Investigating the disparities among drug categories in drug-induced dermatomyositis: A systematic review.

Drug-induced dermatomyositis (DIDM) is a rare and underestimated variant of dermatomyositis (DM) characterized by muscle damage and skin rash and related to certain drug exposure. The spectrum of drugs causing DIDM has evolved over time, originally implicating hydroxyurea, penicillamine, and statins as causative agents. Tumor necrosis factor α inhibitors and immune checkpoint inhibitors have also been associated with such conditions. To bridge the gap between current literature and clinical practice, and therefore guide clinicians, we conducted a comprehensive review of English literature from Pubmed, EMBASE, and MEDLINE. Our analysis included demographic data, clinical features, laboratory findings, therapeutic outcomes, and extant research pertaining to the probable pathogenesis of DIDM induced by various drugs. Furthermore, we categorized the drugs involved in DIDM cases into biologics and traditional agents for subsequent statistical analysis. Over time, there has been a gradual accumulation of reported DIDM cases. A total of 69 published DIDM cases were documented in our study, among which 33 should be attributed to biologics and the remaining 36 to traditional drugs. Interestingly, 41 of all DIDM cases had a previous history of malignancies. Additionally, DIDM cases exhibited similar cutaneous and muscular manifestations to classic DM, with the exception of cases induced by hydroxyurea, which did not entail muscle damage. Positive antinuclear antibodies and anti-TIF1-γ autoantibodies have been predominantly observed in biologics-induced cases, while positive anti-TIF1-γ antibodies were merely reported in the cases that were primarily diagnosed with malignant diseases and exposed to ICIs afterwards. Anti-TIF1-γ antibodies may potentially serve as a red flag in the identification of co-existing malignant diseases in DM patients. We also provided a comprehensive summary and exploration of potential mechanisms lying behind drug-induced dermatomyositis. In conclusion, our review consolidates the current literature on DIDM, highlighting the evolving spectrum of medications and elucidating the differences in clinical manifestations, laboratory findings, and underlying mechanisms.

Interruption of KLF5 acetylation promotes PTEN-deficient prostate cancer progression by reprogramming cancer-associated fibroblasts.

PTEN inactivation is prevalent in human prostate cancer and causes high-grade adenocarcinoma with a long latency. Cancer associated fibroblasts (CAFs) play a pivotal role in tumor progression, but it remains elusive whether and how PTEN-deficient prostate cancers reprogram CAFs to overcome the barriers for tumor progression. Herein, we report that PTEN deficiency induces KLF5 acetylation; and interruption of KLF5 acetylation orchestrates intricate interactions between cancer cells and CAFs that enhance FGFR1 signaling and promote tumor growth. Deacetylated KLF5 promotes tumor cells to secrete TNF-α, which stimulates inflammatory CAFs to release FGF9. CX3CR1 inhibition blocks FGFR1 activation triggered by FGF9 and sensitizes PTEN-deficient prostate cancer to AKT inhibitor capivasertib. This study reveals the role of KLF5 acetylation in reprogramming CAFs and provides a rational for combined therapies using inhibitors of AKT and CX3CR1.

Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality.

Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.

Pancreatic Cancer-Derived Exosomal miR-Let-7b-5p Stimulates Insulin Resistance in Skeletal Muscle Cells Through RNF20/STAT3/FOXO1 Axis Regulation.

