Survival Analysis Of Patients Research Articles

Novel systems biology experimental pipeline reveals matairesinol's antimetastatic potential in prostate cancer: an integrated approach of network pharmacology, bioinformatics, and experimental validation.

Matairesinol (MAT), a plant lignan renowned for its anticancer properties in hormone-sensitive cancers like breast and prostate cancers, presents a promising yet underexplored avenue in the treatment of metastatic prostate cancer (mPC). To elucidate its specific therapeutic targets and mechanisms, our study adopted an integrative approach, amalgamating network pharmacology (NP), bioinformatics, GeneMANIA-based functional association (GMFA), and experimental validation. By mining online databases, we identified 27 common targets of mPC and MAT, constructing a MAT-mPC protein-protein interaction network via STRING and pinpointing 11 hub targets such as EGFR, AKT1, ERBB2, MET, IGF1, CASP3, HSP90AA1, HIF1A, MMP2, HGF, and MMP9 with CytoHuba. Utilizing DAVID, Gene Ontology (GO) analysis highlighted metastasis-related processes such as epithelial-mesenchymal transition, positive regulation of cell migration, and key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including cancer, prostate cancer, PI3K-Akt, and MAPK signaling, while the web resources such as UALCAN and GEPIA2 affirmed the clinical significance of the top 11 hub targets in mPC patient survival analysis and gene expression patterns. Our innovative GMFA enrichment method further enriched network pharmacology findings. Molecular docking analyses demonstrated substantial interactions between MAT and 11 hub targets. Simulation studies confirmed the stable interactions of MAT with selected targets. Experimental validation in PC3 cells, employing quantitative real-time reverse-transcription PCR and various cell-based assays, corroborated MAT's antimetastatic effects on mPC. Thus, this exhaustive NP analysis, complemented by GMFA, molecular docking, molecular dynamics simulations, and experimental validations, underscores MAT's multifaceted role in targeting mPC through diverse therapeutic avenues. Nevertheless, comprehensive in vitro validation is imperative to solidify these findings.

Identification of ASMTL-AS1 and LINC02604 lncRNAs as novel biomarkers for diagnosis of colorectal cancer

PurposeColorectal cancer is one of the major leading causes of death worldwide, and available treatments for advanced colorectal cancer are not successful. Therefore, early detection of colorectal cancer is essential to improve patient survival, and biomarkers are potential tools to achieve this goal. Considering the key role of lncRNAs in cancers, the aim of this study is to identify lncRNAs involved in colorectal cancer as new potential prognosis biomarkers for CRC.MethodsIn this observational study, gene expression data obtained from the TCGA database were analyzed, Identification of differentially expressed mRNAs, miRNAs, and lncRNAs was performed, and ceRNA network was drawn. Also, survival analysis of patients was performed in order to identify potential biomarkers related to the diagnosis and prognosis of colon cancer. After confirming the results using the GSE39582 dataset, the expression of target lncRNAs in colorectal tumor tissues was also investigated to confirm the bioinformatic data.ResultsAnalysis of the TCGA data showed that the expression of three lncRNAs—SNHG7, ASMTL-AS1, and LINC02604—that had the highest interaction with other miRNAs and mRNAs identified based on the ceRNA network was increased in colorectal cancer. Also, based on the ceRNA network, three microRNAs, hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423-5p, and eight mRNAs, including CPA4, MSI2, RRM2, IGF2BP1, ONECUT2, HMGA1, SOX4, and SRM, were associated with all three mentioned lncRNAs, the expression of microRNAs was decreased and the expression of mRNAs was increased. By enrichment analysis, it was found that the target lncRNAs are involved in the processes of cell proliferation, apoptosis, and metastasis, indicating their importance in the development and malignancy of colorectal cancer. Furthermore, Kaplan–Meier analysis showed a significant increase in mortality in patients with higher expression levels of these lncRNAs. Analysis of the GSE39582 dataset, and real-time RT-PCR analysis, confirmed our bioinformatic results. Also, ROC analysis showed that SNHG7 was a relatively good promising biomarker (AUC = 0.73, p value = 0.02), while ASMTL-AS1 (AUC = 0.92, p value < 0.0001) and LINC02604 (AUC = 1.00, p value < 0.0001) emerged as excellent diagnostic biomarkers in colorectal cancer.ConclusionIt seems that increased expression of lncRNAs ASMTL-AS1 and LINC02604 can serve as molecular biomarkers for CRC, possibly through the sponge hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423 5p, which increases target mRNAs, which are effective in the carcinogenesis process.

