Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer.

BACKGROUND: Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis. METHODS: In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line. RESULTS: The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group. CONCLUSIONS: This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.

A novel detection technology for early gastric cancer based on Raman spectroscopy.

Despite universal endoscopic screening, early detection of gastric cancer is challenging, led researchers to seek for a novel approach in detecting. Raman spectroscopy measurements as a fingerprint of biochemical structure, enable accurate prediction of gastric lesions non-destructively. This study aimed to evaluate the diagnostic power of Raman spectroscopy in early gastric cancer (EGC), and reveal dynamic biomolecular changes in vitro from normal to EGC. To clarify the biochemical alterations in Correa's cascade, Raman spectra of human normal gastric mucosa, intestinal metaplasia, dysplasia, and adenocarcinoma were compared at tissue and cellular levels based on a self-developed data processing program. For effectively identify EGC, Raman spectroscopy was used combined with multiple machine learning methods, including partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) with leave-one-out (LOO) cross validation. A total of 450 Raman spectra were investigated in this study. The upregulation of νsym(O-P-O) backbone (p < 0.001) was identified as a favorable factor for the diagnosis of EGC, the area under the ROC curve (AUC) was up to 0.918. In addition, higher levels of lactic acid (p < 0.001), lipids (p < 0.001), phenylalanine (p = 0.002), and carotenoids (p < 0.001) were detected in EGC. Multivariate machine learning methods for diagnosis of EGC based on Raman spectroscopy, the sensitivity, specificity, accuracy, and AUC were 91.0%, 100%, 94.8%, and 95.8% for SVM, and 84.8%, 92.0%, 88.8%, and 95.5% for CNN, respectively. Raman spectroscopy can be used as a powerful tool for detecting EGC while elucidating biomolecular dynamics in tumorigenesis. (Chictr.org.cn, ChiCTR2200060720.).

Asia-Pacific Colorectal Screening Score Combined With Stool DNA Test Improves the Detection Rate for Colorectal Advanced Neoplasms.

BACKGROUND & AIMS: The Asia-Pacific Colorectal Screening (APCS) scoring system was developed to stratify the risk of colorectal advanced neoplasm (AN). We aimed to evaluate the performance of the APCS score combined with a stool DNA test used for colorectal cancer screening. METHODS: A total of 2842 subjects who visited outpatient clinics or cancer screening centers were enrolled. Age, sex, smoking status, and family history were recorded and APCS scores were calculated in 2439 participants. A stool DNA test (SDC2 and SFRP2 tests) and fecal immunochemical test (FIT) were performed and colonoscopy was used as the gold standard among 2240 subjects who completed all study procedures. We used a threshold of 4.4 µg/g for the FIT, in addition to the manufacturer's recommended threshold of 20 µg/g to match the specificity of a stool DNA test. RESULTS: Based on the APCS score, 38.8% (946 of 2439) of the subjects were categorized as high risk, and they had a 1.8-fold increase in risk for AN (95% CI, 1.4-2.3) compared with low and moderate risk. The APCS combined with the stool DNA test detected 95.2% of invasive cancers (40 of 42) and 73.5% of ANs (253 of 344), while the colonoscopy workload was only 47.1% (1056 of 2240). The sensitivity for AN of APCS combined with stool DNA test was significantly higher than that of APCS combined with FIT (73.5% vs 62.8% with FIT cut-off value of 20 µg/g, and 73.5% vs 68.0% with FIT cut-off value of 4.4 µg/g; both P < .01). CONCLUSIONS: The APCS score combined with a stool DNA test significantly improved the detection of colorectal ANs, while limiting colonoscopy resource utilization (Chictr.org.cn, ChiCTR-DDD-17011169).

A novel promising diagnosis model for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene biomarkers.

BACKGROUND: Colorectal cancer (CRC), a commonly diagnosed cancer often develops slowly from benign polyps called adenoma to carcinoma. Altered gut microbiota is implicated in colorectal carcinogenesis. It is warranted to find non-invasive progressive microbiota biomarkers that can reflect the dynamic changes of the disease. This study aimed to identify and evaluate potential progressive fecal microbiota gene markers for diagnosing advanced adenoma (AA) and CRC. RESULTS: Metagenome-wide association was performed on fecal samples from different cohorts of 871 subjects (247 CRC, 234 AA, and 390 controls). We characterized the gut microbiome, identified microbiota markers, and further constructed a colorectal neoplasms classifier in 99 CRC, 94 AA, and 62 controls, and validated the results in 185 CRC, 140 AA, and 291 controls from 3 independent cohorts. 21 species and 277 gene markers were identified whose abundance was significantly increased or decreased from normal to AA and CRC. The progressive gene markers were distributed in metabolic pathways including amino acid and sulfur metabolism. A diagnosis model consisting of four effect indexes was constructed based on the markers, the sensitivities of the Adenoma Effect Index 1 for AA, Adenoma Effect Index 2 for high-grade dysplasia (HGD) adenoma were 71.3% and 76.5%, the specificities were 90.5% and 90.3%, respectively. CRC Effect Index 1 for all stages of CRC and CRC Effect Index 2 for stage III-IV CRC to predict CRC yielded an area under the curve (AUC) of 0.839 (95% CI 0.804-0.873) and 0.857 (95% CI 0.793-0.921), respectively. Combining with fecal immunochemical test (FIT) significantly improved the sensitivity of CRC Effect Index 1 and CRC Effect Index 2 to 96.7% and 100%. CONCLUSIONS: This study reports the successful diagnosis model establishment and cross-region validation for colorectal advanced adenoma and carcinoma based on the progressive gut microbiota gene markers. The results suggested that the novel diagnosis model can significantly improve the diagnostic performance for advanced adenoma.