Background: Cancers trigger systemic metabolic disorders usually associated with glucose intolerance, which is an initially apparent phenomenon. One of the features of pancreatic cancer (PC) metabolic reprogramming is the crosstalk between PC and peripheral tissues (skeletal muscle and adipose tissues), emphasized by insulin resistance (IR). Our previous study reported that mice pancreatic cancer-derived exosomes could induce skeletal muscle cells (C2C12) IR, and exosomal microRNAs (miRNAs) may exert an important effect. However, the underlying mechanism remains to be further elucidated. Methods: qPCR was used to determine the expression of let-7b-5p in normal pancreatic islet cells and PC cells. Exosomes were purified from PC cell culture medium by ultracentrifugation. The role let-7b-5p on IR-mediated by PC cells-derived exosomes was asses by Oil Red O staining using miRNA inhibitor. Western blot assay was performed to examine the expression of IR-related genes and the activation of signaling pathways. A Luciferase experiment was applied to confirm how let-7b-5p regulated the expression of RNF20. IP/WB analysis further determined whether RNF20 promoted STAT3 ubiquitination. Rescue experiment using RNF20 overexpression plasmid was performed to confirm the role of RNF20 on IR-mediated using PC cell-derived exosomes in C2C12 myotube cells. Results: miRNA-let-7b-5p was identified as the key exosomal miRNA, which could promote the IR in C2C12 myotube cells supported the lipid accumulation, the activation of STAT3/FOXO1 axis, and the decreased expression of IRS-1 and GLUT4. RNF20, an E3 ubiquitin ligase, was confirmed as the target gene of let-7b-5p and was found to improve IR by downregulating STAT3 protein expression via ubiquitination-mediated protein degradation. The ectopic expression of RNF20 could effectively attenuate the IR mediated by the pancreatic cancer-derived exosomes in C2C12 myotube cells. Conclusion: Our data suggest that exosomal miRNA-let-7b-5p may promote IR in C2C12 myotube cells by targeting RNF20 to activate the STAT3/FOXO1 axis.

Impact of radiation therapy and alpha-fetoprotein level on survival outcomes for patients with hepatocellular carcinoma: A population-based study.

BACKGROUND: The use of radiation therapy (RT) in hepatocellular carcinoma (HCC) remains a matter for debate. Recently published research indicate that advanced RT techniques may improve survival in patients with HCC. This study aimed to evaluate this hypothesis in a large-scale retrospective cohort. The effect of alpha-fetoprotein (AFP) was taken into account because of its important role in the prognosis of HCC. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for adults patients diagnosed 2010-2019 with HCC (≥ 18 years). The study population was divided into four groups: Non-radiation & AFP-positive (reference), Non-radiation & AF-negative, Radiation & AFP-positive, Radiation & AFP-negative. Distant metastasis (DM) was used as a stratification factor. Differences in 5-year overall survival (OS) of the four groups were assessed using the Kaplan-Meier method. Univariate and multivariable Cox proportional hazards model were used to estimate unadjusted and adjusted hazard ratios (HR). RESULTS: A total of 34,656 patients were eligible for this analysis, including 21,084 (60.8%), 8,449 (24.4%), 3,810 (11.0%) and 1,313 (3.8%) in the Non-radiation & AFP-positive, Non-radiation & AF-negative, Radiation & AFP-positive and Radiation & AFP-negative groups, respectively. Median OSs of the four groups were 3, 4, 5 and 11 months in the DM cohort, and 12, 28, 15, and 28 months in the Non-DM cohort. Patients in the Radiation & AFP - group had the best OS and patients in the Non-radiation & AFP + group had the worst OS (adjusted HR [95% confidence interval (CI)]: 0.497 [0.399-0.619] in the DM cohort, and 0.405 [0.372-0.441] in the Non-DM cohort). Radiation & AFP + also showed improved survival compared with the reference group (adjusted HR [95%CI]: 0.725 [0.657-0.801] in the DM cohort, and 0.630 [0.600-0.661] in the Non-DM cohort). CONCLUSIONS: This population-based cohort study confirmed a significant improvement in overall survival with radiation therapy in HCC. AFP-negative patients benefit the most from RT. Superior OS of radiation therapy and AFP-negative status persisted even in patients with complex metastasis patterns. Our data suggest that radiation may provide an alternative modality for unresectable HCC.

Malignancy in dermatomyositis: a mono-centric retrospective study of 134 patients in China and a potential predictive model.

