A novel approach is proposed leveraging surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques, principal component analysis (PCA)-centroid displacement-based nearest neighbor (CDNN). This label-free approach can identify slight abnormalities between SERS spectra of gastric lesions at different stages, offering a promising avenue for detection and prevention of precancerous lesion of gastric cancer (PLGC). The agaric-shaped nanoarray substrate was prepared using gas-liquid interface self-assembly and reactive ion etching (RIE) technology to measure SERS spectra of serum from mice model with gastric lesions at different stages, and then a SERS spectral recognition model was trained and constructed using the PCA-CDNN algorithm. The results showed that the agaric-shaped nanoarray substrate has good uniformity, stability, cleanliness, and SERS enhancement effect. The trained PCA-CDNN model not only found the most important features of PLGC, but also achieved satisfactory classification results with accuracy, area under curve (AUC), sensitivity, and specificity up to 100%. This demonstrated the enormous potential of this analysis platform in the diagnosis of PLGC.

Objectives: To develop and validate predictive models for esophageal squamous cell carcinoma (ESCC) using circulating cell-free DNA (cfDNA) terminal motif analysis. The goal was to improve the non-invasive detection of early-stage ESCC and its precancerous lesions. Methods: Between August 2021 and November 2022, we prospectively collected plasma samples from 448 individuals at the Department of Endoscopy, Cancer Hospital, Chinese Academy of Medical Sciences for cfDNA extraction, library construction, and sequencing. We analyzed 201 cases of ESCC, 46 high-grade intraepithelial neoplasia (HGIN), 46 low-grade intraepithelial neoplasia (LGIN), 176 benign esophageal lesions, and 29 healthy controls. Participants, including ESCC patients and control subjects, were randomly assigned to a training set (n=284) and a validation set (n=122). The training cohort underwent z-score normalization of cfDNA terminal motif matrices and a selection of distinctive features differentiated ESCC cases from controls. The random forest classifier, Motif-1 (M1), was then developed through principal component analysis, ten-fold cross-validation, and recursive feature elimination. M1\'s efficacy was then validated in the validation and precancerous lesion sets. Subsequently, individuals with precancerous lesions were included in the dataset and participants were randomly allocated to newly formed training (n=243), validation (n=105), and test (n=150) cohorts. Using the same procedure as M1, we trained the Motif-2 (M2) random forest model with the training cohort. The M2 model\'s accuracy was then confirmed in the validation cohort to establish the optimal threshold and further tested by performing validation in the test cohort. Results: We developed two cfDNA terminal motif-based predictive models for ESCC and associated precancerous conditions. The first model, M1, achieved a sensitivity of 90.0%, a specificity of 77.4%, and an area under the curve (AUC) of 0.884 in the validation cohort. For LGIN, HGIN, and T1aN0 stage ESCC, M1\'s sensitivities were 76.1%, 80.4%, and 91.2% respectively. Notably, the sensitivity for jointly predicting HGIN and T1aN0 ESCC reached 85.0%. Both the predictive accuracy and sensitivity increased in line with the cancer\'s progression (P＜0.001). The second model, M2, exhibited a sensitivity of 87.5%, a specificity of 77.4%, and an AUC of 0.857 in the test cohort. M2\'s sensitivities for detecting precancerous lesions and ESCC were 80.0% and 89.7%, respectively, and it showed a combined sensitivity of 89.4% for HGIN and T1aN0 stage ESCC. Conclusions: Two predictive models based on cfDNA terminal motif analysis for ESCC and its precancerous lesions are developed. They both show high sensitivity and specificity in identifying ESCC and its precancerous stages, indicating its potential for early ESCC detection.
目的： 探索并建立基于循环游离DNA（cfDNA）末端基序检测的食管鳞状细胞癌（ESCC）预测模型，并评估其用于ESCC早期检测的可行性。 方法： 前瞻性收集2021年8月至2022年11月中国医学科学院肿瘤医院201例ESCC、46例食管高级别上皮内瘤变（HGIN）、46例食管低级别上皮内瘤变（LGIN）、176例食管良性病变患者和29例健康对照的血浆样本进行cfDNA提取、文库构建与测序。将ESCC患者和对照组受试者随机分为训练集（284例）和验证集（122例）。对训练集中ESCC及对照组cfDNA末端基序矩阵标准化处理及差异特征筛选，经主成分分析降维、十折交叉验证及随机森林递归特征消除后训练并建立随机森林分类模型Motif-1（M1），在验证集和癌前病变组中验证其性能。将癌前病变组患者纳入数据集后随机分为训练集（243例）、验证集（105例）、测试集（150例），采用与M1相同的方法使用训练集训练并构建随机森林模型Motif-2（M2），在验证集中检验并确定最佳阈值，在测试集中进行性能检测。 结果： 构建了2个基于cfDNA 末端基序检测的ESCC及其癌前病变的预测模型，M1模型在验证集中的灵敏度为90.0%，特异度为77.4%，曲线下面积（AUC）为0.884；M1模型预测LGIN、HGIN及T1aN0期食管癌的灵敏度分别为76.1%、80.4%和91.2%，联合预测HGIN及T1aN0期食管癌的灵敏度为85.0%，且预测分值和灵敏度随着恶性肿瘤的临床分期上升而增加（P＜0.001）。M2模型在测试集中灵敏度为87.5%，特异度为77.4%，AUC为0.857；M2模型预测食管癌前病变及ESCC的灵敏度分别为80.0%和89.7%，联合预测HGIN及T1aN0期食管癌的灵敏度为89.4%。 结论： 构建的2种基于 cfDNA 末端基序的血浆检测模型在ESCC及其癌前病变中具有较高的灵敏度及特异度，可用于早期ESCC检测。.

