BACKGROUND: Small cell lung cancer (SCLC) is the most aggressive type of lung cancer. The overall survival has not improved significantly over the last decades because no major therapeutic breakthroughs have been achieved for over 15 years.
METHODS: We analyzed a genome-wide loss-of-function screening database to identify vulnerabilities in SCLC for the development of urgently needed novel therapies.
RESULTS: We identified SKP2 (encoding S-phase kinase-associated protein 2) and CKS1B (encoding CDC28 protein kinase regulatory subunit 1B) as the two most essential genes in that order in SCLC. Notably, SKP2 and CKS1B comprise the p27 binding pocket of the E3 ubiquitin ligase SCFSKP2 complex. Immunohistochemistry on tissue microarrays revealed that SKP2 was expressed in >95% of samples at substantially higher levels than that observed for commonly used neuroendocrine markers. As expected, SCLC cell lines were sensitive to SKP2 inhibition. Furthermore, SKP2 or CKS1B knockdown induced apoptosis in RB1 mutant cells, whereas it induced senescence in RB1 wild-type cells.
CONCLUSIONS: Although the mechanism underlying SKP2 knockdown-induced growth inhibition differs between RB1-wild-type and -mutant SCLC, SKP2 can be considered a novel therapeutic target for patients with SCLC regardless of the RB1 mutation status. Our findings indicate that SKP2 is a potential novel clinical diagnostic marker and therapeutic target in SCLC.

BACKGROUND: Nasopharyngeal carcinoma (NPC), arising from nasopharyngeal epithelium is caused by Epstein-Barr virus (EBV). It is common in South China, South East Asia and North East India. The aim and objectives of this study were to determine the prevalence of EBV in formalin-fixed paraffin-embedded (FFPE) tissue sections of clinically suspected NPC patients, correlate the results of polymerase chain reaction (PCR) with histopathology findings, and to determine the utility of tissue EBV DNA as a diagnostic bio-marker.
METHODS: 31 FFPE tissue samples were collected from clinically suspected NPC patients from April 2018-December 2019. Histopathological diagnosis was done by examination of Hematoxylin and Eosin stained slides. Presence of EBV was detected by EBNA-1 PCR. IHC was performed using EBV Latent Membrane Protein 1.
RESULTS: Of the 31 clinically suspected NPC cases, 15 (48.4 %) were histopathological confirmed NPC. Of these15, 13 (86.6 %) were non-keratinising undifferentiated NPC, and one each were keratinising NPC and non-keratinising differentiated NPC respectively. EBV EBNA1 PCR was positive in 35.5 % (11/31) of clinically suspected NPC cases. Of the 11 PCR positive cases, 9 (81.8 %) were histopathological confirmed NPC. Of the 31 clinically suspected NPC cases, IHC was indicated in 23 biopsies. Of which, 12 (52.2 %) were positive for LMP1 in the abnormal cells. Of the 12 IHC positive samples, 10 were NPC cases.
CONCLUSIONS: EBV DNA as an indicator towards NPC among clinically suspected cases had a sensitivity of 60 % and specificity of 87.5 %. In this study, addition of EBV DNA detection by PCR from FFPE tissue sections could confirm EBV association in 20 % of cases where it was not detected by EBV LMP1 IHC, thus helped in increasing the detection of EBV positivity in NPC cases. Early diagnosis of NPC will improve the cure rate and hence reduce the morbidity and mortality rates.

