Survival Probability Of Patients Research Articles

Survival probabilities and prognostic factors of pregnancy-associated breast cancer in Malaysian women.

Pregnancy-associated breast cancer (PABC) is a rare cancer. This study aimed to determine the survival probabilities and prognostic factors in patients with PABC. A retrospective cohort study was conducted in two tertiary care hospitals in Kota Bharu. We included all patients with breast cancer who were diagnosed by histopathology while pregnant or within 2 years post-partum from 2001 through 2020. We matched patients with PABC to non-pregnant patients with breast cancer by age and year of diagnosis. The data were analyzed using Cox proportional hazard regression. A total of 35 cases of PABC and 70 non-PABC controls were recruited. The 3-year, 5-year, and 10-year survival probabilities for patients with PABC were 58.6%, 47.54%, and 38.03%, respectively. The patients with PABC had a non-significant difference in survival probabilities compared with non-PABC patients. The significant prognostic factors of PABC were age (adjusted hazard ratio [aHR], 0.91; 95% confidence interval [CI], 0.86-0.96; P=0.001), advanced stage of cancer (aHR, 9.97; 95% CI, 3.96-25.2; P<0.001), and no surgery (aHR, 3.16; 95% CI, 1.01-9.85; P=0.047). Pregnancy was not found to be an independent factor in the prognosis of PABC (aHR, 0.72; 95% CI, 0.39-1.28; P=0.266). Women diagnosed with PABC had similar survival probabilities compared with non-PABC patients. Pregnancy was not an independent prognostic factor for breast cancer. This information can be useful when women with breast cancer are counseled and supported with the option of beginning treatment with pregnancy continuation.

Assessing the prognostic impact of prostatic urethra involvement and developing a nomogram for T1 stage bladder cancer

PurposeTo investigate prognostic values of prostatic urethra involvement (PUI) and construct a prognostic model that estimates the probability of cancer-specific survival for T1 bladder cancer patients.Method and materialsWe investigated the national Surveillance, Epidemiology, and End Results (SEER) database (2004–2015) to get patients diagnosed with T1 bladder cancer. An external validation cohort was obtained from the First Affiliated Hospital of Nanchang University. The Kaplan–Meier method with the log-rank test was applied to assess cancer-specific survival (CSS) and overall survival (OS). Moreover, the propensity score matching (PSM) and multivariable Cox proportional hazard model were performed. All patients were randomly divided into the development cohort and validation group at the ratio of 7:3. The performance of the model was internally validated by calibration curves and the concordance index (C-index).ResultsThe PUI group had a lower survival rate of both CSS and overall survival OS before and after PSM when compared to non-involved patients (All P < 0.05). Multivariate analysis revealed a poor prognosis in the PUI group for cancer-specific mortality (CSM) and all-cause mortality (ACM) analyses before and after PSM (All P < 0.05). Seven variables, including age, surgery, radiotherapy, tumour size, PUI, and marital status, were incorporated in the final nomogram. The C-index in the development cohort was 0.715 (0.711–0.719), while it was 0.672 (0.667–0.677) in the validation group. Calibration plots for 3- and 5-year cancer-specific survival showed good concordance in the development and validation cohorts.ConclusionsPUI was an independent risk factor of ACM and CSM in T1 bladder cancer patients. In addition, a highly discriminative and precise nomogram that predicted the individualized probability of cancer-specific survival for patients with T1 bladder cancer was constructed.

System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma

BackgroundSkin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive.MethodsA total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package “pRRophetic” was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using “immuneeconv” R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells.ResultsBased on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan–Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells.ConclusionIn this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.

