions uniquely challenging. Metabolic reprogramming is emerging as a novel cancer hallmark that may serve as the basis for more effective prognosis strategies. Methods: The mRNA expression profiles and relevant clinical information of COAD patients were downloaded from public resources. The least absolute shrinkage and selection operator (LASSO) Cox regression model was exploited to establish a prognostic model, which was performed to gain risk scores for multiple genes in The Cancer Genome Atlas (TCGA) COAD patients and validated in GSE39582 cohort. A forest plot and nomogram were constructed to visualize the data. The clinical nomogram was calibrated using a calibration curve coupled with decision curve analysis (DCA). The association between the model genes' expression and six types of infiltrating immunocytes was evaluated. Apoptosis, cell cycle assays and cell transfection experiments were performed. Results: Univariate Cox regression analysis results indicated that ten differentially expressed genes (DEGs) were related with disease-free survival (DFS) (P-value< 0.01). A four-gene signature was developed to classify patients into high- and low-risk groups. And patients with high-risk exhibited obviously lower DFS in the training and validation cohorts (P < 0.05). The risk score was an independent parameter of the multivariate Cox regression analyses of DFS in the training cohort (HR > 1, P-value< 0.001). The same findings for overall survival (OS) were obtained GO enrichment analysis revealed several metabolic pathways with significant DEGs enrichment, G1/S transition of mitotic cell cycle, CD8+ T-cells and B-cells may be significantly associated with COAD in DFS and OS. These findings demonstrate that si-FUT1 inhibited cell migration and facilitated apoptosis in COAD. Conclusion: This research reveals that a novel metabolic gene signature could be used to evaluate the prognosis of COAD, and targeting metabolic pathways may serve as a therapeutic alternative.

Gastroscopic results for the asymptomatic, average-risk population in Northern China: a cross-sectional study of 60,519 adults.

BACKGROUND: Studies on average-risk individuals undergoing gastroscopy screening in China are scarce. OBJECTIVE: To determine and compare the prevalence of lesions found by gastroscopy and the association between sex, age, Helicobacter pylori infection and gastric premalignant lesions. METHODS: Gastroscopy results were analysed for 60,519 individuals enrolled from January 2013 to December 2019. RESULTS: The median age was 49.84 years (SD, 9.47 years) for women and 48.90 years (SD, 8.82 years) for men, and the ratio of females to males was 35.10% (n = 21,240) to 64.90% (n = 39,279). The most common lesions detected by endoscopy were chronic gastritis, reflux oesophagitis, duodenitis and gastric polyps, detected in 24.48%, 10.28%, 3.96% and 3.61%, respectively. Oesophageal cancer and gastric cancer were detected in 0.33% and 0.47% of patients, respectively. The prevalence of chronic gastritis increased with age and was higher in males than in females (26.47% [n = 10396] versus 20.80% [n = 4417], p < .001). The prevalence of gastric ulcers was highest in the elderly group, and the H. pylori infection rate of gastric ulcer patients was 47.28%. The prevalence of gastric polyps was higher in females than in males (5.47% [n = 1161] versus 2.61% [n = 1024], p < .001), and the H. pylori infection rate in inflammatory polyp patients was higher than that in fundic gland polyp patients (28.32% [n = 442] versus 7.29% [n = 29], p < .001). CONCLUSION: The prevalence of upper gastrointestinal endoscopic lesions is high in the asymptomatic population undergoing physical examination and is associated with sex, age, and H. pylori infection.

Ultrasound-Guided Radiofrequency Ablation Versus Surgical Resection for the Treatment of T1bN0M0 Papillary Thyroid Carcinoma in Different Age Groups.