Identification and model construction of survival-associated proteins for pancreatic cancer based on deep learning

Pancreatic cancer (PC) is a malignancy typified by its insidious onset, rapid progression, limited resectability, poor treatment response, and exceedingly dismal prognosis. The transition from precursor lesions to infiltrating malignant tumors in pancreatic cancer is concomitant with the accrual of genetic mutations. The elucidation of proteins linked to the prognosis of pancreatic cancer holds paramount importance in the realm of pancreatic cancer management. Herein, we introduce DeepPCSA, a model tailored for the screening of target proteins and the prediction of survival time, employing both conventional methodologies and deep learning techniques for patient survival analysis. This framework leverages the LASSOCOX regression approach on differentially expressed genes (DEGs) to discern pivotal genes, succeeded by the development of a prognostic model employing convolutional and fully connected layers to prognosticate patient survival duration. Furthermore, we account for covariates such as age, gender, and other pertinent factors for independent prognostic scrutiny, affirming the autonomy of our model as a prognostic determinant. Employing unsupervised clustering methodologies, we delineated two molecular subtypes delineated by disparate biological processes. Ultimately, via drug sensitivity analysis, we delineated the interrelation between survival duration and pharmaceuticals, substantiating the necessity for tailored drug interventions catering to patients of varying risk strata.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

ObjectivesLung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis. Materials and methodsWe propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost. ResultsThe proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes. ConclusionIn summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Analysis of cancer-specific survival in patients with metastatic colorectal cancer: A evidence-based medicine study.

Metastatic colorectal cancer (mCRC) is a common malignancy whose treatment has been a clinical challenge. Cancer-specific survival (CSS) plays a crucial role in assessing patient prognosis and treatment outcomes. However, there is still limited research on the factors affecting CSS in mCRC patients and their correlation. To predict CSS, we developed a new nomogram model and risk grading system to classify risk levels in patients with mCRC. Data were extracted from the United States Surveillance, Epidemiology, and End Results database from 2018 to 2023. All eligible patients were randomly divided into a training cohort and a validation cohort. The Cox proportional hazards model was used to investigate the independent risk factors for CSS. A new nomogram model was developed to predict CSS and was evaluated through internal and external validation. A multivariate Cox proportional risk model was used to identify independent risk factors for CSS. Then, new CSS columns were developed based on these factors. The consistency index (C-index) of the histogram was 0.718 (95%CI: 0.712-0.725), and that of the validation cohort was 0.722 (95%CI: 0.711-0.732), indicating good discrimination ability and better performance than tumor-node-metastasis staging (C-index: 0.712-0.732). For the training set, 0.533, 95%CI: 0.525-0.540; for the verification set, 0.524, 95%CI: 0.513-0.535. The calibration map and clinical decision curve showed good agreement and good potential clinical validity. The risk grading system divided all patients into three groups, and the Kaplan-Meier curve showed good stratification and differentiation of CSS between different groups. The median CSS times in the low-risk, medium-risk, and high-risk groups were 36 months (95%CI: 34.987-37.013), 18 months (95%CI: 17.273-18.727), and 5 months (95%CI: 4.503-5.497), respectively. Our study developed a new nomogram model to predict CSS in patients with synchronous mCRC. In addition, the risk-grading system helps to accurately assess patient prognosis and guide treatment.

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

Survival Analysis of Lung Cancer Patients at Dr. Saiful Anwar Hospital

Background: Comprising 14% of all cancer, lung cancer is the main cause of cancer-related mortality worldwide. Previous studies showed various findings regarding the survival rate of lung cancer, and factors significantly correlated with survival rate in lung cancer patients. Aim: This research aims to identify the profile and survival rate of lung cancer patients receiving chemotherapy in Saiful Anwar General Hospital from 2018 to 2020. Methods: This cohort retrospective study used medical records of patients diagnosed with lung cancer who underwent chemotherapy from 2018 to 2020. Data analysis was done using the Mantel Cox log rank test. Survival data shown as progression free survival (PFS) and overall survival (OS) were shown using Kaplan-Meier curves. Results: Subjects were largely male (73,2%). Adenocarcinoma is the most often type of lung cancer found (59,8%) with wild type EGFR mutation being the most prevalent (20,6%). Carboplatin – Paclitaxel is the most widely used chemotherapy regimen (54,6%). Survival analysis showed a significant correlation between age, OS, and PFS (p &lt; 0,05) Conclusion: Various factors such as gender, cancer cell type, EGFR mutation, and chemotherapy regimen were not found to be related to survival rate in lung cancer patients. A significant correlation between age and survival rate showed potential in utilizing age as a predictor of survival rate in lung cancer patients.