Objectives: To describe the demographics and phenotypes of malignancies-associated dermatomyositis (MADM) in east China and pinpoint potential factors indicative of malignancies in patients with dermatomyositis and establish a predictive model. Methods: We retrospectively analyzed clinical data from 134 patients with adult-onset dermatomyositis hospitalized between January 2019 and May 2022 in one comprehensive hospital. Clinical data including disease course, initial symptoms and signs, and demographic information were retrieved from the Electronic Medical Records System. Other parameters including myositis-specific autoantibodies profiles, ferritin, sedimentation, etc. were all referable. Multivariable multinomial logistic regression was employed to simulate a model to predict cancer risks. Receiver operating characteristic curve was adopted to evaluate the potency of the model. Results: 134 patients with adult-onset dermatomyositis were aptly enrolled in this study based on inclusive and exclusive criteria: 12 (8.96%) with malignancies, 57 (42.53%) with aberrant tumor biomarkers but no malignancies, 65 (48.51%) with neither malignancies nor abnormal tumor biomarkers. Senior diagnostic age, higher LDH, higher ferritin, positive anti-TIF1γ and anti-Mi2 rather than anti-NXP2 autoantibodies were positive indicators of malignancies. Additionally, neither initial complaints nor signs were found to be correlated to a tendency towards malignancies. Digestive system, nasopharyngeal, and lung malignancies were mostly documented in east China. One multivariable multinomial logistic regression model was established to predict the phenotypes of dermatomyositis on the basis of potential malignancies and the overall sensitivity and specificity was satisfactory. Conclusion: Positivity of anti-TIF1γ and anti-Mi2 autoantibodies are highly indicative of malignancies while the role of anti-NXP2 autoantibody in MADM in the Chinese population remains unclear. The phenotypes of malignancies can be predicted through the model and the predictive power is sufficient. More attention should be paid to malignancies screening in patients with aberrant tumor biomarkers but no malignancies, particularly digestive system, nasopharyngeal, and lung malignancies in patients with dermatomyositis but without malignancies.

Prediction of risk and overall survival of pancreatic cancer from blood soluble immune checkpoint-related proteins.

Background: Immune checkpoint inhibition holds promise as a novel treatment for pancreatic ductal adenocarcinoma (PDAC). The clinical significance of soluble immune checkpoint (ICK) related proteins have not yet fully explored in PDAC. Methods: We comprehensively profiled 14 soluble ICK-related proteins in plasma in 70 PDAC patients and 70 matched healthy controls. Epidemiological data of all subjects were obtained through structured interviews, and patients' clinical data were retrieved from electronical health records. We evaluated the associations between the biomarkers with the risk of PDAC using unconditional multivariate logistic regression. Consensus clustering (k-means algorithm) with significant biomarkers was performed to identify immune subtypes in PDAC patients. Prediction models for overall survival (OS) in PDAC patients were developed using multivariate Cox proportional hazards regression. Harrell's concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve and calibration curve were utilized to evaluate performance of prediction models. Gene expressions of the identified ICK-related proteins in tumors from TCGA were analyzed to provide insight into underlying mechanisms. Results: Soluble BTLA, CD28, CD137, GITR and LAG-3 were significantly upregulated in PDAC patients (all q < 0.05), and elevation of each of them was correlated with PDAC increased risk (all p < 0.05). PDAC patients were classified into soluble immune-high and soluble immune-low subtypes, using these 5 biomarkers. Patients in soluble immune-high subtype had significantly poorer OS than those in soluble immune-low subtype (log-rank p = 9.7E-03). The model with clinical variables and soluble immune subtypes had excellent predictive power (C-index = 0.809) for the OS of PDAC patients. Furthermore, the immune subtypes identified with corresponding genes' expression in PDAC tumor samples in TCGA showed an opposite correlation with OS to that of immune subtypes based on blood soluble ICK-related proteins (log-rank p =0.02). The immune-high subtype tumors displayed higher cytolytic activity (CYT) score than immune-low subtype tumors (p < 2E-16). Conclusion: Five soluble ICK-related proteins were identified to be significantly associated with the risk and prognosis of PDAC. Patients who were classified as soluble immune-low subtype based on these biomarkers had better overall survival than those of the soluble immune-high subtype.

Pathologic complete response of hepatoid adenocarcinoma of the stomach after chemo-immunotherapy: A rare case report and literature review.