PAX8 plays a role in development of the thyroid, kidney, and the Wolffian and Mullerian tract. In surgical pathology, PAX8 immunohistochemistry is used to determine tumors of renal and ovarian origin, but data on its expression in other tumors are conflicting. To evaluate PAX8 expression in normal and tumor tissues, a tissue microarray containing 17,386 samples from 149 different tumor types and 608 samples of 76 different normal tissue types was analyzed by immunohistochemistry. PAX8 results were compared with previously collected data on cadherin 16 (CDH16). PAX8 positivity was found in 40 different tumor types. The highest rate of PAX8 positivity was found in thyroidal neoplasms of follicular origin (98.6-100%), gynecological carcinomas (up to 100%), renal tumors (82.6-97.8%), and urothelial neoplasms (2.3-23.7%). Important tumors with near complete absence of PAX8 staining (< 1%) included all subtypes of breast cancers, hepatocellular carcinomas, gastric, prostatic, pancreatic, and pulmonary adenocarcinomas, neuroendocrine neoplasms, small cell carcinomas of various sites, and lymphomas. High PAX8 expression was associated with low tumor grade in 365 non-invasive papillary urothelial carcinomas (p < 0.0001) but unrelated to patient outcome and/or tumor phenotype in clear cell renal cell carcinoma, high-grade serous ovarian cancer, and endometrioid endometrial carcinoma. For determining a renal tumor origin, sensitivity was 88.1% and specificity 87.2% for PAX8, while sensitivity was 85.3% and specificity 95.7% for CDH16. The combination of PAX8 and CDH16 increased specificity to 96.8%. In conclusion, PAX8 immunohistochemistry is a suitable diagnostic tool. The combination of PAX8 and CDH16 positivity has high specificity for renal cell carcinoma.

Delayed therapy escape (DTE) is frequent after thalamic deep brain stimulation for essential tremor, leading to reduced quality of life, often with ataxic symptoms, and early recognition is challenging. Our goal was to examine whether a low-frequency rebound tremor of the left hand after switching off stimulation is useful as a diagnostic marker for DTE. In this cross-sectional study with additional retrospective analysis, we examined 31 patients with bilateral thalamic DBS ≥ 12 months for essential tremor, using quantitative assessments including video-based motion capture, Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS), and scale for the assessment and rating of ataxia (SARA). If available, preoperative (preOP) and 12-month postoperative assessments were included in the analysis. Evaluations occurred with DBS activated (ON) and deactivated (OFF). A higher ratio FTMTRS nowON/preOP indicated DTE. Preoperative FTMTRS scores were available for 16 patients, including 5 patients with DTE. The receiver operating characteristic analysis found an area under the curve of 0.86 (p = 0.024) for identification of DTE by low-frequency rebound tremor (i.e., OFF) on the left. In conclusion, it could serve as a potential diagnostic marker.

OBJECTIVE: Early diagnosis and prediction of organ dysfunction are critical for intervening and improving the outcomes of septic patients. The study aimed to find novel diagnostic and predictive biomarkers of organ dysfunction for perioperative septic patients.
METHODS: This is a prospective, controlled, preliminary, and single-center study of emergency surgery patients. Mass spectrometry, Gene Ontology (GO) functional analysis, and the protein-protein interaction (PPI) network were performed to identify the differentially expressed proteins (DEPs) from sepsis patients, which were selected for further verification via enzyme-linked immunosorbent assay (ELISA). Logistic regression analysis was used to estimate the relative correlation of selected serum protein levels and clinical outcomes of septic patients. Calibration curves were plotted to assess the calibration of the models.
RESULTS: Five randomized serum samples per group were analyzed via mass spectrometry, and 146 DEPs were identified. GO functional analysis and the PPI network were performed to evaluate the molecular mechanisms of the DEPs. Six DEPs were selected for further verification via ELISA. Cathepsin B (CatB), vascular cell adhesion protein 1 (VCAM-1), neutrophil gelatinase-associated lipocalin (NGAL), protein S100-A9, prosaposin, and thrombospondin-1 levels were significantly increased in the patients with sepsis compared with those of the controls (p < 0.001). Logistic regression analysis showed that CatB, S100-A9, VCAM-1, prosaposin, and NGAL could be used for preoperative diagnosis and postoperative prediction of organ dysfunction. CatB and S100-A9 were possible predictive factors for preoperative diagnosis of renal failure in septic patients. Internal validation was assessed using the bootstrapping validation. The preoperative diagnosis of renal failure model displayed good discrimination with a C-index of 0.898 (95% confidence interval 0.843-0.954) and good calibration.
CONCLUSIONS: Serum CatB, S100-A9, VCAM-1, prosaposin, and NGAL may be novel markers for preoperative diagnosis and postoperative prediction of organ dysfunction. Specifically, S100-A9 and CatB were indicators of preoperative renal dysfunction in septic patients. Combining these two biomarkers may improve the accuracy of predicting preoperative septic renal dysfunction.
BACKGROUND: The study was registered at the Chinese Clinical Trials Registry (ChiCTR2200060418) on June 1, 2022.