Survival analysis of diabetes mellitus and cardiovascular patients with COVID-19: a secondary data analysis

Background: Patients with diabetes mellitus (DM) and cardiovascular disorders who suffer from COVID-19 may have an increased risk of death. Objective: This study aims to analyze the time of death and influencing factors in patients with DM and cardiovascular disorders with COVID-19. Methods: We used a retrospective observational cohort study using medical records of COVID-19 patients treated at Dr Soeradji and Penembahan Senopati hospitals from March 2020 to June 2022. There were 2,959 participants: patients without comorbidities, with DM, and with cardiovascular problems. We extracted sociodemographic and clinical data on patient characteristics using medical records. Data analysis used Kaplan-Meier and Cox regression analysis to estimate survival probability and investigate predictors of death with a 5% significance level. Results: The median survival time was highest in the group without comorbidities (70.00) and lowest in the DM+others group (21.75). Years of treatment, age, presence of comorbidities, and type of hospital were related to the survival rate of COVID-19 patients (p&lt;0.05). Diabetes mellitus and cardiovascular system disorders are significantly associated with survival of COVID-19 patients (p&lt;0.001). There were significant differences between patients without cardiovascular disorders and patients with cardiovascular disorders (Non-Hypertension, Hypertension, and Hypertension + Others) adjusted by year, gender, age, and hospital type (p&lt;0.001). There were significant differences between patients without DM and patients with DM (DM only, DM+Hypertension, and DM+Others) adjusted by year, gender, age, and hospital type(p&lt;0.001). Conclusion: Years of treatment, age, gender, comorbid DM, and cardiovascular problems are associated with the survival rate of COVID-19 patients. Older age, DM patients who have comorbidities other than hypertension, and patients with cardiovascular issues other than hypertension show a greater risk of death than other groups. Key words: COVID-19, Diabetes Mellitus, Hypertension, Cardiovascular Diseases, Survival Rate.

Prognostic role of CRABP2 in lung cancer: a meta-analysis

BackgroundThe prognostic value of cellular retinoic acid-binding protein 2 (CRABP2), in lung cancer patients remains to be uncertained. Therefore, our research attempted to assess the relationship between CRABP2 and survival analysis in lung cancer patients through meta-analysis.MethodRelated literature retrieved from Cochrane Library, Ovid, Embase, PubMed, the CNKI, and the Web of Science. The latest update of the search was May 1, 2023. The outcome indicators included as effective measures in the study were hazard ratio (HR), and 95% confidence interval (CI). The Stata 12.0 software was used to analyze the data.ResultsA total of4 studies were finally enrolled in our meta-analysis. The increased plasma level of CRABP2 predicted poor OS in lung cancer patient with a combined HR of 1.14 (95% CI: 1.00–1.30), and were not associated with poor PFS with combined HR: 1.15% CI: 0.63–2.09) in lung cancer patients.ConclusionsOur meta-analysis found the increased plasma level of CRABP2 was associated with poor OS independently in NSCLC patients. The plasma CRABP2 level may be an indicator of biological aggressiveness of the tumor. Our research was promising regarding the feasibility and utility of plasma CRABP2 as a novel prognostic biomarker in NSCLC, and the findings warrant further investigation.

Survival analysis for patients with metachronous contralateral breast cancer: Insights from a retrospective study.