Background: Hepatoid adenocarcinoma of the stomach (HAS) is a highly malignant subtype of gastric carcinoma with specific clinicopathological features and extremely poor prognosis. We present an exceedingly rare case of complete response after chemo-immunotherapy. Case Description: A 48-year-old woman with highly elevated serum alpha-fetoprotein (AFP) level was found to have HAS verified by pathological examination based on gastroscopy. Computed tomography scan was done and TNM staging of the tumor was T4aN3aMx. Programmed cell death ligand-1 (PD-L1) immunohistochemistry was performed, revealing a negative PD-L1 expression. Chemo-immunotherapy including oxaliplatin plus S-1 and PD-1 inhibitor terelizumab was given to this patient for 2 months until the serum AFP level decreased from 748.5 to 12.9 ng/mL and the tumor shrank. D2 radical gastrectomy was then performed and histopathology of the resected specimen revealed that the cancerous cells had disappeared. Pathologic complete response (pCR) was achieved and no evidence of recurrence has been found after 1 year of follow-up. Conclusions: We, for the first time, reported an HAS patient with negative PD-L1 expression who achieved pCR from the combined chemotherapy and immunotherapy. Although no consensus has been reached regarding the therapy, it might provide a potential effective management strategy for HAS patient.

Anti-PD-1-based immunotherapy plus lenvatinib to treat advanced gallbladder cancer in the elderly: a case series and review of current literature.

BACKGROUND: Gallbladder cancer (GBC) is a rare malignant tumour of the bile duct. Due to the lack of typical clinical manifestations in the early stage, it is basically at an advanced stage when discovered. Radical resection remains the only curative therapy for patients with GBC. The resection rate is relatively low due to tumour invasion and metastasis, and the overall prognosis is poor. For most patients with unresectable lesions, chemotherapy has been the only recommended treatment for decades. Immunotherapy combined with TKIs (tyrosine kinase inhibitors) was proven to be effective in patients with hepatocellular carcinoma and cholangiocarcinoma. Some physicians have attempted to apply immunotherapy and TKIs combined with traditional chemotherapy in patients with advanced GBC. However, the outcomes were not clear because limited cases were reported. CASE PRESENTATION: We present a case series of four elderly patients with advanced GBC who received tislelizumab and lenvatinib combined with chemotherapy. All four patients responded to this treatment approach. Tumour responses were better in Patient 1 (TMB-H, MSS), Patient 2 (low TMB, MSS), and Patient 3 (low TMB, MSI-H) than in Patient 4 (low TMB, MSS), in whom metastasis occurred during the later stage of treatment. CONCLUSION: The combination of tislelizumab and lenvatinib may be a promising treatment for patients with advanced GBC. The efficacy and safety need further confirmation.

SDHA/B reduction promotes hepatocellular carcinoma by facilitating the deNEDDylation of cullin1 and stabilizing YAP/TAZ.

BACKGROUND AND AIMS: Succinate dehydrogenase enzyme (SDH) is frequently diminished in samples from patients with hepatocellular carcinoma (HCC), and SDH reduction is associated with elevated succinate level and poor prognosis in patients with HCC. However, the underlying mechanisms of how impaired SDH activity promotes HCC remain unclear. APPROACH AND RESULTS: In this study, we observed remarkable downregulations of SDH subunits A and B (SDHA/B) in chronic liver injury-induced murine HCC models and patient samples. Subsequent RNA sequencing, hematoxylin and eosin staining, and immunohistochemistry analyses of HCC samples revealed that Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) were significantly upregulated in HCC, with their levels inversely correlating with that of SDHA/B. YAP/TAZ stability was greatly enhanced in SDHA/B-depleted HCC cells along with accumulation of succinate. Further mechanistic analyses demonstrated that impaired activity of SDHA/B resulted in succinate accumulation, which facilitated the deNEDDylation of cullin1 and therefore disrupted the E3 ubiquitin ligase SCF ß-TrCP complex, consequently leading to YAP/TAZ stabilization and activation in HCC cells. The accelerated in vitro cell proliferation and in vivo tumor growth caused by SDHA/B reduction or succinate exposure were largely dependent on the aberrant activation of YAP/TAZ. CONCLUSIONS: Our study demonstrated that SDHA/B reduction promotes HCC proliferation by preventing the proteasomal degradation of YAP/TAZ through modulating cullin1 NEDDylation, thus binding SDH-deficient HCC cells to YAP/TAZ pathway and rendering these cells vulnerable to YAP/TAZ inhibition. Our findings warrant further investigation on the therapeutic effects of targeting YAP/TAZ in patients with HCC displaying reduced SDHA/B or elevated succinate levels.