Kidney-type glutaminase is a biomarker for the diagnosis and prognosis of hepatocellular carcinoma: a prospective study

PurposeThe pathological diagnosis and prognosis prediction of hepatocellular carcinoma (HCC) is challenging due to the lack of specific biomarkers. This study aimed to validate the diagnostic and prognostic efficiency of Kidney-type glutaminase (GLS1) for HCC in prospective cohorts with a large sample size.MethodsA total of 1140 HCC patients were enrolled in our prospective clinical trials. Control cases included 114 nontumour tissues. The registered clinical trial (ChiCTR-DDT-14,005,102, chictr.org.cn) was referred to for the exact protocol. GLS1 immunohistochemistry was performed on the whole tumour section. The diagnostic and prognostic performances of GLS1 was evaluated by the receiver operating characteristic curve and Cox regression model.ResultsThe sensitivity, specificity, positive predictive value, negative predictive value, Youden index, and area under the curve of GLS1 for the diagnosis of HCC were 0.746, 0.842, 0.979, 0.249, 0.588, and 0.814, respectively, which could be increased to 0.846, 0.886, 0.987,0.366, 0.732, and 0.921 when combined with glypican 3 (GPC3) and alpha-fetoprotein (AFP), indicating better diagnostic performance. Further, we developed a nomogram with GPC3 and GLS1 for identifying HCC which showed good discrimination and calibration. GLS1 expression was also related with age, T stage, TNM stage, Edmondson–Steiner grade, microvascular invasion, Ki67, VEGFR2, GPC3, and AFP expression in HCC. GLS1 expression was negatively correlated with disease-free survival (P < 0.001) probability of patients with HCC.ConclusionsIt was validated that GLS1 was a sensitive and specific biomarker for pathological diagnosis of HCC and had prognostic value, thus having practical value for clinical application.

Abnormal workload-indexed blood pressure response to exercise in patients at risk of or with overt heart failure

Abstract Background Conflicting evidence exists regarding the functional and prognostic significance of hypertensive response to exercise (HRE), commonly defined as peak systolic blood pressure (SBP) &amp;gt;210 mmHg in males and &amp;gt;190 mmHg in females. No study yet has explored the impact of sex-specific, workload-indexed HRE in patients at different stages of the heart failure (HF) spectrum, as defined by the American College of Cardiology/American Heart Association HF staging system. Purpose We evaluated the SBP/Workload slope during exercise across the HF spectrum and investigated the hemodynamic, functional and prognostic correlates of HRE. Methods Fifty-eight healthy controls, 201 subjects at risk of developing HF (HF Stages A-B), and 369 patients with a definite diagnosis of HF (Stage C) were enrolled; among the latter, 143 had preserved (HFpEF) and 226 reduced (HFrEF) left ventricular ejection fraction. All patients underwent a combined cardiopulmonary-exercise stress echocardiography test. For each patient, we recorded at least two BP measurements during exercise: the first within the first minute of exercise and the last at peak effort. The SBP/Workload slope was calculated as the difference in SBP over the corresponding increment in watts (W): (SBPpeak – SBPfirst)/(Wpeak – Wfirst). In each ACC/AHA HF Stage, we divided patients according to SBP/Workload slope tertiles; HRE was defined as the highest sex-specific SBP/Workload slope tertile. A composite of all-cause mortality and hospitalization for cardiovascular reasons was chosen as the primary outcome. Results The median SBP/Workload slope was 0.53 (interquartile range 0.36-0.72) mmHg/W and was steeper in women than in men in all subgroups (p&amp;lt;0.0001). After adjusting for age and sex, SBP/Workload slope was significantly higher in Stages A-B (0.61, 0.47-0.75 mmHg/W) and HFpEF (0.63, 0.42-0.86 mmHg/W) compared to controls (0.43, 0.35-0.57 mmHg/W) and HFrEF (0.47, 0.30-0.63 mmHg/W, Figure 1). In all subgroups, patients with HRE showed significantly lower peak oxygen consumption and peripheral oxygen extraction (Table). After a median follow-up of 16 months, HRE was an independent predictor of adverse outcomes (Hazard Ratio 2.05, 95% confidence interval 1.81-5.18), while rest and peak absolute SBP values did not. Kaplan-Meier analysis confirmed a worse survival probability for patients with HRE in Stages A-B and HFpEF but not in HFrEF. After adjusting for confounding factors, the patients with the highest SBP/Workload slope had a 47% higher risk of the primary outcome than subjects in the lowest tertile (Figure 2). Conclusions Across the HF spectrum, patients with HRE, as evaluated by the SBP/Workload slope, present with peculiar pathophysiological features and impaired functional capacity. This is especially true for those in Stages A-B and patients with HFpEF. In these subgroups, HRE may portend an increased risk of adverse cardiovascular outcomes.SBP/workload slopesKaplan-Meier curves and Hazard Ratios

The mitotic cell cycle-associated nomogram predicts overall survival in lung adenocarcinoma.