Continued advances in the diagnosis and treatment of breast cancer (BC) have led to an increase in the number of long-term BC survivors and an increase in the incidence of metachronous BC in the contralateral breast. Therefore, it is important to understand the factors that influence the development of metachronous BC; however, the impact of the laterality of the initial ipsilateral (I)BC as a risk factor for the development of metachronous contralateral (MC)BC has not been extensively investigated. The present study included 17,082 female patients with stage 0-3 IBC from the prospectively maintained Korean Breast Cancer Registry from 1989-2013 and divided them into two groups: Patients with MCBC (n=88) and those without MCBC (n=16,994). Risk factors that present at the initial BC diagnosis that could significantly influence the development of MCBC were screened for and risks were evaluated using the Fine-Gray subdistribution hazard model. Significant differences in baseline characteristics between MCBC and non-MCBC groups were demonstrated. Patients aged <40 years, those with histological and nuclear grade 3 tumors, and those with the triple-negative BC subtype were significantly more prevalent in the MCBC group than in the non-MCBC group. Additionally, the cumulative incidence of MCBC increased over time, with a notable increase from 0.1% in year 1 to 1.6% in year 10. Survival analysis revealed no significant differences in overall or BC-specific survival between the two groups. Key predictive factors identified for MCBC included an age of <40 years at initial diagnosis, a negative progesterone receptor status, and a Ki-67 score of >14%. Overall, the present study revealed several factors associated with MCBC and emphasized the need for long-term monitoring of BC survivors, considering these newly identified risk factors.

Cardiovascular and Cerebrovascular Morbidity in Patients with Urolithiasis: An Epidemiological Approach Based on Hospitalization Burden Data from 1997 to 2021.

Background: Patients with kidney stones (KSFs) are known to have a heightened risk of coronary heart disease (CHD) or stroke. The objective of the present study was to describe the natural history of these complications through the longitudinal analysis of the hospitalizations due to kidney stones in Spain from 1997 to 2021. Methods: A retrospective longitudinal observational study was developed based on nationwide hospitalization data (minimum basic data base). Three different analyses were carried out. In the first step, the prevalence of coronary or cerebrovascular events in kidney stone hospitalizations was compared with the hospitalization burden of CHD or strokes related to the general population. In the second step, a survival analysis of the kidney stones-hospitalized patients using the Kaplan-Meier method was conducted. In the third step, a Cox regression was used to assess the influence of the classical comorbidities in the development of the lithiasic patients-cardiovascular disease. Results: Kidney stone-hospitalized patients exhibit a significantly higher risk of CHD (OR = 14.8 CI95%: 14.7-14.9) and stroke (OR = 6.7 CI95%: 6.6-6.8) compared to the general population across in all age groups, although they had less cardiovascular risk factors. A total of 9352 KSFs (1.5%) developed a coronary event within an average time of 78.8 months. A total of 2120 KSFs (0.33%) suffered a stroke in an average time of 71.1 months. Diabetes, hypertension, hyperlipidemia, and being overweight were identified as risk factors for developing CHD and stroke using a univariate and multivariate analysis. Conclusions: Our study confirms previous studies in which kidney stones must be considered as a risk factor for developing CHD or cerebrovascular disease. Preventive strategies should target patients with kidney stones and classical risk cardiovascular factors to mitigate modifiable conditions associated with cardiovascular diseases.

Bayesian Parametric Survival Analysis of Patients With Non-Hodgkin’s Lymphoma in Yazd, Iran, During 2009-2019

Background and aims: Non-Hodgkin’s lymphoma (NHL) is one of two main types of lymphoma. This present study aimed to investigate the survival rate in patients with NHL. Methods: This analytical survival-type study examined 372 patients with NHL referred to Shahid Sadoughi Hospital in Yazd in 2009-2019. The data were extracted from patients’ files. STATA/17 software, the Kaplan-Meier method, and Bayesian parametric survival analysis were used for data analysis. Results: The overall survival rate and the mean overall survival time were 65% and 129±17.94 months, respectively. According to the Kaplan-Meier method, the survival rates of 1, 2, 3, 5, and 10 years were 86%, 80%, 76%, 69.5%, and 52%. The results revealed that men, people over 70 years old, patients with the NHL.T-cell subtype, patients with involvement in the chest, inguinal, and abdominal-pelvic regions, and patients in stage III or IV of the disease had a shorter survival time. In addition, survival time was shorter in patients with disease recurrence at the primary site. The variables of gender (hazard ratio [HR]=0.65; 95% confidence interval [CI]: 0.44‒0.93), type of treatment (HR=3.43; 95% CI: 1.58‒6.16), stage of disease (HR=2.87; 95% CI: 2.09‒3.57), and involvement site (HR=1.45:95%; CI: 1.03‒1.99) had a significant effect on survival time using the Bayesian parametric regression model. Conclusion: If patients are identified at an early stage, they have the best chance of survival. In chronic lymphocytic leukemia (CLL) patients, the best survival is achieved without treatment. At the same time, in other types of cancer, chemotherapy offers the best survival.

