Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Background: Screening with low‐dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false‐positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.Methods: The keywords “lung cancer,” “lung neoplasms,” “lung tumor,” “risk,” “lung carcinoma” “risk,” “predict,” “assessment,” and “nodule” were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.Results: A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single‐center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.Conclusion: The existing models showed good discrimination for identifying high‐risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.

Background: Epigenetic alterations have been shown to contribute immensely to human carcinogenesis. Dynamic and reversible N6‐methyladenosine (m6A) RNA modification regulates gene expression and cell fate. However, the reasons for activation of KIAA1429 (also known as VIRMA, an RNA methyltransferase) and its underlying mechanism in lung adenocarcinoma (LUAD) remain largely unexplored. In this study, we aimed to clarify the oncogenic role of KIAA1429 in the tumorigenesis of LUAD. Methods: Whole‐genome sequencing and transcriptome sequencing of LUAD data were used to analyze the gene amplification of RNA methyltransferase. The in vitro and in vivo functions of KIAA1429 were investigated. Transcriptome sequencing, methylated RNA immunoprecipitation sequencing (MeRIP‐seq), m6A dot blot assays and RNA immunoprecipitation (RIP) were performed to confirm the modified gene mediated by KIAA1429. RNA stability assays were used to detect the half‐life of the target gene. Results: Copy number amplification drove higher expression of KIAA1429 in LUAD, which was correlated with poor overall survival. Manipulating the expression of KIAA1429 could regulate the proliferation and metastasis of LUAD. Mechanistically, the target genes of KIAA1429‐mediated m6A modification were confirmed by transcriptome sequencing and MeRIP‐seq assays. We also revealed that KIAA1429 could regulate BTG2 expression in an m6A‐dependent manner. Knockdown of KIAA1429 significantly decreased the m6A levels of BTG2 mRNA, leading to enhanced YTH m6A RNA binding protein 2 (YTHDF2, the m6A “reader”)‐dependent BTG2 mRNA stability and promoted the expression of BTG2; thus, participating in the tumorigenesis of LUAD. Conclusions: Our data revealed the activation mechanism and important role of KIAA1429 in LUAD tumorigenesis, which may provide a novel view on the targeted molecular therapy of LUAD.

Tumor-initiating cells (TICs) reprogram their metabolic features to meet their bioenergetic, biosynthetic, and redox demands. Our previous study established a role for wild-type isocitrate dehydrogenase 1 (IDH1 ^WT ) as a potential diagnostic and prognostic biomarker for non–small cell lung cancer (NSCLC), but how IDH1 ^WT modulates NSCLC progression remains elusive. Here, we report that IDH1 ^WT activates serine biosynthesis by enhancing the expression of phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase 1 (PSAT1), the first and second enzymes of de novo serine synthetic pathway. Augmented serine synthesis leads to GSH/ROS imbalance and supports pyrimidine biosynthesis, maintaining tumor initiation capacity and enhancing gemcitabine chemoresistance. Mechanistically, we identify that IDH1 ^WT interacts with and stabilizes PHGDH and fragile X–related protein-1 (FXR1) by impeding their association with the E3 ubiquitin ligase parkin by coimmunoprecipitation assay and proximity ligation assay. Subsequently, stabilized FXR1 supports PSAT1 mRNA stability and translation, as determined by actinomycin D chase experiment and in vitro translation assay. Disrupting IDH1 ^WT -PHGDH and IDH1 ^WT -FXR1 interactions synergistically reduces NSCLC stemness and sensitizes NSCLC cells to gemcitabine and serine/glycine–depleted diet therapy in lung cancer xenograft models. Collectively, our findings offer insights into the role of IDH1 ^WT in serine metabolism, highlighting IDH1 ^WT as a potential therapeutic target for eradicating TICs and overcoming gemcitabine chemoresistance in NSCLC.

Esophageal cancer (EC) is a common cancer and is histopathologically classified into esophageal squamous cell carcinoma and esophageal adenocarcinoma. EC is a worldwide public health issue because of late diagnosis and lack of effective therapy. In contrast to standard tumor biopsies, liquid biopsies are emerging as a tool which is minimally invasive that can complement or even substitute more classical approaches. Specifically, cell‐free DNA (cfDNA) has shown promise in cancer‐related clinical applications. Indeed, cfDNA has been shown to be an effective circulating biomarker for non‐invasive cancer diagnosis and monitoring of cancer patients. Although the clinical application of cfDNA has been reported on other cancers, few studies have evaluated its use in EC. Here, we review this relevant literature and discuss limitations and advantages of its application in the diagnosis and monitoring of EC.

Background: Pulmonary neuroendocrine tumors (PNETs) are a special subtype of lung cancer with treatment methods are limited and prognostic indicators are insufficient. The preoperative systemic immune-inflammation index (SII) and prognostic nutritional index (PNI) are effective tumor biomarkers that have important significance for the prognosis of many malignant tumors. However, there is no similar research on the predictive value of SII and PNI for operable PNETs. Our study aimed to clarify the predictive value of SII and PNI in PNETs patients after surgical resection. Methods: This study retrospectively analysed the relevant clinical data of PNETs patients who received surgical treatment from 2005 to 2015, which was obtained from patient’s clinical records, blood test results recorded on admission before surgical treatment, and follow-up by hospital records. Results: A total of 381 PNETs patients were enrolled in this study. Preoperative PNI was associated with age (P=0.001), T stage (P=0.001), tumor length (P=0.002), drinking status (P=0.013) and smoking status (P=0.049), while SII was significantly associated with T stage (P=0.001), tumor length (P=0.001) and TNM stage (P=0.001). There was significant difference between high SII and low PNI and worse OS of PENTs (P=0.001 and P<0.001). SII (P=0.002), neutrophil/lymphocyte ratio (NLR) (P<0.001), platelet/lymphocyte ratio (PLR) (P=0.001), lymph node metastasis (P<0.001), operation time (P=0.034<0.05), treatment (P<0.001) and PNI (P=0.044<0.05) were independent prognostic factors for PNETs identified by multivariate Cox regression analysis. Conclusions: High SII and low PNI indicated poor prognosis of patients with PNETs. Both of SII and PNI can predict the prognosis of PNETs and stratify patients for better treatment.