This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) associated with mitotic cell cycle. The model will predict the probability of survival at different time points and serve as a reference tool to evaluate the effectiveness of LUAD treatment. A cohort of 442 patients with LUAD from the gene expression omnibus (GEO) database was randomly divided into a training group (n = 299) and a validation group (n = 99). The least absolute shrinkage and selection operator (LASSO)-COX algorithm was used to reduce the number of predictors based on the clinicopathological and RNA sequencing data to establish mutant characteristics that could predict patient survival. Additionally, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) analyses were conducted on the mutant characteristics. The performance of the developed nomogram was evaluated using calibration curves and the C-index. The mutant characteristics had prognostic value for LUAD and acted as an independent prognostic factor. The mutant characteristics profile derived from the LASSO-COX algorithm demonstrated a significant association with overall survival in patients with LUAD. Functional annotation based on the mutant score, its involvement in the phase transition of the mitotic cell cycle, and its regulatory processes. The nomogram, which combined the mutant score with clinical factors associated with prognosis, showed robust accuracy in both the training and validation groups. This study presents the first individualized model that establishes a mutant score for predicting survival in LUAD. This model can be used as a predictive tool for determining 1-, 2-, 3-, and 5-year survival probabilities in patients with LUAD.

The development and experimental validation of hypoxia-related long noncoding RNAs prognostic signature in predicting prognosis and immunotherapy of cutaneous melanoma.

Cutaneous melanoma (CM) is widely acknowledged as a highly aggressive form of malignancy that is associated with a considerable degree of morbidity and poor prognosis. Despite this recognition, the precise role of hypoxia-related long noncoding RNAs (HRLs) in the pathogenesis of CM remains an area of active research. This study sought to elucidate the contribution of HRLs in CM by conducting a thorough screening and extraction of hypoxia-related genes (HRGs). In particular, we conducted univariate and multivariate Cox regression analyses to assess the independence of the prognostic signature of HRLs. Our results demonstrated that a novel risk model could be established based on five prognostic HRLs. Remarkably, patients with low-risk scores exhibited significantly higher overall survival rates compared to their high-risk counterparts, as confirmed by Kaplan-Meier survival analysis. Furthermore, we utilized consensus clustering analysis to categorize CM patients into two distinct subtypes, which revealed marked differences in their prognosis and immune infiltration landscapes. Our nomogram results confirmed that the HRLs prognostic signature served as an independent prognostic indicator, offering an accurate evaluation of the survival probability of CM patients. Notably, our findings from ESTIMATE and ssGSEA analyses highlighted significant disparities in the immune infiltration landscape between low- and high-risk groups of CM patients. Additionally, IPS and TIDE results suggested that CM patients in different risk subtypes may exhibit favorable responses to immunotherapy. Enrichment analysis and GSVA results indicated that immune-related signaling pathways may mediate the role of HRLs in CM. Finally, our tumor mutation burden (TMB) results indicated that patients with low-risk scores had a higher TMB status. In summary, the establishment of a risk model based on HRLs in this study provided an accurate prognostic prediction and correlated with the immune infiltration landscape of CM, thereby providing novel insights for the future clinical management of this disease.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan–Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Clinical Text Reports to Stratify Patients Affected with Myeloid Neoplasms Using Natural Language Processing