Purpose: We aimed to compare the efficacy and safety of radiofrequency ablation (RFA) to that of surgical resection (SR) in patients with T1bN0M0 papillary thyroid carcinoma (PTC) in different age groups. Methods: Totally, 204 patients with an isolated, solitary, intrathyroidal T1bN0M0 PTC, who underwent either RFA (n=94) or SR (n=110) between April 2014 and December 2019, were retrospectively enrolled and were divided into two subgroups according to age (<45 years, ≧45 years). Patients with pathologically aggressive or advanced lesions were excluded from the study. Tumor progression and procedural complications were the primary and secondary endpoints, respectively. Tumor recurrence in situ, newly discovered tumors, lymph node involvement, or distant metastases indicated tumor progression. Complications included pain, fever, voice change, choking, numbness in the limbs, and cardiac events. Incidence rates of all endpoint events were compared between different age subgroups. Results: There were no significant differences in age, sex, and tumor size between the treatment groups. While the RFA group incurred less cost and experienced significantly shorter operative duration than the SR group, no significant differences were observed in incidences of both tumor progression and complications. Further, subgroup analysis of patients <45 years versus those ≧45 years showed no significant differences in the incidence of tumor progression and complications within or between different treatment groups. Older patients in the SR group incurred higher hospital costs than younger counterparts, but this difference was not observed in the RFA group. Conclusions: Our results indicated that RFA had a similar prognosis as that of SR but was associated with lower overall cost in both young (<45 years) and middle-aged patients (≧45 years) with T1bN0M0 PTC. Therefore, RFA may be an effective and safe alternative to surgery for the treatment of patients with T1bN0M0 PTC.

Trends in Comorbidities and Postoperative Complications of Geriatric Hip Fracture Patients from 2000 to 2019: Results from a Hip Fracture Cohort in a Tertiary Hospital.

OBJECTIVE: To describe the secular trends in comorbidities and postoperative complications of geriatric hip fracture patients from the Chinese People's Liberation Army General Hospital Hip Fracture Cohort between 2000 and 2019. METHODS: We included 2,805 hip fracture patients aged 65 years or older and received surgical treatment from 25 January 2000 to 19 December 2019. Demographic characteristics, comorbidities, postoperative complications, length of hospital stay, and the time to surgery were extracted and examined in each 5-year period based on the admission year, namely 2000-2004, 2005-2009, 2010-2014, and 2015-2019. Categorical data were analyzed by chi-squared or Fisher's exact test, with ordinal data by row mean scores difference test and continuous data by one-way analysis of variance. Trends in comorbidities and postoperative complications were examined by the Cochran-Armitage trend test. RESULTS: The average age of the included population was 79.1 ± 7.3 years (mean ± standard deviation), and 69.1% were female. From 2000 to 2019, the proportion of females increased from 59.8% to 73.0% (P for trend <0.05). Hypertension (51.8%), type 2 diabetes (23.6%), coronary heart disease (20.9%), stroke (18.7%), and arrhythmia (11.2%) were the most prevalent five comorbidities. The proportion of hypertension was 27.0%, 45.4%, 53.0%, and 57.2% in each 5-year period with an increasing trend (P for trend <0.05). The proportion of type 2 diabetes was 9.8%, 22.8%, 23.5%, and 26.0% in each 5-year period (P for trend <0.05). Similar increasing trends were found in myocardial infarction, arrhythmia, and tumor. On the contrary, the proportion of patients with major postoperative complications decreased from 2000 to 2019, with 23.0%, 14.6%, 6.5%, and 5.6% in each 5-year period (P for trend <0.05). For each specific postoperative complication, i.e. pneumonia, cardiovascular event, respiratory failure, and in-hospital death, similar decreasing trends were found (all P for trend <0.05). CONCLUSION: This descriptive analysis sheds light on the fact that the health status of the hip fracture population tends to shift gradually. Improving concepts and practices of clinical interventions may help reduce postoperative complications, whereas challenges in the management of comorbidities increase.

Identification of the Signature Associated With m6A RNA Methylation Regulators and m6A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma.

BACKGROUND: N6-methyladenosine (m6A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m6A RNA methylation regulators and m6A-related genes to understand the prognosis of early lung adenocarcinoma. METHODS: The relevant datasets were utilized to analyze 21 m6A RNA methylation regulators and 5,486 m6A-related genes in m6Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan-Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed. RESULTS: Respectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m6A methylation. CONCLUSION: The findings of this study suggested that associated with m6A RNA methylation regulators and m6A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.

Risk analysis of pulmonary metastasis of chondrosarcoma by establishing and validating a new clinical prediction model: a clinical study based on SEER database.

