OBJECTIVE: Gastric cancer (GC) is the third cause of cancer mortality worldwide. A screening strategy that combines an upper gastrointestinal endoscopy (UGIE) with a screening colonoscopy may be cost-effective in intermediate-risk regions. This study aimed to evaluate the intention to adhere to combined endoscopic screening and assess knowledge of GC symptoms, risk factors, and barriers to screening.
METHODS: Cross-sectional study enrolling individuals eligible for CRC screening in northern Portugal, where a populational fecal occult blood test (FOBT) program is implemented. The validated PERCEPT-PREVENT tool was applied across three groups: (a) not yet invited to CRC screening, (b) FOBT-positive referred to colonoscopy, and (c) primary colonoscopy screening.
RESULTS: A high acceptance rate was observed for combined endoscopic screening (94%; n = 264) [not yet invited to CRC screening 98% (n = 90) vs. FOBT-positive referred to colonoscopy 90% (n = 103) vs. primary colonoscopy 97% (n = 71); p = 0.017], with the vast majority reporting intention to adhere in the setting of full reimbursement (97%; n = 255). Most respondents were unaware of any possible GC symptom (76%; n = 213), risk factor (73%; n = 205), and UGIE-related complication (85%; n = 237). Regular follow-up with the primary care physician (Odds Ratio (OR) 27.59, 95% confidence interval (CI) 2.99-254.57), lower perceived negative health consequences of UGIE (OR 1.40, 95% CI 1.13-1.74), and lower perceived financial burden (OR 2.46, 95% CI 1.04-5.85) were the only factors independently associated with a higher intention to undergo combined screening.
CONCLUSIONS: Willingness to undergo combined endoscopic screening was notably high and positively impacted by lower perceived barriers. Additional efforts should be undertaken to improve levels of digestive health literacy.

BACKGROUND: The burden of cancer in India has been rising, yet testing for early detection remains low. This study explored inequalities in the uptake of breast cancer (BC) examination and cervical cancer (CC) among Indian women, focusing on socioeconomic, regional, and educational differences.
METHODS: Data from the 2019-21 National Family Health Survey (n=353,518) were used to assess the uptake of BC examination and CC testing. Inequalities were quantified using the slope index of inequality (SII), relative index of inequality (RII), and relative concentration index (RCI). SII measured absolute inequality, while RII and RCI assessed relative inequality between disadvantaged and advantaged groups.
RESULTS: The ever uptake of tests for early detection of BC and CC were low at 9 and 20 per 1,000 women, respectively. Higher uptake was observed among women from the richest households compared to the poorest (SII: 1.1 for BC and 1.8 for CC). The magnitude of relative socioeconomic inequalities was more pronounced in rural areas (RCI: 22.5 for BC and 21.3 for CC) compared to urban areas. Similarly, higher-educated women were 4.84 times (RII: 4.84) and 2.12 times (RII: 2.12) more likely to undergo BC examination and CC testing, respectively, compared to non-educated women. The northeastern region exhibited greater socioeconomic inequality, while the western region showed more education-based inequality.
CONCLUSIONS: The lower uptake of BC examination and CC testing and the marked inequalities underscore the need for targeted interventions to improve access and utilization of testing services, especially among lower-educated women, and those in rural areas.