Background: The availability of multimodal patient data, such as demographics, clinical, imaging, treatment, quality of life, outcomes and wearables data, as well as genome sequencing, have paved the way for the development of multimodal clinical solutions that introduce personalized or precision medicine. The clinical report is an information layer that contains relevant information about the disease in addition to the patient's point of view. Natural language processing (NLP) is a branch of artificial intelligence (AI) and its pre-trained language models are the key technology for extracting value from this data layer. Aims: This project was conducted by GenoMed4all and Synthema EU consortia, with the aim to: 1) Build an AI language model specific for the hematology domain. 2) Use NLP technology to extract relevant information from clinical reports and perform unsupervised stratification of patients, in order to 3) demonstrate that the clinical report is earlier access to data relative to disease clinical phenotype and biology and provide important information for patient stratification and prediction of clinical outcomes. Methods: To translate text sentences into numerical embeddings, we implemented bidirectional encoder representations from transformers (BERT) framework. To learn text representations and correlations within data, we performed domain-adaptation by fine-tuned pre-trained model on hematological clinical reports of patients with myeloproliferative neoplasms (MPN), myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Patient stratification was performed by HDBSCAN clustering on text embedding encoded by BERT (HematoBERT). Clusters validation was performed by assessing patients' diagnosis and survival probability. Finally, we compared domain-tuned HematoBERT vs pre-trained non-contextualized models. Results: We implemented HematoBERT based on the bert-base-multilingual-uncased version of BERT. Training data were hematological text reports of 1,328 patients. During fine-tuning, texts were tokenized, then we randomly replaced 15% of the tokens with masked tokens, training the model to predict them. We performed stratification using clinical reports from a validation cohort of 360 patients. We identified 7 clusters, defined according to similar words in meaning that were placed in a specific topic. We extracted the most important words and concepts for each cluster (topic) and we summarized them into effective descriptions for each group of patients. Two clusters included MDS patients with excess blasts, and without excess blasts with ring sideroblasts and del5q (n=69, n=115). One cluster included patients with excess blasts and MDS/MPN (n= 33). Two clusters included MPN patients with primary and secondary myelofibrosis, and MPN patients most including subjects affected with polycythemia vera and essential thrombocythemia (n=35, n=46). Two clusters included patients with AML from MDS and therapy-related AML, and patients with de novo AML (n=22, n=42). Clinical validation was performed based on the diagnosis and survival probability of patients assigned to clusters. Patients' diagnoses were compatible with the cluster assignment (Figure 1). Frequency of gene mutations (as assessed by targeted Next-Generation Sequencing) among different clusters reflected the well-known genotypic-phenotypic associations in MDS, MPN and AML. Kaplan-Maier curves indicated significative risk stratification in clusters in terms of survival probability (Figure 2), similar to stratifications performed on clinical and genomic data. Finally, we evaluate the domain adaptation by comparing the model to other pre-trained non-contextualized ones. Pseudo perplexity score (PPS), accuracy and F1 score were calculated to quantify how good the models are when they see new data, predicting the next word given the context of the sentence. HematoBERT obtained high PPS, accuracy and F1 scores, outperforming the other models also trained on generic clinical domains. Conclusion: Domain-adapted language models are able to understand contexts and correlations in documents. HematoBERT can be used to extract relevant features from clinical reports. This data layer is relevant to perform disease stratification of patients based on clinical and genomic information and could be integrated into next-generation multimodal models of personalized medicine.

Metastasis, characteristic, and treatment of breast cancer in young women and older women: A study from the Surveillance, Epidemiology, and End Results registration database.