BACKGROUND: The prognosis of lung metastasis (LM) in patients with chondrosarcoma was poor. The aim of this study was to construct a prognostic nomogram to predict the risk of LM, which was imperative and helpful for clinical diagnosis and treatment. METHODS: Data of all chondrosarcoma patients diagnosed between 2010 and 2016 was queried from the Surveillance, Epidemiology, and End Results (SEER) database. In this retrospective study, a total of 944 patients were enrolled and randomly splitting into training sets (n = 644) and validation cohorts(n = 280) at a ratio of 7:3. Univariate and multivariable logistic regression analyses were performed to identify the prognostic nomogram. The predictive ability of the nomogram model was assessed by calibration plots and receiver operating characteristics (ROCs) curve, while decision curve analysis (DCA) and clinical impact curve (CIC) were applied to measure predictive accuracy and clinical practice. Moreover, the nomogram was validated by the internal cohort. RESULTS: Five independent risk factors including age, sex, marital, tumor size, and lymph node involvement were identified by univariate and multivariable logistic regression. Calibration plots indicated great discrimination power of nomogram, while DCA and CIC presented that the nomogram had great clinical utility. In addition, receiver operating characteristics (ROCs) curve provided a predictive ability in the training sets (AUC = 0.789, 95% confidence interval [CI] 0.789-0.808) and the validation cohorts (AUC = 0.796, 95% confidence interval [CI] 0.744-0.841). CONCLUSION: In our study, the nomogram accurately predicted risk factors of LM in patients with chondrosarcoma, which may guide surgeons and oncologists to optimize individual treatment and make a better clinical decisions. TRIAL REGISTRATION: JOSR-D-20-02045, 29 Dec 2020.

High Expression of CLEC11A Predicts Favorable Prognosis in Acute Myeloid Leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous disease of the hematopoietic system, for which identification of novel molecular markers is potentially important for clinical prognosis and is an urgent need for treatment optimization. METHODS: We selected C-type lectin domain family 11, member A (CLEC11A) for study via several public databases, comparing expression among a variety of tumors and normal samples as well as different organs and tissues. To investigated the relationship between CLEC11A expression and clinical characteristics, we derived an AML cohort from The Cancer Genome Atlas (TCGA); we also investigated the Bloodspot and HemaExplorer databases. The Kaplan-Meier method and log-rank test were used to evaluate the associations between CLEC11A mRNA expression, as well as DNA methylation, and overall survival (OS), event-free survival (EFS), and relapse-free survival (RFS). DNA methylation levels of CLEC11A from our own 28 de novo AML patients were assessed and related to chemotherapeutic outcomes. Bioinformatics analysis of CLEC11A was carried out using public databases. RESULTS: Multiple public databases revealed that CLEC11A expression was higher in leukemia. The TCGA data revealed that high CLEC11A expression was linked with favorable prognosis (OS p-value = 2e-04; EFS p-value = 6e-04), which was validated in GSE6891 (OS p-value = 0; EFS p-value = 0; RFS p-value = 2e-03). Methylation of CLEC11A was negatively associated with CLEC11A expression, and high CLEC11A methylation level group was linked to poorer prognosis (OS p-value = 1e-02; EFS p-value = 2e-02). Meanwhile, CLEC11A hypermethylation was associated with poor induction remission rate and dismal survival. Bioinformatic analysis also showed that CLEC11A was an up-regulated gene in leukemogenesis. CONCLUSION: CLEC11A may be used as a prognostic biomarker, and could do benefit for AML patients by providing precise treatment indications, and its unique gene pattern should aid in further understanding the heterogeneous AML mechanisms.

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma.

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM). Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram. Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group. Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

Cervical cancer prevention-related knowledge and attitudes among female undergraduate students from different ethnic groups within China, a survey-based study.

The purpose of this study was to understand cervical cancer prevention-related knowledge and attitudes among female undergraduate students from different ethnic groups within China. We conducted a survey among ethnically diverse female students from the Minzu University of China, in Beijing in October, 2014. RESULTS: Questionnaires from 493 participants aged from 16 to 26 years were included in the final database. The seven ethnic groups included in the final analysis were Han, Korean, Mongolian, Uyghur, Tibetan, Hui, and Tujia. Compared to the Han Chinese, the members of the other six ethnic groups had lower cervical cancer knowledge levels. The knowledge scores of Mongolian and Korean students were significantly lower than those of the Han Chinese. The willingness to accept cervical cancer prevention efforts also differed across different ethnic groups. After adjusting for age and place of residence, the acceptance of cervical cancer screening among the Tibetan, Uyghur, and Korean groups was significantly lower than among the Han Chinese, with different related decision-making factors in each group. Cervical cancer prevention-related public education is an urgent need in China. Extra consideration of ethnic differences should be taken into account when designing and improving new current cervical cancer prevention programs.