BACKGROUND: Dilated cardiomyopathy (DCM) is a common cause of heart failure. However, the role of cellular senescence in DCM has not been fully elucidated. Here, we aimed to investigate senescence in DCM, identify senescence related characteristic genes, and explore the potential small molecule compounds for DCM treatment.
METHODS: DCM-associated datasets and senescence-related genes were respectively obtained from Gene Expression Omnibus (GEO) database and CellAge database. The characteristic genes were identified through methods including weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and random forest. The expression of characteristic genes was verified in the mouse DCM model. Moreover, the CIBERSORT algorithm was applied to analyze immune characteristics of DCM. Finally, several therapeutic compounds were predicted by CMap analysis, and the potential mechanism of chlorogenic acid (CGA) was investigated by molecular docking and molecular dynamics simulation.
RESULTS: Three DCM- and senescence-related characteristic genes (MME, GNMT and PLA2G2A) were ultimately identified through comprehensive transcriptome analysis, and were experimentally verified in the doxorubicin induced mouse DCM. Meanwhile, the established diagnostic model, derived from dataset analysis, showed ideal diagnostic performance for DCM. Immune cell infiltration analysis suggested dysregulation of inflammation in DCM, and the characteristic genes were significantly associated with invasive immune cells. Finally, based on the specific gene expression profile of DCM, several potential therapeutic compounds were predicted through CMap analysis. In addition, molecular docking and molecular dynamics simulations suggested that CGA could bind to the active pocket of MME protein.
CONCLUSIONS: Our study presents three characteristic genes (MME, PLA2G2A, and GNMT) and a novel senescence-based diagnostic nomogram, and discusses potential therapeutic compounds, providing new insights into the diagnosis and treatment of DCM.

UNASSIGNED: Echinococcosis is a chronic zoonotic disease caused by tapeworms of the genus Echinococcus. The World Health Organization (WHO) has identified encapsulated disease as one of 17 neglected diseases to be controlled or eliminated by 2050. There is no accurate, early, non-invasive molecular diagnostic method to detect echinococcosis. The feasibility of circulating free DNA as a diagnostic method for echinococcosis has yielded inconclusive results in a number of published studies. However, there has been no systematic evaluation to date assessing the overall performance of these assays. We report here the first meta-analysis assessing the diagnostic accuracy of cfDNA in plasma, serum, and urine for echinococcosis.
UNASSIGNED: We systematically searched PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and WeiPu databases up to 17 January 2024, for relevant studies. All analyses were performed using RevMan 5.3, Meta-DiSc 1.4, Stata 17.0, and R 4.3.1 software. The sensitivity, specificity, and other accuracy indicators of circulating free DNA for the diagnosis of echinococcosis were summarized. Subgroup analyses and meta-regression were performed to identify sources of heterogeneity.
UNASSIGNED: A total of 7 studies included 218 patients with echinococcosis and 214 controls (156 healthy controls, 32 other disease controls (non-hydatid patients), and 26 non-study-targeted echinococcosis controls were included). Summary estimates of the diagnostic accuracy of cfDNA in the diagnosis of echinococcosis were as follows: sensitivity (SEN) of 0.51 (95% CI: 0.45-0.56); specificity (SPE) of 0.99 (95% CI: 0.97-0.99); positive likelihood ratio (PLR) of 11.82 (95% CI: 6.74-20.74); negative likelihood ratio (NLR) of 0.57 (95% CI: 0.41-0.80); diagnostic ratio (DOR) of 36.63 (95% CI: 13.75-97.59); and area under the curve (AUC) value of 0.98 (95% CI: 0.96-1.00).
UNASSIGNED: Existing evidence indicates that the combined specificity of circulating cfDNA for echinococcosis is high. However, the combined sensitivity performance is unsatisfactory due to significant inter-study heterogeneity. To strengthen the validity and accuracy of our findings, further large-scale prospective studies are required.Systematic review registrationThe systematic review was registered in the International Prospective Register of Systematic Reviews PROSPERO [CRD42023454158]. https://www.crd.york.ac.uk/PROSPERO/.

UNASSIGNED: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.
UNASSIGNED: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.
UNASSIGNED: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.
UNASSIGNED: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.

