A Novel Serum Exosomal miRNA Signature in the Early Prediction of Persistent Organ Failure in Patients with Acute Pancreatitis.

OBJECTIVE: Current study aims to investigate whether serum exosomal microRNAs (miRNAs) could be potential biomarkers in predicting APs with POF at early phase. BACKGROUND: Novel biomarkers are sorely needed for early prediction of persistent organ failure (POF) in acute pancreatitis (AP) patients. METHODS: In the discovery stage, exosomal miRNAs were profiled in sera from APs with or without POF (5 vs. 5) using microarrays. POF-associated miRNA signatures then were assessed in training cohort (n=227) and further validated in three independent cohorts (n=516), including one nested case-control cohort. RESULTS: A total of 743 APs were recruited in this large-scale biomarker identification study with a nested case-control study. Data from the discovery cohort demonstrated that 90 exosomal miRNAs were significantly dysregulated in APs with POF compared with controls. One miRNA classifier (Cmi) comprising 3 miRNAs (miR-4265, 1208, 3127-5p) was identified in the training cohort, and was further evaluated in two validation cohorts for their predictive value for POF. AUCs for Cmi ranged from 0.88 to 0.90, which was statistically superior to AUCs of APACHE-II and BISAP, and outperformed BUN and creatinine in POF prediction across all cohorts (P<.05). Higher levels of Cmi indicated increased need for ICU admission, prolonged hospitalization, and elevated mortality rate, thus poor prognosis. In the nested case-control study, Cmi could help identify prediagnostic POF in post-ERCP pancreatitis cases within "golden hours" after ERCP with high efficacy. CONCLUSIONS: Serum exosomal Cmi may be an early predictor for POF in AP, even within "golden hours" after AP onset. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02602808).

Multigene testing panels reveal pathogenic variants in sporadic breast cancer patients in northern China.

Background: Breast cancer, the most prevalent malignancy in women worldwide, presents diverse onset patterns and genetic backgrounds. This study aims to examine the genetic landscape and clinical implications of rare mutations in Chinese breast cancer patients. Methods: Clinical data from 253 patients, including sporadic and familial cases, were analyzed. Comprehensive genomic profiling was performed, categorizing identified rare variants according to the American College of Medical Genetics (ACMG) guidelines. In silico protein modeling was used to analyze potentially pathogenic variants' impact on protein structure and function. Results: We detected 421 rare variants across patients. The most frequently mutated genes were ALK (22.2%), BARD1 (15.6%), and BRCA2 (15.0%). ACMG classification identified 7% of patients harboring Pathogenic/Likely Pathogenic (P/LP) variants, with one case displaying a pathogenic BRCA1 mutation linked to triple-negative breast cancer (TNBC). Also identified were two pathogenic MUTYH variants, previously associated with colon cancer but increasingly implicated in breast cancer. Variants of uncertain significance (VUS) were identified in 112 patients, with PTEN c.C804A showing the highest frequency. The role of these variants in sporadic breast cancer oncogenesis was suggested. In-depth exploration of previously unreported variants led to the identification of three potential pathogenic variants: ATM c.C8573T, MSH3 c.A2723T, and CDKN1C c.C221T. Their predicted impact on protein structure and stability suggests a functional role in cancer development. Conclusion: This study reveals a comprehensive overview of the genetic variants landscape in Chinese breast cancer patients, highlighting the prevalence and potential implications of rare variants. We emphasize the value of comprehensive genomic profiling in breast cancer management and the necessity of continuous research into understanding the functional impacts of these variants.

Low RCAN1.2 mRNA Expression Is Associated with Poor Prognosis of Patients with Esophageal Squamous Cell Carcinoma.