Younger age is an independent risk factor for breast cancer (BC) prognosis, and BC in young women is often considered more aggressive. BC patients with different age and molecular subtypes have different metastasis patterns and survival. Herein, we aim to explore the metastasis patterns, characteristics and treatment methods of young patients with BC, and to compare them with older patients. Data of young patients (aged ≤40 years old) and older patients (aged >40 years old) with BC were extracted from the Surveillance, Epidemiology, and End Results (SEER) registration database in 2010-2019 in this retrospective cohort study. Univariate and multivariate competing risk models and proportional hazard models were used to explore the association between different metastasis patterns and treatments and BC prognoses in young and older patients. Kaplan-Meier (KM) curves were drawn to reflect the survival probability of patients with BC who have different metastasis patterns. Also, we performed subgroup analysis of different metastasis patterns to explore the association between different treatments and overall survival (OS)/cancer specific survival (CSS) in patients with BC. The evaluation index was hazard ratios (HRs) with 95% confidence intervals (CIs). Totally, 5,984 patients died, and 92.56% of them died from BC. There were respectively 1,089 young patients and 9,105 older patients, and we found some differences of characteristics and metastasis patterns between them. After adjusting for covariates, young patients who had brain metastasis and multiple sites metastasis seemed to have high risk of both lower OS and CSS. Among older patients with BC, brain metastasis, liver metastasis, and multiple sites metastasis were all positively associated with both lower OS and CSS. In young and older patients, those who not receive radiotherapy or surgery, or received non-surgery combined with radiotherapy seemed to have high risk of both lower OS and CSS. Breast-conserving surgery (BCS) and surgery combined with radiotherapy were associated with higher OS and CSS in young patients, while only older patients received surgery combined with radiotherapy had higher OS and CSS. Results of subgroup analysis indicated that for patients with different metastasis patterns, developing a personalized treatment plan is necessary. Characteristics of BC between young patients and older patients were different. Clinicians should focus on different metastasis sites and choose appropriate treatments in patients with different ages, which may improve the prognoses.

Knockdown of PROM2 Enhances Paclitaxel Sensitivity in Endometrial Cancer Cells by Regulating the AKT/FOXO1 Pathway.

Endometrial cancer is a very common and highly lethal reproductive malignant tumour in women. Paclitaxel (PTX) is a usual drug utilized in chemotherapy for endometrial cancer. It has been uncovered that PROM2 participates in the progression of various cancers through playing a promoter. However, the regulatory function of PROM2 in PTX treatment for endometrial cancer remains unclear. The cell viability (IC50) was examined through CCK8 assay. The mRNA and protein expressions of genes were measured through RT-qPCR and western blot. The proliferation was evaluated through colony formation and EdU assays. The cell apoptosis was assessed through flow cytometry. In this work, through bioinformatic analysis on online websites, it is found that the up-regulated expression of PROM2 existed in endometrial cancer. In addition, the survival probability of UCEC patients with high PROM2 expression was worse. This study adopted PTX treatment for obtaining the PTX-resistant cells (HEC-1A/PTX and KLE/PTX). Furthermore, suppression of PROM2 enhanced PTX sensitivity through decreasing IC50 and proliferation in endometrial cancer. Additionally, knockdown of PROM2 facilitated cell apoptosis in HEC-1A/PTX and KLE/PTX cells. Next, we found that silencing of PROM2 retards the AKT/FOXO1 pathway. At last, rescue assays reversed the strengthened PTX sensitivity mediated by PROM2 inhibition after SC79 treatment (AKT activator). Knockdown of PROM2 enhanced PTX sensitivity in endometrial cancer through modulating the AKT/FOXO1 pathway. This study hinted that PROM2 may be a useful therapeutic target for PTX treatment in endometrial cancer.

Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive

Prostate cancer (PCa) patients with lymph node involvement (LNI) constitute a single-risk group with varied prognoses. Existing studies on this group have focused solely on those who underwent prostatectomy (RP), using statistical models to predict prognosis. This study aimed to develop an easily accessible individual survival prediction tool based on multiple machine learning (ML) algorithms to predict survival probability for PCa patients with LNI. A total of 3280 PCa patients with LNI were identified from the Surveillance, Epidemiology, and End Results (SEER) database, covering the years 2000–2019. The primary endpoint was overall survival (OS). Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. Discrimination was evaluated using the time-dependent areas under the receiver operating characteristic curve (time-dependent AUC) and the concordance index (c-index). Calibration was assessed using the time-dependent Brier score (time-dependent BS) and the integrated Brier score (IBS). Moreover, the beeswarm summary plot in SHAP (SHapley Additive exPlanations) was used to display the contribution of variables to the results. The 3280 patients were randomly split into a training cohort (n = 2624) and a validation cohort (n = 656). Nine variables including age at diagnosis, race, marital status, clinical T stage, prostate-specific antigen (PSA) level at diagnosis, Gleason Score (GS), number of positive lymph nodes, radical prostatectomy (RP), and radiotherapy (RT) were used to develop models. The mean time-dependent AUC for GBSA, RSF, and EST was 0.782 (95% confidence interval [CI] 0.779–0.783), 0.779 (95% CI 0.776–0.780), and 0.781 (95% CI 0.778–0.782), respectively, which were higher than the Cox regression model of 0.770 (95% CI 0.769–0.773). Additionally, all models demonstrated almost similar calibration, with low IBS. A web-based prediction tool was developed using the best-performing GBSA, which is accessible at https://pengzihexjtu-pca-n1.streamlit.app/. ML algorithms showed better performance compared with Cox regression and we developed a web-based tool, which may help to guide patient treatment and follow-up.