UNASSIGNED: Survival rates for esophageal squamous cell carcinoma (ESCC) are extremely low due to the late diagnosis of most cases. An understanding of the early molecular processes that lead to ESCC may facilitate opportunities for early diagnosis; however, these remain poorly defined. Tylosis with esophageal cancer (TOC) is a rare syndrome associated with a high lifetime risk of ESCC and germline mutations in RHBDF2, encoding iRhom2. Using TOC as a model of ESCC predisposition, this study aimed to identify early-stage transcriptional changes in ESCC development.
UNASSIGNED: Esophageal biopsies were obtained from control and TOC individuals, the latter undergoing surveillance endoscopy, and adjacent diagnostic biopsies were graded as having no dysplasia or malignancy. Bulk RNA-Seq was performed, and findings were compared with sporadic ESCC vs normal RNA-Seq datasets.
UNASSIGNED: Multiple transcriptional changes were identified in TOC samples, relative to controls, and many were detected in ESCC. Accordingly, pathway analyses predicted an enrichment of cancer-associated processes linked to cellular proliferation and metastasis, and several transcription factors were predicted to be associated with TOC and ESCC, including negative enrichment of GRHL2. Subsequently, a filtering strategy revealed 22 genes that were significantly dysregulated in both TOC and ESCC. Moreover, Keratin 17, which was upregulated in TOC and ESCC, was also found to be overexpressed at the protein level in \'normal\' TOC esophagus tissue.
UNASSIGNED: Transcriptional changes occur in TOC esophagus prior to the onset of dysplasia, many of which are associated with ESCC. These findings support the utility of TOC to help reveal the early molecular processes that lead to sporadic ESCC.

Epithelial ovarian cancer (EOC) is the deadliest women\'s cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.

(1) Background: The detection of methylated SEPT9 (mSEPT9) in plasma is a promising approach to non-invasive colorectal cancer (CRC) screening. Traditional approaches have limitations in sensitivity and cost-effectiveness, particularly in resource-limited settings. (2) Methods: We developed a semi-nested realtime PCR assay utilizing extendable blocking probes (ExBP) to enhance the detection of low-level mSEPT9 based on DNA melting. This assay allows for the discrimination of mSEPT9 in the presence of high concentrations of non-methylated SEPT9 (up to 100,000 times higher). (3) Results: The assay demonstrated a sensitivity of 73.91% and specificity of 80%, showcasing its ability to detect very low levels of methylated DNA effectively. The innovative use of ExBP without costly modified probes simplifies the assay setup and reduces the overall costs, enhancing its applicability in diverse clinical settings. (4) Conclusions: This novel assay significantly improves the detection of mSEPT9, offering a potential advance in CRC screening and monitoring. Its cost-efficiency and high sensitivity make it particularly suitable for the early detection and management of CRC, especially in settings with limited resources. Future studies are encouraged to validate this assay in larger populations to establish its clinical benefits and practical utility.

This review comprehensively explores the complex interplay between extracellular vesicles (ECVs)/exosomes and circadian rhythms, with a focus on the role of this interaction in hepatocellular carcinoma (HCC). Exosomes are nanovesicles derived from cells that facilitate intercellular communication by transporting bioactive molecules such as proteins, lipids, and RNA/DNA species. ECVs are implicated in a range of diseases, where they play crucial roles in signaling between cells and their surrounding environment. In the setting of cancer, ECVs are known to influence cancer initiation and progression. The scope of this review extends to all cancer types, synthesizing existing knowledge on the various roles of ECVs. A unique aspect of this review is the emphasis on the circadian-controlled release and composition of exosomes, highlighting their potential as biomarkers for early cancer detection and monitoring metastasis. We also discuss how circadian rhythms affect multiple cancer-related pathways, proposing that disruptions in the circadian clock can alter tumor development and treatment response. Additionally, this review delves into the influence of circadian clock components on ECV biogenesis and their impact on reshaping the tumor microenvironment, a key component driving HCC progression. Finally, we address the potential clinical applications of ECVs, particularly their use as diagnostic tools and drug delivery vehicles, while considering the challenges associated with clinical implementation.