Background: This study aims to explore the role of RCAN1 in esophageal squamous cell carcinoma (ESCC) cells, determine the mRNA level of three RCAN1 isoforms in ESCC tissue, and evaluate the prognostic value of three RCAN1 isoforms. Methods: Colony-forming assay, Wound-healing assay and Transwell assay were used to evaluate the effect of RCAN1 on cell proliferation, migration and invasion. The mRNA expression of three RCAN1 isoforms was detected in paired tumor and normal tissues from 100 ESCC patients by real-time PCR. Kaplan-Meier survival curves and Cox proportional hazards model were used to evaluate the prognostic value of three RCAN1 isoforms. A nomogram was used to predict the probability of 2-year and 5-year overall survival (OS). Results: In vitro, knockdown of RCAN1 could promote ESCC cell proliferation, migration and invasion abilities. Compared to the paired normal tissues, RCAN1 isoform 1 (RCAN1.1, P=0.0027) and RCAN1 isoform 2 (RCAN1.2, P=0.0006) were significantly decreased in tumor tissues. The low expression of RCAN1.2 mRNA was associated with advanced stage (P=0.0176) and lymph node metastasis (LNM, P=0.0219). ESCC patients with low RCAN1.2 mRNA levels had shorter survival time compared to those with high RCAN1.2 levels (P=0.007). Multivariate COX analysis indicated that RCAN1.2 mRNA level was an independent prognostic indicator of OS of patients with ESCC (hazard ratio=0.5266, P=0.03554). The concordance index of nomogram to predict OS was 0.693 based on LNM, RCAN1.2, tumor stage and patients' age. Conclusion: These findings show that RCAN1 gene play a role in preventing proliferation, migration, and invasive activity of ESCC cells. RCAN1.2 mRNA level is a novel prognostic marker in ESCC, targeting RCAN1.2 may provide a potential therapeutic approach in ESCC.

Effects of N6-Methyladenosine Regulators on LAG3 and Immune Infiltrates in Lung Adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is the most common histological subtype of lung cancer, which is one of the most commonly diagnosed tumors and the leading causes of death from cancer around the world. Since RNA methylation is a posttranscriptional modification and affects so much biological progress, it is urged to explore the role of N6-methyladenosine (m6A) methylation in LUAD. Methods: We explored the expression of 24 m6A methylation genes, as well as their correlations with LAG3 in 561 LUAD samples from TCGA. Consensus clustering was applied to m6A methylation genes, and two LUAD subgroups were identified. The expression of m6A genes was analyzed by the Wilcoxon test. KEGG and GO enrichment analyses were performed to indicate the pathway affected by differentially expressed genes in the two groups. A prognostic model based on LASSO regression using an eleven-m6A gene signature was constructed according to the expression of these genes. Receiver operating characteristic (ROC) curve was used to confirm the accuracy of the model in the TCGA cohort, as well as in the test cohort from the Gene Expression Omnibus (GEO) database. Results: Compared to cluster 1, cluster 2 showed poorer overall survival (OS) and higher LAG3 expression. In addition, KEGG and GO enrichment analyses indicated that differentially expressed genes are enriched in the immune response. We also observed that the expression of LAG3 is positively correlated with IGF2BP2, CBLL1, and HNRNPA2B1 and negatively correlated with YTHDF2, YTHDF3, and FTO. For patients in the TCGA cohort, the AUC score is 0.7, and the AUC score for the GSE50081 cohort is 0.675. Patients with lower risk scores exhibited better overall survival and lower expression of LAG3 than patients with higher risk scores. Conclusions: In brief, our results indicated the important role of m6 methylation in affecting the tumor immune microenvironment and the survival of patients with LUAD. The m6A methylation gene signatures might serve as promising therapeutic targets and help the immunotherapy of LUAD in the future.

Construction of machine learning tools to predict threatened miscarriage in the first trimester based on AEA, progesterone and ß-hCG in China: a multicentre, observational, case-control study.