Establishment of a lncRNA-Based Prognostic Gene Signature Associated With Altered Immune Responses in HCC.

Hepatocellular carcinoma (HCC) is a common malignancy with higher mortality, and means are urgently needed to improve the prognosis. T cell exclusion (TCE) plays a pivotal role in immune evasion, and lncRNAs represent a large group of tumor development and progression modulators. Using the TCGA HCC dataset (n=374), we identified 2752 differentially expressed and 702 TCE-associated lncRNAs, of which 336 were in both groups. As identified using the univariate Cox regression analysis, those associated with overall survival (OS) were subjected to the LASSO-COX regression analysis to develop a prognosis signature. The model, which consisted of 11 lncRNAs and was named 11LNCPS for 11-lncRNA prognosis signature, was validated and performed better than two previous models. In addition to OS and TCE, higher 11LNCPS scores had a significant correlation with reduced infiltrations of CD8+ T cells and dendritic cells (DCs) and decreased infiltrations of Th1, Th2, and pro B cells. As expected, these infiltration alterations were significantly associated with worse OS in HCC. Analysis of published data indicates that HCCs with higher 11LNCPS scores were transcriptomically similar to those that responded better to PDL1 inhibitor. Of the 11LNCPS lncRNAs, LINC01134 and AC116025.2 seem more crucial, as their upregulations affected more immune cell types' infiltrations and were significantly associated with TCE, worse OS, and compromised immune responses in HCC. LncRNAs in the 11LNCPS impacted many cancer-associated biological processes and signaling pathways, particularly those involved in immune function and metabolism. The 11LNCPS should be useful for predicting prognosis and immune responses in HCC.

Efficacy and safety of FOLFIRINOX as second-line chemotherapy for advanced pancreatic cancer after gemcitabine-based therapy: A systematic review and meta-analysis.

OBJECTIVE: To undertake a meta-analysis of the treatment effects of different second-line chemotherapy regimens compared with FOLFIRINOX (FOL [folinic acid], F [fluorouracil], IRIN [irinotecan], OX [oxaliplatin]) after failure of gemcitabine-based first-line therapy in patients with pancreatic cancer. METHODS: This meta-analysis searched electronic databases, including Embase®, Medline, PubMed® and the Cochrane library, for eligible studies that reported the use of FOLFIRINOX and other drug regimens as second-line chemotherapy after failure of gemcitabine-based chemotherapy. Pooled analyses for progression-free survival (PFS), overall survival (OS), objective response rate (ORR), disease control rate (DCR) and grade 3/4 treatment-emergent adverse events (TRAEs) were undertaken. RESULTS: The analysis included six studies with a total of 858 patients. Compared with the three other second-line regimens, FOLFIRINOX had a significantly longer PFS (hazard ratio [HR] 0.68, 95% confidence interval [CI] 0.52, 0.89) and OS (HR 0.71, 95% CI 0.59, 0.86); and a significantly better ORR (HR 0.43, 95% CI 0.23, 0.80) and DCR (HR 0.71, 95% CI 0.58, 0.88). However, grade 3/4 adverse events were more frequently reported in patients administered FOLFIRINOX compared with the other three regimens. CONCLUSION: FOLFIRINOX is recommended as a second-line chemotherapy regimen for patients with pancreatic cancer that have failed on gemcitabine-based first-line therapy.Research Registry number: reviewregistry1300.

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography.

BACKGROUND: The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters. METHODS: Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk. FINDINGS: The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set. INTERPRETATION: This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies. FUNDING: This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

The Cardiac Glycoside Deslanoside Exerts Anticancer Activity in Prostate Cancer Cells by Modulating Multiple Signaling Pathways.

