Cancer Biomarkers最新文献

BACKGROUNDAssessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease.OBJECTIVETo assess the potential impact of a new biomarker for lung cancer using the IPC.METHODSThe IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker.RESULTSThe IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds.CONCLUSIONSThe IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.

BackgroundIt is unclear what proportion of the population cancer burden is covered by current implementation of USPSTF A/B screening recommendations.ObjectiveWe estimated the proportion of all US cancer deaths caused by cancer types not covered by screening recommendations or cancer types covered but unaddressed by current implementation.MethodsWe used 2018-2019 National Center for Health Statistics mortality data, Surveillance, Epidemiology, and End Results registries incidence-based mortality data, and published estimates of screening eligibility and receipt.ResultsOf approximately 600,000 annual cancer deaths in the US, 31.4% were from screenable cancer types, including colorectal, female breast, cervical, and smoking-associated lung cancers. Further accounting for the low receipt of lung cancer screening reduced the proportion to 17.4%; accounting for receipt of other screening reduced it to 12.8%. Thus, we estimated that current implementation of recommended screening may not address as much as 87.2% of cancer deaths-including 30.4% from individually uncommon cancer types unlikely ever to be covered by dedicated screening.ConclusionsThe large proportion of cancer deaths unaddressed by current screening represents a major opportunity for improved implementation of current approaches, as well as new multi-cancer screening technologies.

Background: The limited diagnostic accuracy of prostate-specific antigen screening for prostate cancer (PCa) has prompted innovative solutions, such as the state-of-the-art 18-gene urine test for clinically-significant PCa (MyProstateScore2.0 (MPS2)). Objective: We aim to develop a non-invasive biomarker test, the simplified MPS2 (sMPS2), which achieves similar state-of-the-art accuracy as MPS2 for predicting high-grade PCa but requires substantially fewer genes than the 18-gene MPS2 to improve its accessibility for routine clinical care. Methods: We grounded the development of sMPS2 in the Predictability, Computability, and Stability (PCS) framework for veridical data science. Under this framework, we stress-tested the development of sMPS2 across various data preprocessing and modeling choices and developed a stability-driven PCS ranking procedure for selecting the most predictive and robust genes for use in sMPS2. Results: The final sMPS2 model consisted of 7 genes and achieved a 0.784 AUROC (95% confidence interval, 0.742-0.825) for predicting high-grade PCa on a blinded external validation cohort. This is only 2.3% lower than the 18-gene MPS2, which is similar in magnitude to the 1-2% in uncertainty induced by different data preprocessing choices. Conclusions: The 7-gene sMPS2 provides a unique opportunity to expand the reach and adoption of non-invasive PCa screening.

BackgroundApolipoprotein C1 (APOC1) and Apoprotein E (APOE) play important roles in lipid transport and metabolism. In recent years, APOC1 and APOE have been shown to play key roles in the occurrence and development of various cancers. However, the expression levels, gene regulatory networks, prognostic values, and target predictions of APOC1 and APOE in adrenocortical carcinoma (ACC) remain unclear.MethodsVarious bioinformatics analysis methods were used, including gene expression profiling interactive analysis, the University of Alabama at Birmingham cancer data analysis portal, biomarker exploration of solid tumors software, the BioPortal for Cancer Genomics, search tool for the retrieval of interacting genes/proteins, gene multiple association network integration algorithm, Metascape, transcriptional regulatory relationships unraveled by sentence-based text-mining, LinkedOmics, and genomics of drug sensitivity in cancer analysis.ResultsAPOC1 and APOE expression were strongly downregulated in patients with ACC. APOC1 and APOE expression levels were lower in male patients with ACC than those in female patients. Furthermore, APOC1 and APOE expression levels affected the prognosis of patients with ACC. The main functions of APOC1 and its altered neighboring genes (ANG) were organophosphate ester transport, rRNA processing, and positive regulation of cytokine production. Cytolysis, protein ubiquitination, and histone modification were the main functions of APOE and its ANGs. The transcription factor E2F1, tumor protein p53, miR-182, miR-493, Erb-B2 receptor tyrosine kinase 2, and cyclin dependent kinase 1 were key regulatory targets of APOC1, APOE, and the ANGs. APOC1 and APOE expression in patients with ACC were positively associated with immune cell infiltration. Furthermore, anti-programmed cell death protein 1 immunotherapy strongly downregulated the expression of APOC1 in patients with ACC. Both pilaralisib and elesclomol strongly inhibited SW13 cell growth.ConclusionsThis study preliminarily clarified that APOC1 and APOE might be potential therapeutic and prognostic targets for ACC, and identified new targets and treatment strategies for ACC.

BACKGROUNDLung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies.OBJECTIVETo investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD.METHODSWe investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD.RESULTSTen differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P <  0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes.CONCLUSIONSOur study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.