BACKGROUND: Endocannabinoid anandamide (AEA), progesterone (P4) and ß-human chorionic gonadotrophin (ß-hCG) are associated with the threatened miscarriage in the early stage. However, no study has investigated whether combing these three hormones could predict threatened miscarriage. Thus, we aim to establish machine learning models utilizing these three hormones to predict threatened miscarriage risk. METHODS: This is a multicentre, observational, case-control study involving 215 pregnant women. We recruited 119 normal pregnant women and 96 threatened miscarriage pregnant women including 58 women with ongoing pregnancy and 38 women with inevitable miscarriage. P4 and ß-hCG levels were detected by chemiluminescence immunoassay assay. The level of AEA was tested by ultra-high-performance liquid chromatography-tandem mass spectrometry. Six predictive machine learning models were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), accuracy and precision. RESULTS: The median concentration of AEA was significantly lower in the healthy pregnant women group than that in the threatened miscarriage group, while the median concentration of P4 was significantly higher in the normal pregnancy group than that in the threatened miscarriage group. Only the median level of P4 was significantly lower in the inevitable miscarriage group than that in the ongoing pregnancy group. Moreover, AEA is strongly positively correlated with threatened miscarriage, while P4 is negatively correlated with both threatened miscarriage and inevitable miscarriage. Interestingly, AEA and P4 are negatively correlated with each other. Among six models, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) models obtained the AUC values of 0.75, 0.70 and 0.70, respectively; and their accuracy and precision were all above 0.60. Among these three models, the LR model showed the highest accuracy (0.65) and precision (0.70) to predict threatened miscarriage. CONCLUSIONS: The LR model showed the highest overall predictive power, thus machine learning combined with the level of AEA, P4 and ß-hCG might be a new approach to predict the threatened miscarriage risk in the near feature.

Circulating Tumor-Derived Endothelial Cells: An Effective Biomarker for Breast Cancer Screening and Prognosis Prediction.

Background: Circulating tumor-derived endothelial cell (CTEC) is a new potential tumor biomarker to be associated with cancer development and treatment efficacy. However, few evidences are available for breast cancer. Methods: Eighty-nine breast cancer patients were recruited, and preoperative and postoperative blood samples were collected. Besides, 20 noncancer persons were enrolled as controls. An improved subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) method was adopted to codetect CD31+ aneuploid CTEC and CD31- aneuploid circulating tumor cell (CTC). Then, the clinical significance of CTCs and CTECs on breast cancer screening and prognosis prediction was evaluated and compared. Results: The positive rate of CTCs and CTECs in newly diagnosed breast cancer patients was 68.75% and 71.88%. Among detected aneuploid circulating rare cells, CTEC accounts for a greater proportion than CTC in breast cancer patients. CTEC-positive rate and level were significantly higher in breast cancer patients with lymph node metastasis (LNM) than those without LNM (P=0.043), while there was no significant difference in CTC. CTEC (area under the curve, AUC = 0.859) had better performance than CTC (AUC = 0.795) to distinguish breast cancer patients from controls by receiver operator characteristic curve analysis. Preoperative CTEC count ≥ 2 was a significant risk factor for reducing PFS of breast cancer patients. Conclusions: CTECs may function as a reliable supplementary biomarker in breast cancer screening and prognosis prediction.

Differences in actionable genomic alterations between brain metastases and non­brain metastases in patients with non­small cell lung cancer.