Prostate cancer (PCa) is a leading cause of cancer-related deaths among men worldwide, and novel therapies for advanced PCa are urgently needed. Cardiac glycosides represent an attractive group of candidates for anticancer repurposing, but the cardiac glycoside deslanoside has not been tested for potential anticancer activity so far. We found that deslanoside effectively inhibited colony formation in vitro and tumor growth in nude mice of PCa cell lines 22Rv1, PC-3, and DU 145. Such an anticancer activity was mediated by both the cell cycle arrest at G2/M and the induction of apoptosis, as demonstrated by different functional assays and the expression status of regulatory proteins of cell cycle and apoptosis in cultured cells. Moreover, deslanoside suppressed the invasion and migration of PCa cell lines. Genome-wide expression profiling and bioinformatic analyses revealed that 130 genes were either upregulated or downregulated by deslanoside in both 22Rv1 and PC-3 cell lines. These genes enriched multiple cellular processes, such as response to steroid hormones, regulation of lipid metabolism, epithelial cell proliferation and its regulation, and negative regulation of cell migration. They also enriched multiple signaling pathways, such as necroptosis, MAPK, NOD-like receptor, and focal adhesion. Survival analyses of the 130 genes in the TCGA PCa database revealed that 10 of the deslanoside-downregulated genes (ITG2B, CNIH2, FBF1, PABPC1L, MMP11, DUSP9, TMEM121, SOX18, CMPK2, and MAMDC4) inversely correlated, while one deslanoside-upregulated gene (RASD1) positively correlated, with disease-free survival in PCa patients. In addition, one deslanoside-downregulated gene (ENG) inversely correlated, while three upregulated genes (JUN, MXD1, and AQP3) positively correlated with overall survival in PCa patients. Some of the 15 genes have not been implicated in cancer before. These findings provide another candidate for repurposing cardiac glycosides for anticancer drugs. They also suggest that a diverse range of molecular events underlie deslanoside's anticancer activity in PCa cells.

Whether early stage pancreatic ductal adenocarcinoma patients could benefit from the post-operation chemotherapy regimens: a SEER-based propensity score matching study.

To investigate whether chemotherapy could prolong the postoperative survival time in patients with early stages pancreatic ductal adenocarcinoma (PDAC). A total of 5280 stage ⅠA -ⅡB PDAC patients diagnosed from 2010 to 2015 were selected from surveillance，epidemiology，and end results (SEER) database. Propensity score matching (PSM) analysis was adopted to reduce the baseline differences between the groups. Univariate survival analysis was conducted with the Kaplan-Meier method. Multivariate survival analysis was performed with the Cox proportional hazards model. Univariate and multivariate survival analyses showed that age, differentiation, stage, chemotherapy were independent risk factors for the survival of PDAC patients. After PSM, it is found that adjuvant chemotherapy could prolong the median overall survival time (mOS) for stage ⅠB, ⅡA and ⅡB patients. However, for stage ⅠA patients, there were no significant differences in 3-year survival rate and mOS between patients with chemotherapy (=283) and without chemotherapy (=229) (57.4% vs 55.6%, vs all >0.05). Further analyses show that among 101 patients with well differentiated PDAC and 294 patients with moderately differentiated PDAC, there were no significant differences in survival rate and mOS between patients with and without chemotherapy (all >0.05). Among 117 patients with low-differentiated + undifferentiated PDAC, 3-year survival rate and mOS in patients with chemotherapy were significantly better than those without chemotherapy (48.5% vs 34.1%, vs all <0.05). Chemotherapy regimen used currently is not beneficial for patients with moderately and well differentiated stage ⅠA PDAC, but it is an independent prognostic factor for low-differentiated + undifferentiated PDAC patients.

Multi-institutional development and external validation of machine learning-based models to predict relapse risk of pancreatic ductal adenocarcinoma after radical resection.