Downregulation of Rab23 inhibits hepatocellular carcinoma by repressing SHH signaling pathway.

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumors and the third leading cause of cancer-related death worldwide. As an oncogene, Rab23 has been shown to be significantly related to the growth and migration of hepatocellular carcinoma in both in vitro and in vivo studies, but its underlying mechanism remains obscure. In the present study, we examined the effect of inhibiting Rab23 expression on the pathological progression of HCC. The correlation between liver Rab23 gene expression and survival probability in human HCC patients was analyzed using the TCGA database and CPTAC database. Rab23 knockdown hepatocellular carcinoma cell line was generated through lentiviral transduction, then we established a nude HCC xenograft model by subcutaneously implanting the transfected cells. The analysis of gene and protein expression was carried out using Western blot or RT-qPCR, respectively. Flow cytometry analysis was used to detect the level of apoptosis. The expression levels of key proteins involved in the Sonic Hedgehog (SHH) signaling pathway were assessed. The results showed that HCC patients with low levels of hepatic Rab23 mRNA and protein had a better survival tendency than those with higher levels of Rab23. Cell proliferations were reduced and apoptosis levels were increased after Knocking down Rab23 in HCC cell lines. Furthermore, in vivo studies have demonstrated that suppression of the Rab23 gene results in decreased tumor size, proliferation rate, and reduced levels of SHH-related proteins Smoothened and GLI-1. The above results suggest that Rab23 is involved in the pathological progression of HCC as an important regulator of the SHH signaling pathway, which also provides an important research basis for new therapeutic strategies for HCC.

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.

Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention. In this study, we introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision-making process made by machines specifically for medical applications. More specifically, the framework comprises two phases: In the first phase, referred to as the “clinician-guided design” phase, the dataset is preprocessed using explainable AI and domain expert input. To better demonstrate this phase, we prepared a benchmark dataset of carefully curated clinical and biochemical markers based on clinician assessments for survival and kidney injury prediction in COVID-19 patients. This dataset was selected from a patient cohort of 1366 individuals at Stony Brook University. Moreover, we designed and trained a diverse collection of machine learning models, encompassing gradient-based boosting tree architectures and deep transformer architectures, specifically for survival and kidney injury prediction based on the selected markers. In the second phase, called the “explainability-driven design refinement” phase, the proposed framework employs explainability methods to not only gain a deeper understanding of each model’s decision-making process but also to identify the overall impact of individual clinical and biochemical markers for bias identification. In this context, we used the models constructed in the previous phase for the prediction task and analyzed the explainability outcomes alongside a clinician with over 8 years of experience to gain a deeper understanding of the clinical validity of the decisions made. The explainability-driven insights obtained, in conjunction with the associated clinical feedback, are then utilized to guide and refine the training policies and architectural design iteratively. This process aims to enhance not only the prediction performance but also the clinical validity and trustworthiness of the final machine learning models. Employing the proposed explainability-driven framework, we attained 93.55% accuracy in survival prediction and 88.05% accuracy in predicting kidney injury complications. The models have been made available through an open-source platform. Although not a production-ready solution, this study aims to serve as a catalyst for clinical scientists, machine learning researchers, and citizen scientists to develop innovative and trustworthy clinical decision support solutions, ultimately assisting clinicians worldwide in managing pandemic outcomes.