Brain metastases (BM) have been closely associated with increased morbidity and poor survival outcomes in patients with non­small cell lung cancer (NSCLC). Excluding risk factors in histological subtypes, genomic alterations, including epidermal growth factor receptor mutations and anaplastic lymphoma kinase (ALK) rearrangements have been also regarded as greater risk factors for BM in the aspect of molecular subtypes. In the present study, 69 tumor tissues and 51 peripheral blood samples from patients with NSCLC were analyzed using a hybridization capture­based next­generation sequencing (NGS) panel, including 95 known cancer genes. Among the 90 patients with stage IV NSCLC, 26 cases suffered from BM and 64 cases did not. In total, 174 somatic mutations in 35 mutated genes were identified, and 12 of these genes were concurrently present in the BM group and the non­BM group. Importantly, five mutated genes including ALK, cytidine deaminase (CDA), SMAD family member 4 (SMAD4), superoxide dismutase 2 (SOD2) and Von Hippel­Lindau tumor suppressor (VHL) genes were uniquely detected in the BM group, and they were enriched in the Hippo signaling pathway, pyrimidine metabolism and pantothenate and co­enzyme A (CoA) biosynthesis, as demonstrated using Kyoto Encyclopedia of Genes and Genomes enrichment analysis. RNA polymerase II transcription regulator complex and promyelocytic leukemia nuclear body were the top functional categories according to the Gene Ontology enrichment analysis in the BM group and non­BM group, respectively. Furthermore, 43.33% (13/30) of mutated genes were detected by both tumor tissue deoxyribonucleic acid (DNA) and plasma­derived circulating tumor DNA (ctDNA) in the non­BM group, while this percentage was only limited to 29.41% (5/17) in the BM group. To summarize, significant differences in somatic mutations, somatic interactions, key signaling pathways, functional biological information, and clinical actionability for the therapy of targeted agents were founded between the BM group and the non­BM group, and ctDNA analysis may by applied as a more credible alternative for genomic profiling in patients with advanced NSCLC without BM, due to its higher consistency for genomic profiling between ctDNA analysis and tissue DNA analysis.

The Influence of Heavy Metals on Gastric Tumorigenesis.

Objectives: This study aimed to observe the relationship among heavy metals concentration, microsatellite instability (MSI), and human epidermal growth factor receptor type 2 (HER2) gene amplification in gastric cancer (GC) patients. Methods: The concentrations of 18 heavy metals in the plasma of GC patients and healthy controls were measured by inductive coupled plasma emission spectrometry (ICP-MS). MSI detection was conducted by detecting 5 microsatellite repeat markers by PCR analysis. HER2 gene amplification was detected by fluorescence in situ hybridization (FISH). The relationship among heavy metal elements, tumor biomarkers, HER2 amplification, and MSI status was analyzed by Pearson correlation analysis. Results: A total of 105 GC patients and 62 healthy controls were recruited in this study. The concentration of arsenic (As), chromium (Cr), cuprum (Cu), mercury (Hg), manganese (Mn), lead (Pb), stibium (Sb), selenium (Se), stannum (Sn), strontium (Sr), thallium (Tl), vanadium (V), and zinc (Zn) were significantly different between GC patients and controls. Among 105 GC patients, including 87 microsatellite-stable (MSS) samples and 18 MSI samples, the concentration of Ga is significantly higher in the MSI group than that in the MSS group. Meanwhile, in 97 GC patients having detected HER2 gene amplification, 69 of 97 had negative HER2 gene amplification and the rest 28 GC patients had positive HER2 gene amplification. The concentration of Hg, Sn, and Tl is noticeably higher in the HER2 positive group than in the HER2 negative group. Only Sb was positively correlated with MSI, but none of these heavy metals was correlated with HER2 gene amplification. Conclusions: The results indicated that Sb has significant positive correlation with the MSI status, which suggests that Sb may cause MSI in GC. However, further research studies are required to elucidate the mechanisms in the near feature.

MBD3 promotes hepatocellular carcinoma progression and metastasis through negative regulation of tumour suppressor TFPI2.