BackgroundLarge community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models.ObjectiveA robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale.MethodsIn this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center.ResultsOur approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs.ConclusionsThis work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

BackgroundIn clinical practice, preoperative identification of mixed ground-glass opacity (mGGO) nodules with micropapillary component (MPC) to facilitate the implementation of individualized therapeutic strategies and avoid unnecessary surgery is increasingly importantObjectiveThis study aimed to build a predictive model based on clinical and radiological variables for the early identification of MPC in lung adenocarcinoma presenting as mGGO nodules.MethodsThe enrolled 741 lung adenocarcinoma patients were randomly divided into a training cohort and a validation cohort (3:1 ratio). The pathological specimens and preoperative images of malignant mGGO nodules from the study subjects were retrospectively reviewed. Furthermore, in the training cohort, selected clinical and radiological variables were utilized to construct a predictive model for MPC prediction.ResultsThe MPC was found in 228 (43.3%) patients in the training cohort and 72 (41.1%) patients in the validation cohort. Based on the predictive nomogram, the air bronchogram was defined as the most dominant independent risk factor for MPC of mGGO nodules, followed by the maximum computed tomography (CT) value ($>$ 200), adjacent to pleura, gender (male), and vacuolar sign. The nomogram demonstrated good discriminative ability with a C-index of 0.783 (95%[CI] 0.744-0.822) in the training cohort and a C-index of 0.799 (95%[CI] 0.732-0.866) in the validation cohort Additionally, by using the bootstrapping method, this predictive model calculated a corrected AUC of 0.774 (95% CI: 0.770-0.779) in the training cohort.ConclusionsThis study proposed a predictive model for preoperative identification of MPC in known lung adenocarcinomas presenting as mGGO nodules to facilitate individualized therapy. This nomogram model needs to be further externally validated by subsequent multicenter studies.

BackgroundThe national lung screening trial (NLST) demonstrated a reduction in lung cancer mortality with lowdose CT (LDCT) compared to chest x-ray (CXR) screening. Overdiagnosis was high (79%) among bronchoalveolar carcinoma (BAC) currently replaced by adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and adenocarcinoma of low malignant potential (LMP) exhibiting 100% disease specific survival (DSS).ObjectiveCompare the outcomes and proportions of BAC, AIS, MIA, and LMP among NLST screendetected stage IA NSCLC with overdiagnosis rate.MethodsWhole slide images were reviewed by a thoracic pathologist from 174 of 409 NLST screen-detected stage IA LUAD. Overdiagnosis rates were calculated from follow-up cancer incidence rates.ResultsMost BAC were reclassified as AIS/MIA/LMP (20/35 = 57%). The 7-year DSS was 100% for AIS/MIA/LMP and 94% for BAC. Excluding AIS/MIA/LMP, BAC behaved similarly to NSCLC (7-year DSS: 86% vs. 83%, P =  0.85) The overdiagnosis rate of LDCT stage IA NSCLC was 16.6% at 11.3-years, matching the proportion of AIS/MIA/LMP (16.2%) but not AIS/MIA (3.5%) or BAC (22.8%).ConclusionsAIS/MIA/LMP proportionally matches the overdiagnosis rate among stage IA NSCLC in the NLST, exhibiting 100% 7-year DSS. Biomarkers designed to recognize AIS/MIA/LMP preoperatively, would be useful to prevent overtreatment of indolent screen-detected cancers.

BackgroundThe status of carbohydrate antigen 19-9 (CA19-9) in metabolic syndrome (MetS) is unknown.ObjectiveTo investigate the association between serum CA19-9 levels and incident metabolic syndrome in obese middle-aged and older men.MethodsFrom 2007 to 2015, 1,750 participants were retrospectively reviewed. Health checkup data were obtained, and participants were divided into three groups based on CA19-9 levels. Various parameters including BMI, waist circumference, blood pressure, and biochemical parameters were measured. Cox regression analysis was used to assess the association between CA19-9 levels and incident MetS. The MetS diagnostic criteria were based on the National Cholesterol Education Program Adult Treatment Panel III guidelines.ResultsThe highest CA19-9 tertile was associated with an increased risk of incident MetS, high systolic blood pressure, high waist circumference, high fasting plasma glucose, low high-density lipoprotein, and high triglyceride levels. The observation period was 9 years, during which 328 (18.7%) new-onset MetS cases were identified. Subgroup analysis showed increased risk among individuals in the highest CA19-9 tertile who were obese, male, and ≥ 50 years old.ConclusionsThere is a positive correlation between serum CA19-9 levels and incident metabolic syndrome in obese middle-aged and older men.

BackgroundBreast cancer is a malignant tumor with high morbidity and mortality, which seriously endangers the health of women around the world. Biomarker-based exploration will be effective for better diagnosis, prediction and targeted therapy.ObjectiveTo construct biomarker models related to glycolysis and gluconeogenesis in breast cancer.MethodsThe gene expression of 932 breast cancer patients in the Cancer Genome Atlas (TCGA) database was analyzed by Gene Set Variation Analysis (GSVA) using glycolysis and gluconeogenesis-related pathways. Differential expression genes were searched for by the T-test. Univariate Cox proportional hazards model (COX) regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Multivariate COX regression were used to find clinically significant genes for prognostic survival. After that, the constructed gene signature was externally validated through the Gene Expression Omnibus (GEO). Finally, a nomogram was constructed to predict the survival of patients. In addition, analyzing the role of biomarkers in pan-cancer.ResultsA risk scoring model associated with glycolysis and gluconeogenesis was developed and validated. A nomogram was created to predict 2-, 3-, and 5- survival.ConclusionsThe predictive model accurately predicted the prognosis of breast cancer patients.