OBJECTIVE: The genomic and molecular ecology involved in the stepwise continuum progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) remains unclear and requires further elucidation. We aimed to characterize gene mutations and expression landscapes, and explore the association between differentially expressed genes (DEGs) and significantly mutated genes (SMGs) during the dynamic evolution from AIS to IAC.
METHODS: Thirty-five patients with ground-glass nodules (GGNs) lung adenocarcinomas were enrolled. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-Seq) were conducted on all patients, encompassing both tumor samples and corresponding noncancerous tissues. Data obtained from WES and RNA-Seq were subsequently analyzed.
RESULTS: The findings from WES delineated that the predominant mutations were observed in EGFR (49%) and ANKRD36C (17%). SMGs, including EGFR and RBM10, were associated with the dynamic evolution from AIS to IAC. Meanwhile, DEGs, including GPR143, CCR9, ADAMTS16, and others were associated with the entire process of invasive LUAD. We found that the signaling pathways related to cell migration and invasion were upregulated, and the signaling pathways of angiogenesis were downregulated across the pathological stages. Furthermore, we found that the messenger RNA (mRNA) levels of FAM83A, MAL2, DEPTOR, and others were significantly correlated with CNVs. Gene set enrichment analysis (GSEA) showed that heme metabolism and cholesterol homeostasis pathways were significantly upregulated in patients with EGFR/RBM10 co-mutations, and these patients may have poorer overall survival than those with EGFR mutations. Based on the six calculation methods for the immune infiltration score, NK/CD8+ T cells decreased, and Treg/B cells increased with the progression of early LUAD.
CONCLUSIONS: Our findings offer valuable insights into the unique genomic and molecular features of LUAD, facilitating the identification and advancement of precision medicine strategies targeting the invasive progression of LUAD from AIS to IAC.

OBJECTIVE: To investigate the risk factors for delayed postoperative bleeding after endoscopic submucosal dissection (ESD) in patients with gastric precancerous lesions and to construct a risk prediction model.
METHODS: This retrospective analysis included clinical data from patients with gastric precancerous lesions who underwent ESD at Wuhan University People\'s Hospital between November 2016 and June 2022. An XGBoost model was built to predict delayed bleeding after ESD using risk factors identified by univariable and multivariate logistic regression analysis. The model was evaluated using receiver operating characteristic curves (ROC), and SHapely Additive exPlanations (SHAP) analysis was used to interpret the model.
RESULTS: Seven factors were statistically associated with delayed postoperative bleeding in gastric precancerous lesions after ESD: age, low-grade intraepithelial neoplasia, hypertension, lesion size ≥ 40 mm, operative time ≥ 120 min, female, and nonuse of hemoclips. A risk prediction model was established. In the training cohort, the model achieved an AUC of 0.97 (0.96-0.98), a sensitivity of 0.90, a specificity of 0.94, and an F1 score of 0.91. In the validation cohort, the AUC was 0.94(0.90-0.98), with a sensitivity of 0.85, a specificity of 0.89, and an F1 score of 0.85. In the test cohort, the AUC was 0.94 (0.89-0.99), the sensitivity was 0.80, the specificity was 0.92, and the F1 score was 0.84, indicating strong predictive capability.
CONCLUSIONS: In this study, an XGBoost prediction model for assessing the risk of delayed postoperative bleeding after ESD in patients with gastric precancerous lesions was developed and validated. This model can be applied in clinical practice to effectively predict the risk of post-ESD bleeding for individual patients.