BACKGROUND: The mechanism of recurrence and metastasis of hepatocellular carcinoma (HCC) is complex and challenging. Methyl-CpG binding domain protein 3 (MBD3) is a key epigenetic regulator involved in the progression and metastasis of several cancers, but its role in HCC remains unknown. METHODS: MBD3 expression in HCC was detected by immunohistochemistry and its association with clinicopathological features and patient's survival was analysed. The effects of MBD3 on hepatoma cells growth and metastasis were investigated, and the mechanism was explored. RESULTS: MBD3 is significantly highly expressed in HCC, associated with the advanced tumour stage and poor prognosis in HCC patients. MBD3 promotes the growth, angiogenesis and metastasis of HCC cells by inhibiting the tumour suppressor tissue factor pathway inhibitor 2 (TFPI2). Mechanistically, MBD3 can inhibit the TFPI2 transcription via the Nucleosome Remodeling and Deacetylase (NuRD) complex-mediated deacetylation, thus reactivating the activity of matrix metalloproteinases (MMPs) and PI3K/AKT signaling pathway, leading to the progression and metastasis of HCC CONCLUSIONS: Our results unravel the novel regulatory function of MBD3 in the progression and metastasis of HCC and identify MBD3 as an independent unfavourable prognostic factor for HCC patients, suggesting its potential as a promising therapeutic target as well.

Plasma DNA Integrity as a Prognostic Biomarker for Colorectal Cancer Chemotherapy.

OBJECTIVES: To verify whether the concentrations and integrity index of circulating cell-free DNA (cfDNA) in serum may be clinically useful for the progression monitoring of colorectal cancer (CRC) patients. METHODS: Serum samples were collected from 76 primary CRC patients who underwent surgery, including 60 with chemotherapy and 43 with follow-up. Long (247 bp) and short (115 bp) DNA fragments in serum were detected by real-time quantitative PCR by amplifying the ALU repeats. Ten serum traditional biomarkers levels were detected by chemiluminescence immunoassay assay. RESULTS: The median DNA integrity index (ALU247/ALU115) of serum DNA in the preoperative group was significantly higher than those in the postchemotherapy and the follow-up groups, while cfDNA concentration (ALU115) was significantly lower in the preoperative group compared with the postchemotherapy and the follow-up groups. CEA and CA242 were significantly lower in the postoperative group than in the preoperative group. CONCLUSIONS: Serum DNA integrity index (ALU247/115) may prove to be a promising candidate biomarker for prognostic prediction of CRC who underwent chemotherapy and during short-term follow-up.

G3BP1 Inhibition Alleviates Intracellular Nucleic Acid-Induced Autoimmune Responses.

The detection of intracellular nucleic acids is a fundamental mechanism of host defense against infections. The dysregulated nucleic acid sensing, however, is a major cause for a number of autoimmune diseases. In this study, we report that GTPase-activating protein SH3 domain-binding protein 1 (G3BP1) is critical for both intracellular DNA- and RNA-induced immune responses. We found that in both human and mouse cells, the deletion of G3BP1 led to the dampened cGAS activation by DNA and the insufficient binding of RNA by RIG-I. We further found that resveratrol (RSVL), a natural compound found in grape skin, suppressed both intracellular DNA- and RNA-induced type I IFN production through inhibiting G3BP1. Importantly, using experimental mouse models for Aicardi-Goutières syndrome, an autoimmune disorder found in humans, we demonstrated that RSVL effectively alleviated intracellular nucleic acid-stimulated autoimmune responses. Thus, our study demonstrated a broader role of G3BP1 in sensing different kinds of intracellular nucleic acids and presented RSVL as a potential treatment for autoimmune conditions caused by dysregulated nucleic acid sensing.

Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development.

Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals. Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk. Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction. Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

Definition and risk factors of early recurrence based on affecting prognosis of esophageal squamous cell carcinoma patients after radical resection.