Hypoxia induced deregulation of sphingolipids in colon cancer is a prognostic marker for patient outcome

Sphingolipids are important for the physicochemical properties of cellular membranes and deregulated in tumors. In human colon cancer tissue ceramide synthase (CerS) 4 and CerS5 are reduced which correlates with a reduced survival probability of late-stage colon cancer patients. Both enzymes are reduced after hypoxia in advanced colorectal cancer (CRC) cells (HCT-116, SW620) but not in non-metastatic CRC cells (SW480, Caco-2). Downregulation of CerS4 or CerS5 in advanced CRC cells enhanced tumor formation in nude mice and organoid growth in vitro. This was accompanied by an enhanced proliferation rate and metabolic changes leading to a shift towards the Warburg effect. In contrast, CerS4 or CerS5 depletion in Caco-2 cells reduced tumor growth in vivo. Lipidomic and proteomic analysis of membrane fractions revealed significant changes in tumor-promoting cellular pathways and cellular transporters. This study identifies CerS4 and CerS5 as prognostic markers for advanced colon cancer patients and provides a comprehensive overview about the associated cellular metabolic changes. We propose that the expression level of CerS4 and CerS5 in colon tumors could serve as a basis for decision-making for personalized treatment of advanced colon cancer patients.Trial registration: The study was accredited by the study board of the Deutsche Krebsgesellschaft (Registration No: St-D203, 2017/06/30, retrospectively registered).

Survival analysis and development of a prognostic nomogram for patients with hepatitis B virus-associated hepatocellular carcinoma

Background and aimsHepatitis B virus (HBV) is a common cause of hepatocellular carcinoma (HCC) in China, and this study aimed to identify high-risk factors for overall survival and develop a nomogram prediction model. MethodsIn the present retrospective cohort study, patients with HBV-associated HCC diagnosed from January 2009 to December 2018 were enrolled. Their clinical characteristics and time-to-event information were retrieved from electronic medical records. The zero time was the date of HCC diagnosis, and the endpoint was death or liver transplantation. Multivariable COX proportional hazard regression was used to screen independent risk factors for overall survival; then a nomogram model was developed to predict the survival probability of HCC patients. ResultsA total of 1723 patients were enrolled, with 82.7 % male and a median age of 54.0 years. During a median follow-up time of 41.3 months, 672 cases (39.0 %) died. Age ≥60 years (HR = 1.209), Male (HR = 1.293), ALB <35 g/L (HR = 1.491), AST ≥80 U/L (HR = 1.818); AFP 20–400 ng/mL (HR = 2.284), AFP ≥400 ng/mL (HR = 2.746); LSM 9–22 kPa (HR = 2.266), LSM ≥22 kPa (HR = 4.326); BCLC stage B/C (HR = 4.079) and BCLC stage D (HR = 16.830) were the independent high-risk factors associated with HCC survival. A prognostic nomogram with a consistency index of 0.842 (95 % CI: 0.827–0.858) was developed. The calibration curve for long-term survival rate fitted well. ConclusionsThis study identified independent risk factors affecting the survival of patients with HBV-associated HCC and constructed a predictive nomogram model, which can individually predict the overall survival and has good clinical application value.

Conditional Prostate Cancer-Specific Survival Probabilities in Recurrent Prostate Cancer Patients Receiving Radiation ± Antiandrogen Therapy: Secondary Analysis of a Randomized Clinical Trial