Technological advancements in cell-free DNA (cfDNA) liquid biopsy have triggered exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has made significant strides in personalizing cancer treatment, the exploration and translation of epigenetics in liquid biopsy to clinical practice is still nascent. This comprehensive review seeks to provide a broad yet in-depth narrative of the present status of epigenetics in cfDNA liquid biopsy and its associated challenges. It highlights the potential of epigenetics in cfDNA liquid biopsy technologies with the hopes of enhancing its clinical translation. The momentum of cfDNA liquid biopsy technologies in recent years has propelled epigenetics to the forefront of molecular biology. We have only begun to reveal the true potential of epigenetics in both our understanding of disease and leveraging epigenetics in the diagnostic and therapeutic domains. Recent clinical applications of epigenetics-based cfDNA liquid biopsy revolve around DNA methylation in screening and early cancer detection, leading to the development of multi-cancer early detection tests and the capability to pinpoint tissues of origin. The clinical application of epigenetics in cfDNA liquid biopsy in minimal residual disease, monitoring, and surveillance are at their initial stages. A notable advancement in fragmentation patterns analysis has created a new avenue for epigenetic biomarkers. However, the widespread application of cfDNA liquid biopsy has many challenges, including biomarker sensitivity, specificity, logistics including infrastructure and personnel, data processing, handling, results interpretation, accessibility, and cost effectiveness. Exploring and translating epigenetics in cfDNA liquid biopsy technology can transform our understanding and perception of cancer prevention and management. cfDNA liquid biopsy has great potential in precision oncology to revolutionize conventional ways of early cancer detection, monitoring residual disease, treatment response, surveillance, and drug development. Adapting the implementation of liquid biopsy workflow to the local policy worldwide and developing point-of-care testing holds great potential to overcome global cancer disparity and improve cancer outcomes.

We present a follow-on technique for the cyclic-immunofluorescence profiling of suspension particles isolated using dielectrophoresis. The original lab-on-chip technique (\"cyc-DEP\" [cyclic immunofluorescent imaging on dielectrophoretic chip]) was designed for the multiplex surveillance of circulating biomarkers. Nanoparticles were collected from low-volume liquid biopsies using microfluidic dielectrophoretic chip technology. Subsequent rounds of cyclic immunofluorescent labeling and quenching were imaged and quantified with a custom algorithm to detect multiple proteins. While cyc-DEP improved assay multiplicity, long runtimes threatened its clinical adoption. Here, we modify the original cyc-DEP platform to reduce assay runtimes. Nanoparticles were formulated from human prostate adenocarcinoma cells and collected using dielectrophoresis. Three proteins were labeled on-chip with a mixture of short oligonucleotide-conjugated antibodies. The sample was then incubated with complementary fluorophore-conjugated oligonucleotides, which were dehybridized using an ethylene carbonate buffer after each round of imaging. Oligonucleotide removal exhibited an average quenching efficiency of 98 ± 3% (n = 12 quenching events), matching the original cyc-DEP platform. The presented \"oligo cyc-DEP\" platform achieved clinically relevant sample-to-answer times, reducing the duration for three rounds of cyclic immunolabeling from approximately 20 to 6.5 h-a 67% decrease attributed to rapid fluorophore removal and the consolidated co-incubation of antibodies.

Circulating extracellular vesicles and particles (EVPs) are being investigated as potential biomarkers for early cancer detection, prognosis, and disease monitoring. However, the suboptimal purity of EVPs isolated from peripheral blood plasma has posed a challenge of in-depth analysis of the EVP proteome. Here, we compared the effectiveness of different methods for isolating EVPs from healthy donor plasma, including ultracentrifugation (UC)-based protocols, phosphatidylserine-Tim4 interaction-based affinity capture (referred to as \"PS\"), and several commercial kits. Modified UC methods with an additional UC washing or size exclusion chromatography step substantially improved EVP purity and enabled the detection of additional proteins via proteomic mass spectrometry, including many plasma membrane and cytoplasmic proteins involved in vesicular regulation pathways. This improved performance was reproduced in cancer patient plasma specimens, resulting in the identification of a greater number of differentially expressed EVP proteins, thus expanding the range of potential biomarker candidates. However, PS and other commercial kits did not outperform UC-based methods in improving plasma EVP purity. PS yielded abundant contaminating proteins and a biased enrichment for specific EVP subsets, thus unsuitable for proteomic profiling of plasma EVPs. Therefore, we have optimized UC-based protocols for circulating EVP isolation, which enable further in-depth proteomic analysis for biomarker discovery.

UNASSIGNED: Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps.
UNASSIGNED: Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions\' tissue pathological reports were used as the learning labels.
UNASSIGNED: Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value.
UNASSIGNED: The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.