BACKGROUND: Microbiota may be associated with esophageal squamous cell carcinoma (ESCC) development. However, it is not known the predictive value of microbial biomarkers combining epidemiological factors for the early detection of ESCC and precancerous lesions.
METHODS: A total of 449 specimens (esophageal swabs and saliva) were collected from 349 participants with different esophageal statuses in China to explore and validate ESCC-associated microbial biomarkers from genes level to species level by 16S rRNA sequencing, metagenomic sequencing and real-time quantitative polymerase chain reaction.
RESULTS: A bacterial biomarker panel including Actinomyces graevenitzii (A.g_1, A.g_2, A.g_3, A.g_4), Fusobacteria nucleatum (F.n_1, F.n_2, F.n_3), Haemophilus haemolyticus (H.h_1), Porphyromonas gingivalis (P.g_1, P.g_2, P.g_3) and Streptococcus australis (S.a_1) was explored by metagenomic sequencing to early detect the participants in Need group (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia and ESCC) vs participants without these lesions as the Noneed group. Significant quantitative differences existed for each microbial target in which the detection efficiency rate was higher in saliva than esophageal swab. In saliva, the area under the curve (AUC) based on the microbial biomarkers (A.g_4 ∩ P.g_3 ∩ H.h_1 ∩ S.a_1 ∩ F.n_2) was 0.722 (95% CI 0.621-0.823) in the exploration cohort. Combining epidemiological factors (age, smoking, drinking, intake of high-temperature food and toothache), the AUC improved to 0.869 (95% CI 0.802-0.937) in the exploration cohort, which was validated with AUC of 0.757 (95% CI 0.663-0.852) in the validation cohort.
CONCLUSIONS: It is feasible to combine microbial biomarkers in saliva and epidemiological factors to early detect ESCC and precancerous lesions in China.

BACKGROUND: Proliferative verrucous leukoplakia (PVL), distinguished by its malignant transformation rate of 43.87% to 65.8%, stands as the oral potentially malignant disorder with the highest propensity for malignancy. PVL is marked by distinctive heterogeneity regarding the clinical or histopathological characteristics as well as prognostic factors pertinent to this condition. The purpose of this study is to compile and assess the clinicopathological features, malignant transformation, and associated risk factors in patients diagnosed with PVL.
METHODS: This study is a hospital-based retrospective longitudinal study of 36 patients diagnosed with PVL from 2013 to 2023. We conducted complete clinical and histopathological evaluations of the patients.
RESULTS: The cohort comprised 16 males and 20 females, yielding a male-to-female ratio of 1:1.25. The follow-up period ranged from 8 to 125 months, with an average of 47.50 months. The most common clinical type of lesion was the verrucous form (58.33%), and the gingiva was the most common site (44.44%). Each patient had between 2 to 7 lesions, averaging 3.36 per patient. During the follow-up period, twelve patients (33.3%) developed oral cancer, with an average time to malignant transformation of 35.75 months. Kaplan-Meier survival analysis indicated that patients with complaints of pain, roughness, or a rough sensation, with diabetes, and the presence of cytologic atypia histologically showed a higher risk of malignant transformation (p < 0.05). In this study, the rate of malignant transformation in the treatment group (5/23) was lower than that in the untreated group (7/13), however, no statistically significant difference (p = 0.05).
CONCLUSIONS: The main complaints of pain, roughness, or foreign body sensation, coupled with cytologic atypia histologically are indicative of an increased risk of malignant transformation in PVL. Further research is needed to elucidate the influence of these clinicopathological parameters on the malignant progression of PVL.