Early recurrence after surgery could affect cancerous patients' prognosis, but the definition of early recurrence and its risk factors for esophageal squamous cell carcinoma (ESCC) patients are still unclear. This study analyzed the clinical data of 468 post-surgery recurrent ESCC patients retrospectively. A minimum p-value approach was used to evaluate the optimal cut-off value of recurrence free survival (RFS) to define early recurrence. Risk factors of early recurrence were developed based on a Cox model. The optimal cut-off value of RFS to distinguish early recurrence was 21 months (p <0.001). Independent risk factors for early recurrence included tumor locations (HR=0.562, p <0.001), pathological T stage (HR=1.829, p <0.001), tumor diameter (HR=1.344, p = 0.039), positive lymph nodes (HR=1.361, p <0.001), and total resected lymph nodes (HR=1.271, p = 044). For the late recurrent patients, there was a much more significant survival advantage for recurrence after concurrent chemoradiotherapy than that after sequential chemoradiotherapy and radiotherapy alone (p = 0.0066). In conclusion, this study defined 21 months of RFS as early recurrence and also identified its risk factors. Concurrent chemoradiotherapy was suggested as preferred post-relapse treatment for late recurrent ESCC patients.

Mutational landscape and potential therapeutic targets for sporadic pancreatic neuroendocrine tumors based on target next-generation sequencing.

Pancreatic neuroendocrine tumor (PNET), a heterogenous type of neoplasm with limited treatment options, is relatively rare and to date, the genetic background has remained to be fully elucidated. The present study aimed to determine the mutational landscape of PNET with and without liver metastasis, as well as its clinical application value for treatment. Fresh tumor tissues were collected from 14 patients with PNET following surgery, 4 of whom had developed liver metastasis. Subsequently, targeted next-generation sequencing of 612 cancer-associated genes and comprehensive analysis were performed on the tumor tissues. The results identified 63 somatic mutations in 53 genes in the 14 patients with PNET, amongst which menin 1 was identified as the most recurrently mutated gene. The analysis also identified several novel recurrently mutated genes, including adrenoceptor alpha 2B, ARVCF delta catenin family member, carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase and neuregulin 1. Among the 53 mutated genes, 11 were enriched in the PI3K/AKT signaling pathway (adjusted P=7.12x10-5). In addition, 4 patients with PNET with liver metastasis had distinctly different mutational profiles compared with those without liver metastasis; 13 genes were discovered to be exclusively mutated in the liver metastasis group of the patients with PNET, including ATRX chromatin remodeler, thioredoxin reductase 2, anus kinase 3, ARVCF delta catenin family member, integrin subunit alpha V and RAD50 double strand break repair protein. In addition, two potentially actionable alterations in BRCA2 DNA repair-associated (p.Q548Q) and neurofibromin 1 (p.Q1188X) were identified using the OncoKB database. In conclusion, the present study generated a comprehensive mutational profile of 14 patients with PNET and further described the features of patients with liver metastasis, which highlights potential targets for drug development of PNET.

The Landscape of Actionable Genomic Alterations by Next-Generation Sequencing in Tumor Tissue Versus Circulating Tumor DNA in Chinese Patients With Non-Small Cell Lung Cancer.

Background: Circulating tumor DNA (ctDNA) sequence analysis shows great potential in the management of non-small cell lung cancer (NSCLC) and the prediction of drug sensitivity or resistance in many cancers. Here, we drew and compared the somatic mutational profile using ctDNA and tumor tissue sequence analysis in lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), and assess its potential clinical value. Methods: In this study, 221 tumor tissues and 174 plasma samples from NSCLC patients were analyzed by hybridization capture-based next-generation sequencing (NGS) panel including 95 cancer-associated genes. Tumor response assessments were applied to 137 patients with advanced-stage (III and IV) NSCLC who first received targeted agents. Results: Twenty significantly mutated genes were identified such as TP53, EGFR, RB1, KRAS, PIK3CA, CD3EAP, CTNNB1, ERBB2, APC, BRAF, TERT, FBXW7, and HRAS. Among them, TP53 was the most frequently mutated gene and had a higher mutation probability in male (p = 0.00124) and smoking (p < 0.0001) patients. A total of 48.35% (191/395) of NSCLC patients possessed at least one actionable alteration according to the OncoKB database. Although the sensitivity of genomic profiling from ctDNA was lower than that from tumor tissue DNA, the mutational landscape of target genes from ctDNA is similar to that from tumor tissue DNA, which led to 61.22% (30/49) of mutational concordance in NSCLC. Additionally, the mutational concordance between tissue DNA and ctDNA in LUAD differs from that in LUSC, which is 63.83% versus 46.67%, indicating that NSCLC subtypes influence the specificity of mutation detection in plasma-derived ctDNA. Lastly, patients with EGFR and TP53 co-alterations showed similar responses to Gefitinib and Icotinib, and the co-occurring TP53 mutation was most likely to be a poor prognostic factor for patients receiving Gefitinib, indicating that the distributions and types of TP53 mutations may contribute to the efficacy and prognosis of molecular targeted therapy. Conclusions: As a promising alternative for tumor genomic profiling, ctDNA analysis is more credible in LUAD than in LUSC. Genomic subtyping has strong potential in prognostication and therapeutic decision-making for NSCLC patients, which indicated the necessity for the utility of target NGS in guiding clinical management.