Survival analysis in rectal cancer patients after lateral lymph node dissection: Exploring the necessity of nCRT for suspected lateral lymph node metastasis

BackgroundIt remains unclear whether combining neoadjuvant chemoradiotherapy (nCRT) with lateral lymph node dissection (LLND) improves survival in patients with rectal cancer suspected of lateral lymph node metastasis (LLNM) compared with LLND alone. MethodsA retrospective analysis of clinical data from rectal cancer patients from January 2017 to November 2022 was conducted. Univariate and multivariate Cox regression analyses were performed on patient clinical data, with a focus on identifying potential risk factors that affect disease-free survival (DFS) and overall survival (OS). Patients were divided into nCRT+LLND and LLND groups. K‒M survival curves and log-rank tests were used to analyze the survival outcomes of the different groups. ResultsThe study included 92 patients. Univariate Cox regression analysis revealed that intraoperative bleeding, pathological LLNM and histological type were associated with DFS. When these factors were included in multivariate Cox regression, pathological LLNM was identified as an independent risk factor for reduced DFS. There were no statistically significant differences between the nCRT+LLND and LLND groups when comparing survival curves for both DFS and OS. ConclusionIn rectal cancer patients with suspected LLNM, TME+LLND without nCRT may be a viable, yet to be validated, therapeutic strategy.

Analysis of lncRNA-miRNA-mRNA interactions identified a novel biomarker LINC00657 to improve prognosis prediction of papillary thyroid carcinoma

BackgroundPapillary thyroid cancer (PTC) is the most common type of thyroid cancer. Identification of novel biomarkers can potentially help explore the underlying molecular mechanisms of PTC. Long non-coding RNAs (lncRNAs) are involved in cancer development. However, understanding the role of lncRNA in PTC remains challenging. MethodsBased on the competitive endogenous RNA (ceRNA) theory, we constructed a comprehensive PTC-related lncRNA-miRNA-mRNA network using data from The Cancer Genome Atlas. To evaluate the prognostic power, we performed survival analysis for patients with PTC with low and high lncRNA expression levels, and examined the relationship between lncRNA and immune-related functions. ResultsWe identified a hub node, long intergenic non-coding RNA, LINC00657, as a novel prognostic biomarker in PTC. LINC00657 was differentially expressed between tumor and adjacent normal samples. Low LINC00657 expression levels was significantly associated with better survival outcome. Our functional analyses showed that LINC00657 was related with infiltration of CD8+ T cell and macrophage; immune check point molecules; and immune metagenes such as IgG, LCK, MHC_I/II and etc. These results suggest that LINC00657 is an immune-related biomarker with potential clinical applicability. Additionally, cancer-related signaling pathway and high frequency of gene BRAF mutation were found in PTC samples with high LINC00657 expression level, which were consistent with previous findings. ConclusionLINC00657 is an immune-related biomarker that can potentially improve prognosis prediction in PTC. Our study provided new treatment target of PTC in clinical practice and offered the novel insights in elucidating the functional role of lncRNAs.