BACKGROUND: Surgical resection is the only potentially curative treatment for pancreatic ductal adenocarcinoma (PDAC) and the survival of patients after radical resection is closely related to relapse. We aimed to develop models to predict the risk of relapse using machine learning methods based on multiple clinical parameters. METHODS: Data were collected and analysed of 262 PDAC patients who underwent radical resection at 3 institutions between 2013 and 2017, with 183 from one institution as a training set, 79 from the other 2 institution as a validation set. We developed and compared several predictive models to predict 1- and 2-year relapse risk using machine learning approaches. RESULTS: Machine learning techniques were superior to conventional regression-based analyses in predicting risk of relapse of PDAC after radical resection. Among them, the random forest (RF) outperformed other methods in the training set. The highest accuracy and area under the receiver operating characteristic curve (AUROC) for predicting 1-year relapse risk with RF were 78.4% and 0.834, respectively, and for 2-year relapse risk were 95.1% and 0.998. However, the support vector machine (SVM) model showed better performance than the others for predicting 1-year relapse risk in the validation set. And the k neighbor algorithm (KNN) model achieved the highest accuracy and AUROC for predicting 2-year relapse risk. CONCLUSIONS: By machine learning, this study has developed and validated comprehensive models integrating clinicopathological characteristics to predict the relapse risk of PDAC after radical resection which will guide the development of personalized surveillance programs after surgery.

Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

BACKGROUND: Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. METHODS: In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models. RESULTS: A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k-nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector-machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed. CONCLUSION: The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

The Effect of Indoor Daylight Levels on Hospital Costs and Length of Stay of Patients Admitted to General Surgery.

BACKGROUND: Indoor daylight levels can directly affect the physical and psychological state of people. However, the effect of indoor daylight levels on the clinical recovery process of the patient remains controversial. This study was to evaluate the effect of indoor daylight levels on hospital costs and the average length of stay (LOS) of a large patient population in general surgery wards. METHODS: Data were collected retrospectively and analyzed of patients in the Second Affiliated Hospital of Zhejiang University, School of Medicine between January 2015 and August 2020. We measured daylight levels in the patient rooms of general surgery and assessed their association with the total hospital costs and LOS of the patients. RESULTS: A total of 2,998 patients were included in this study with 1,478 each assigned to two daylight level groups after matching. Overall comparison of hospital total costs and LOS among patients according to daylight levels did not show a significant difference. Subgroup analysis showed when exposed to higher intensity of indoor daylight, illiterate patients had lower total hospital costs (CNY ¥13070.0 vs. ¥15210.3, p = 0.018) and shorter LOS (7 vs. 10 days, p = 0.011) as compared to those exposed to a lower intensity. CONCLUSIONS: Indoor daylight levels were not associated with the hospital costs and LOS of patients in the wards of general surgery, except for those who were illiterate. It might be essential to design guidelines for medical staff and healthcare facilities to enhance the indoor environmental benefits of daylight for some specific populations.

ZFHX3 Promotes the Proliferation and Tumor Growth of ER-Positive Breast Cancer Cells Likely by Enhancing Stem-Like Features and MYC and TBX3 Transcription.

Breast cancer is a common malignancy, but the understanding of its cellular and molecular mechanisms is limited. ZFHX3, a transcription factor with many homeodomains and zinc fingers, suppresses prostatic carcinogenesis but promotes tumor growth of liver cancer cells. ZFHX3 regulates mammary epithelial cells' proliferation and differentiation by interacting with estrogen and progesterone receptors, potent breast cancer regulators. However, whether ZFHX3 plays a role in breast carcinogenesis is unknown. Here, we found that ZFHX3 promoted the proliferation and tumor growth of breast cancer cells in culture and nude mice; and higher expression of ZFHX3 in human breast cancer specimens was associated with poorer prognosis. The knockdown of ZFHX3 in ZFHX3-high MCF-7 cells decreased, and ZFHX3 overexpression in ZFHX3-low T-47D cells increased the proportion of breast cancer stem cells (BCSCs) defined by mammosphere formation and the expression of CD44, CD24, and/or aldehyde dehydrogenase 1. Among several transcription factors that have been implicated in BCSCs, MYC and TBX3 were transcriptionally activated by ZFHX3 via promoter binding, as demonstrated by luciferase-reporter and ChIP assays. These findings suggest that ZFHX3 promotes breast cancer cells' proliferation and tumor growth likely by enhancing BCSC features and upregulating MYC, TBX3, and others.