Salvage radiation therapy (SRT) and androgen-deprivation therapy (ADT) are routinely used in patients with elevated prostate-specific antigen (PSA) levels after radical prostatectomy (RP) for prostate cancer (PCa). Although prostate cancer-specific survival (PCSS) following SRT is useful when counseling patients at the time of treatment, the utility of this data from time of diagnosis diminishes over time. Conditional survival (CS) can provide more relevant estimates, especially given long-term PCa survivorship. The primary aim of this study was to analyze CS estimates for recurrent PCa patients undergoing SRT ± ADT. The secondary objective was to determine if factors prognostic of PCSS at diagnosis remain relevant in survivorship. We analyzed data from 760 post-RP patients enrolled in NRG/RTOG 9601 (1998-2003). Eligible patients included men who had undergone prior RP and had pT2/T3 disease without nodal involvement and detectable PSA levels of 0.2-4.0 ng/mL. Patients were randomly assigned to undergo SRT and receive either 24 mo of ADT or placebo. OS was calculated (Kaplan-Meier), cumulative incidence was used to estimate PCSS rates, and prognostic factors associated with PCSS were analyzed by multivariable Cox proportional hazards modeling (MVA). Patients were followed for a median of 13 y. The 5- and 10-y PCSS estimates from diagnosis were 98.52% and 92.7%, respectively. At 1- (n = 755), 3- (n = 727), 5- (n = 680), and 8-y (n = 602) survivorship, chances of PCSS at an additional 5-y were 97.41%, 95.29%, 93.76%, and 91.04%, respectively. On MVA at diagnosis, omission of ADT (HR 2.156, P = 0.0003), increasing age (HR 1.833, P = 0.03), Gleason score (GS) 7 (HR 2.356, P = 0.0057), and GS 8-10 (HR 4.841, P < 0.0001) were associated with PCa-specific mortality. For those who achieved survivorship at 1-, 3-, 5-, and 8-y, all variables remained prognostic of PCa-specific mortality on MVA. Conditional risk of death from PCa for patients treated with SRT increases over time. The initial risk of dying from PCa over a given period, however, is higher than if already having survived a portion of that period. ADT, younger age, and lower GS continue to confer a reduced risk of PCa-specific mortality for long-term survivors. This data can be used to inform survivorship care planning, giving patients more relevant prognostic information during continued surveillance after completion of treatment.

Prehospital predicting factors using a decision tree model for patients with witnessed out-of-hospital cardiac arrest and an initial shockable rhythm

The effect of prehospital factors on favorable neurological outcomes remains unclear in patients with witnessed out-of-hospital cardiac arrest (OHCA) and a shockable rhythm. We developed a decision tree model for these patients by using prehospital factors. Using a nationwide OHCA registry database between 2005 and 2020, we retrospectively analyzed a cohort of 1,930,273 patients, of whom 86,495 with witnessed OHCA and an initial shockable rhythm were included. The primary endpoint was defined as favorable neurological survival (cerebral performance category score of 1 or 2 at 1 month). A decision tree model was developed from randomly selected 77,845 patients (development cohort) and validated in 8650 patients (validation cohort). In the development cohort, the presence of prehospital return of spontaneous circulation was the best predictor of favorable neurological survival, followed by the absence of adrenaline administration and age. The patients were categorized into 9 groups with probabilities of favorable neurological survival ranging from 5.7 to 70.8% (areas under the receiver operating characteristic curve of 0.851 and 0.844 in the development and validation cohorts, respectively). Our model is potentially helpful in stratifying the probability of favorable neurological survival in patients with witnessed OHCA and an initial shockable rhythm.

Establishment of a new prognostic risk model of GNG7 pathway-related molecules in clear cell renal cell carcinoma based on immunomodulators

Clear cell renal cell carcinoma (CCRCC) is a common tumor of the urological system for which surgery is the preferred treatment, but there is a lack of therapeutic options after surgery. This study aims to explore the biological role of GNG7 on CCRCC from a genetic perspective. Differences in mRNA expression and patient survival of GNG7 in patients with CCRCC and healthy patients were analyzed using the TCGA database. It was observed that GNG7 gene expression was downregulated in CCRCC tissue compared with healthy tissue, and high GNG7 predicted better prognosis for patients, and GNG7 also showed strong variability in clinical and TMN staging. The immune relevance of GNG7 and related genes was explored using renal cancer data from CCLE and TISIDB database. It was verified that the risk score constructed by 7 GNG7-related regulators might be used as an independent prognostic risk factor for CCRCC. A CCRCC prognostic model that involved 7 immune genes was further established to predict the survival probabilities of patients. At last, the GEO database and immunochemical tissue staining were used to validate GNG7 expression in CCRCC. Our study proposed a novel panel of genes to predict CCRCC OS based on GNG7-related immune genes, which may help to accurately predict the prognosis of CCRCC patients and make better clinical decisions for individual treatment.