Disulfidptosis-related lncRNAs signature predicting prognosis and immunotherapy effect in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a prevalent malignant tumor worldwide, with high incidence and mortality rates. However, there is still a lack of specific and sensitive biomarkers for its early diagnosis and targeted treatment. Disulfidptosis is a newly identified mode of cell death that is characteristic of disulfide stress. Therefore, exploring the correlation between disulfidptosis-related long non-coding RNAs (DRGs-lncRNAs) and patient prognosis can provide new molecular targets for LUAD patients. The study analysed the transcriptome data and clinical data of LUAD patients in The Cancer Genome Atlas (TCGA) database, gene co-expression, and univariate Cox regression methods were used to screen for DRGs-lncRNAs related to prognosis. The risk score model of lncRNA was established by univariate and multivariate Cox regression models. TIMER, CIBERSORT, CIBERSORT-ABS, and other methods were used to analyze immune infiltration and further evaluate immune function analysis, immune checkpoints, and drug sensitivity. Real-time polymerase chain reaction (RT-PCR) was performed to detect the expression of DRGs-lncRNAs in LUAD cell lines. A total of 108 lncRNAs significantly associated with disulfidptosis were identified. A prognostic model was constructed by screening 10 lncRNAs with independent prognostic significance through single-factor Cox regression analysis, LASSO regression analysis, and multiple-factor Cox regression analysis. Survival analysis of patients through the prognostic model showed that there were obvious survival differences between the high- and low-risk groups. The risk score of the prognostic model can be used as an independent prognostic factor independent of other clinical traits, and the risk score increases with stage. Further analysis showed that the prognostic model was also different from tumor immune cell infiltration, immune function, and immune checkpoint genes in the high- and low-risk groups. Chemotherapy drug susceptibility analysis showed that high-risk patients were more sensitive to Paclitaxel, 5-Fluorouracil, Gefitinib, Docetaxel, Cytarabine, and Cisplatin. Additionally, RT-PCR analysis demonstrated differential expression of DRGs-lncRNAs between LUAD cell lines and the human bronchial epithelial cell line. The prognostic model of DRGs-lncRNAs constructed in this study has certain accuracy and reliability in predicting the survival prognosis of LUAD patients, and provides clues for the interaction between disulfidptosis and LUAD immunotherapy.

Survival analysis of adult visceral leishmaniasis patients admitted to Metema Hospital, Metema, Ethiopia: a hospital-based cross-sectional study.

Visceral leishmaniasis (VL) is a neglected tropical disease that mostly affects the working class and impoverished segments of society, having a significant negative effect on the economic development of the affected nation. While anti-leishmanial medications lower mortality among VL patients, patients may still die or require more time to recover while receiving treatment. In this regard, there are limited studies in Ethiopia. This study aims to determine the time to recovery and its associated predictors among adult VL patients at Metema Hospital, Metema, Ethiopia. A hospital-based cross-sectional study was employed and the data were collected from patient's charts from September 2017 to September 2021. Data were entered and analysed using EpiData version 3.1, Stata version 14.2 and R version 3.4.0 statistical software. Kaplan-Meier survival curves and logrank tests were used to compare the survival time. The Cox proportional hazards model assumption and model fitness were checked and used to identify statistical association predictors in VL patients. The Cox proportional hazards model was fitted. The overall medium recovery time was 7d (minimum 4, maximum 14). The variables of nasal bleeding (adjusted hazard ratio [aHR] 0.44 [95% confidence interval {CI} 0.19 to 0.89]), no comorbidity (aHR 2.29 [95% CI 1.27 to 4.11]), relapse of VL (aHR 0.33 [95% CI 0.15 to 0.75]), low parasite load (aHR 2.58 [95% CI 1.48 to 4.51]) and ambulatory (aHR 3.26 [95% CI 2.45 to 6.53]) were significantly associated with time to recovery in VL patients. Patients with comorbidities, nasal bleeding, relapse of VL, bedridden and high parasite load should be treated and monitored carefully to recover quickly from their illness.

Survival Analysis of Ovarian Cancer Patients in Yazd City, Central part of Iran, 1999-2018

Background: Ovarian Cancer (OC) as a common gynecologic cancer according to mortality rate has the seventh rank among women in the world. This study aimed to identify risk factors associated with OC survival in Yazd, Iran. Methods: In this observational retrospective cohort study, the medical records of 150 patients who were definitively diagnosed with OC from 1999 to 2018 were investigated. The Kaplan–Meier estimator and the Cox proportional hazard model with hazard ratio and the log-rank test were used for data analysis. All statistical analyses were done in R 4.0.5, package survival. Results: 60.77% (91 people) of patients were under 60 years old. 32.7% (49 people) were in stage III of the disease. 62% (93 people) did not have ascites, 67.3% (101 people) had disease-free survival more than 65 months. 45 (30%) of 150 patients were dead. Median survival time was 96 months (95% CI∶57.20 to 134.79), one, three, five-, and ten-year survival rates were 83,73,55 and 33 months; respectively. Log-rank test results showed there was a significant difference between age, stage, ascites, disease-free survival, and Treatment method, CA125 after and before treatment (p &lt; 0.05). Cox proportional hazard model result showed ascites (HRadj = 3.89, 95% CI: 1.35 to 11.15, P = 0.01) and DFS (HRadj = 23.52, 95 % CI: 4.21to128.33, p = &lt; 0.001) as significant covariates. Conclusion: The results of our study showed that disease-free survival and ascites are the main risk factors for OC and paying attention to them will be effective in increasing patient survival.