Diquat (DQ) is a commonly used bipyridine herbicide known for its toxic properties and adverse effects on individuals. However, the mechanism underlying DQ-induced damage remain elusive. Our research aimed to uncover the regulatory network involved in DQ-induced damage. We analyzed publicly accessible gene expression patterns and performed research using a DQ-induced damage animal model. The GSE153959 dataset from the Gene Expression Omnibus collection and the animal model of DQ-induced kidney injury were used to identify differentially expressed genes (DEGs). Pathways including the regulation of DNA-templated transcription in response to stress, RNA polymerase II transcription regulator complex and transcription coregulatory activity were shown to be enriched in 21 DEGs. We used least absolute shrinkage and selection operator (LASSO) regression analysis to find possible diagnostic biomarkers for DQ-induced damage. Then, we used an HK-2 cell model to confirm these results. Additionally, we confirmed that 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) was the major gene associated with DQ-induced damage using multi-omics screening. The sample validation strongly suggested that HMGCS2 has promise as a diagnostic marker and may provide new targets for therapy in the context of DQ-induced damage.

Introduction Cyclin-dependent kinase inhibitor 2A (CDKN2A) is a suppressor carcinogenic gene that is upregulated across various types of cancer including breast, liver, thyroid, and bile duct cancer due to its crucial role in cell cycle regulation and cell division. Nevertheless, it is mostly investigated at the genetic level, but it is still poorly studied on pan-cancer analysis as a biomarker and this study shows its significant potential diagnostic and prognostic characteristics. However, this study aims to investigate the role of CDKN2A as a diagnostic and prognostic biomarker across various types of cancer focusing primarily on colon adenocarcinoma (COAD). Methods We investigated CDKN2A gene expression in a pan-cancer analysis across different types of cancer to show its diagnostic potential characteristics by using various bioinformatic tools, including Tumor Immune Estimation Resource (TIMER) 2.0, Gene Expression Profiling Interactive Analysis (GEPIA), and University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) database. TIMER was used to profile gene expression across 32 types of cancer composed of 10,000 RNA-seq samples obtained from the Cancer Genome Atlas (TCGA) and to analyze the tumor-infiltrating immune cells. In addition, GEPIA and UALCAN were further used to analyze gene expression, in terms of gene regulation, pathological stages, and clinical parameters, including gender, age, and race. Therefore, we used GEPIA, UALCAN, and Kaplan-Meier plotter particularly across adenocarcinoma to investigate CDKN2A prognosis by studying its high expression association with the patient\'s overall survival rate to show the tumor progression. Then, we looked into the genetic alteration of CDKN2A by using the cBio Cancer Genomics Portal (cBioPortal), including 10 pan-cancer studies. We concluded the analysis with gene validation by using a public cohort in Gene Expression Omnibus (GEO). Results CDKN2A showed a trend of upregulation in most cancers and it was significantly upregulated in five cancers, which were commonly identifiable in three databases, including breast invasive carcinoma (p < 0.001), kidney chromophobe (p < 0.001), kidney renal clear cell carcinoma (p < 0.001), kidney renal papillary cell carcinoma (p < 0.001), and COAD (p < 0.001). The upregulation was significantly different in association with pathogenic stages II and III (pr(>F) = 0.00234) which was identifiable significantly in COAD more than in other cancers. The gene showed a high upregulation in association with poor prognosis of patient survival in three cancers, including COAD (log-rank p = 0.011), mesothelioma (log-rank p = 5.9e-07), and liver hepatocellular carcinoma (log-rank p = 0.0045). Therefore, COAD was the only comprehensively analyzed tumor to show a diagnostic and prognostic potential characteristic during high upregulation of CDKN2A. Furthermore, CDKN2A displayed a rare mutation in the form of deep deletion (9%) and revealed an upregulation associated with CD4+ T cells (p = 0.0108), macrophage (p = 0.0073), and neutrophils (p = 0.0272) as immune cells infiltrating COAD.  Conclusion Our study demonstrates the pan-cancer relevance of CDKN2A and revealed a novelty in showing CDKN2A underscores its potential as a diagnostic prognostic biomarker in COAD since CDKN2A is mostly studied at a genetic level across COAD.