BACKGROUND: Incidental colorectal fluorodeoxyglucose (FDG) uptake, observed during positron emission tomography/computed tomography (PET/CT) scans, attracts particular attention due to its potential to represent both benign and pre-malignant/malignant lesions. Early detection and excision of these lesions are crucial for preventing cancer development and reducing mortality. This research aims to evaluate the correlation between incidental colorectal FDG uptake on PET/CT with colonoscopic and histopathological results.
METHODS: Retrospective analysis was performed on data from all patients who underwent PET/CT between December 2019 and December 2023 in our hospital. The study included 79 patients with incidental colonic FDG uptake who underwent endoscopy. Patient characteristics, imaging parameters, and the corresponding colonoscopy and histopathological results were studied. A comparative analysis was performed among the findings from each of these modalities. The optimal cut-off value of SUVmax for 18F-FDG PET/CT diagnosis of premalignant and malignant lesions was determined by receiver operating characteristic (ROC) curves. The area under the curve (AUC) of SUVmax and the combined parameters of SUVmax and colonic wall thickening (CWT) were analyzed.
RESULTS: Among the 79 patients with incidental colorectal FDG uptake, histopathology revealed malignancy in 22 (27.9%) patients and premalignant polyps in 22 (27.9%) patients. Compared to patients with benign lesions, patients with premalignant and malignant lesions were more likely to undergo a PET/CT scan for primary evaluation (p = 0.013), and more likely to have focal GIT uptake (p = 0.001) and CWT (p = 0.001). A ROC curve analysis was made and assesed a cut-off value of 7.66 SUVmax (sensitivity: 64.9% and specificity: 82.4%) to distinguish premalignant and malignant lesions from benign lesions. The AUCs of the SUVmax and the combined parameters of SUVmax and CWT were 0.758 and 0.832 respectively.
CONCLUSIONS: For patients undergo PET/CT for primary evaluation, imaging features of colorectal focal FDG uptake and CWT were more closely associated with premalignant and malignant lesions. The SUVmax helps determine benign and premalignant/malignant lesions of the colorectum. Moreover, the combination of SUVmax and CWT parameters have higher accuracy in estimating premalignant and malignant lesions than SUVmax.

BACKGROUND: Low grade intraepithelial neoplasia (LGIN) and high grade intraepithelial neoplasia (HGIN) are potential precancerous lesion of gastric neoplasms. Endoscopic submucosal dissection (ESD) is the first option for the treatment of precancerous lesion and early gastric cancer (EGC). Traction is an effective method to improve efficiency, and reduce complications during ESD. In this study, we shared a useful traction method using the clip-and-snare method with a pre-looping technique (CSM-PLT) for precancerous lesion and EGC.
METHODS: We retrospectively analyzed patients received ESD combined with CSM-PLT or conventional ESD from June 2018 to December 2021 in Shenzhen People\'s hospital. The primary outcome was resection speed.
RESULTS: Forty-two patients were enrolled in ESD combined with CSM-PLT group and sixty-five patients in conventional ESD group respectively. Baseline characteristics were comparable among two groups (P>0.05). There were no significant differences in terms of R0 resection rate, en bloc resection rate (97.6% vs. 98.5%, P = 1.000 and 97.6% vs. 96.9%, P = 1.000, respectively), operation costs (933.7 (644.1-1102.4) dollars vs. 814.7 (614.6-988.3) dollars, P = 0.107), and hospital stays (8.0 ± 3.1 days vs. 7.3 ± 3.2 days, P = 0.236). In addition, no significant difference was observed with respect to complications (P>0.05). However, the resection speed of ESD combined with CSM-PLT was faster than that of conventional ESD (11.3 (9.4-14.9) mm2/min vs. 8.0 (5.8-10.9) mm2/min, P < 0.001), particularly lesions located in anterior wall and lesser curvature. In addition, the association between ESD combined with CSM-PLT and resection speed was still supported after propensity matching scores (PMS).
CONCLUSIONS: CSM-PLT can help to improve ESD efficiency without reducing the en bloc resection rate or increasing the incidence of complications.

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC\'s mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

OBJECTIVE: Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI\'s diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists\' ability, and explore the main factors affecting AI\'s performance.
METHODS: The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data.
RESULTS: Twelve studies with 11,173 patients were included, demonstrating AI models\' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%).
CONCLUSIONS: AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.