Analysis of Prognostic Factors and Cancer-Specific Survival in Patients with Undifferentiated and Dedifferentiated Endometrial Carcinoma Undergoing Various Postoperative Adjuvant Therapies

Analysis of Prognostic Factors and Cancer-Specific Survival in Patients with Undifferentiated and Dedifferentiated Endometrial Carcinoma Undergoing Various Postoperative Adjuvant Therapies

Targeting USP7 to induce ubiquitination degradation of oncogenic ATAD2 and effects on cholangiocarcinoma growth.

e16215 Background: Cholangiocarcinoma (CCA) is a highly fatal disease due to limited therapeutic targets. Deubiquitinase USP7 is an oncogene and has been suggested to be deeply involved in regulating tumor initiation and progression, but its abundance and role in CCA still remain unclear. Methods: USP7 mRNA expression was performed using TCGA and GEO database. Survival analysis in CCA patients with high- and low-USP7 mRNA expression was performed using NODE database. CCK8, colony formation, flow cytometry and shRNA interference assay were used to evaluate the biological function of USP7-ATAD2 axis on CCA cancer cells in vitro. 4D label-free quantification proteomics was used to analyze the targets and networks regulated by USP7. The molecular mechanism of USP7 and ATAD2 in CCA was determined by conducting Western blot, co-immunoprecipitation, IHC and quantitative real-time PCR (qPCR) analyses. The CCA PDX model was used to evaluate the efficacy of targeting USP7 in the treatment of cholangiocarcinoma. Results: Here, we found USP7 is highly expressed in CAA, and is associated with poor outcomes of CCA patients. We show that USP7 depletion induces S phase arrest, and suppresses CCA tumor cells proliferation. Next, we provide a comprehensive understanding of targets and networks affected by USP7 depletion by performing a 4D label-free quantification proteomics analysis. We show that ATAD2 is a USP7 target, and USP7 interacts with and deubiquitinates ATAD2 to stabilize ATAD2 protein level. Ectopic expression of ATAD2 in USP7-depleted cells rescues the cell proliferation phenotype. Further, pharmacological blocking the activity of USP7 by P5091 inhibits tumor growth in CCA patient-derived xenograft (PDX) mouse model. Conclusions: This study uncovers the role of USP7-ATAD2 axis in regulating tumor growth, and targeting USP7 may provide a novel therapeutic strategy for CCA.

Survival analysis and life expectancy of pediatric patients with spinal muscular atrophy in Thailand

BackgroundSurvival data for Thai patients with 5q spinal muscular atrophy (SMA), the leading cause of infant mortality worldwide, are lacking. ObjectiveThis study aimed to determine the survival rates and life expectancies of pediatric patients with SMA types 1, 2, and 3. MethodsWe conducted a retrospective cohort analysis of genetically confirmed 5q SMA patients aged 0–18 years who were treated between 1999 and 2021 at the pediatric neuromuscular clinic of Siriraj Hospital, Bangkok, Thailand. Mortality data were sourced from the Civil Registration Office. ResultsThe study included 113 patients: 37 with SMA type 1, 53 with type 2, and 23 with type 3. Life expectancy varied significantly by SMA type: 2.2 years for type 1, 11 years for type 2, and 16.5 years for type 3. The median survival times for SMA type 1 and 2 were 1.9 and 19 years, respectively. In SMA type 2, early onset (<1 year) correlated with a shorter median survival than later onset (≥1 year) (log-rank test P = 0.009). Early onset SMA type 2 had a median survival time of 15.9 years, while 75 % of those with later onset SMA type 2 survived until the age of 19 years. Cox proportional hazards analysis revealed that each month's delay in disease onset reduced the annual mortality risk by 17 % for type 1 patients and by 20 % for type 2 patients. Compared with female patients, male patients with type 2 disease had a 12-fold increased mortality risk. ConclusionsAge at onset is a significant predictor of survival and life expectancy in patients with SMA types 1 and 2. These insights are crucial for genetic counseling and prognostic discussions in clinical settings.