Optimal Strategy for Colorectal Cancer Patients' Diagnosis Based on Circulating Tumor Cells and Circulating Tumor Endothelial Cells by Subtraction Enrichment and Immunostaining-Fluorescence In Situ Hybridization Combining with CEA and CA19-9.

BACKGROUND: Cancerous embryo antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are commonly used in clinical practice to assist in diagnosing CRC. However, their sensitivity is very low. This study aims to investigate the clinical significance of circulating tumor cells (CTCs) and circulating tumor endothelial cells (CTECs) compared with CEA and CA19-9 in the auxiliary diagnosis of colorectal cancer (CRC) patients. METHODS: 115 pathologically confirmed CRC patients and 20 healthy controls were enrolled in this study. CTCs and CTECs were enriched and identified by subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH). A logistic regression was used to establish a model for the receiver-operating characteristic (ROC) curve analysis, and the diagnostic efficacy of CTCs, CTECs, CEA, CA19-9, and their combinations was analyzed. RESULTS: The CTC (P < 0.0001) and CTEC (P=0.0009) level was significantly higher in CRC patients than that in healthy controls. For CRC patients, CTC and CTEC level was significantly correlated with tumor stage and lymph node metastasis status, but not with sex, age, tumor location, and degree of differentiation. The positive rate of CTCs, CTECs, CEA, and CA19-9 in CRC patients was 87.8%, 39.1%, 28.7%, and 26.1%, respectively. To distinguish CRC patients from controls, the area under the curve (AUC) of CTC was 0.889, which was much higher than 0.695 of CTEC, 0.696 of CEA, and 0.695 of CA19-9. Establishing ROC curve by logistic regression algorithm, the highest AUC was 0.935, which combined CTCs with CTEC, CEA, and CA19-9. CONCLUSIONS: CTCs combined with CTEC, CEA, and CA19-9 are useful to improve the diagnostic efficiency, which has high clinical significance in the diagnosis of colorectal cancer.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach.

BACKGROUND: To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). METHODS: A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis. RESULTS: Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity. CONCLUSION: The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Clinical exome sequencing identified POLB c.C1002A as a possible genetic cause in a family with hereditary cancer-predisposing syndrome.

This study recruited a Chinese family with hereditary cancer-predisposing syndrome. To investigate the causative mutations, disease-associated exome sequencing was conducted using peripheral blood of three members with malignant disease. As a result, three variants (PLD2 c. C1951T, RAB3GAP1 c.A701G and POLB c.C1002A) came out to be the potential candidate pathogenic mutations, which were not reported before. Sanger sequencing was used to validate the candidate variant in seven healthy members of this family. The candidate variant POLB c.C1002A was proved to co-segregate with malignant diseases, which was selected through a series of filtering criteria. This study thus identified POLB c.C1002A as a potential causative variant for hereditary cancer-predisposing syndrome.

RNA Sequencing Revealed Signals of Evolution From Gallbladder Stone to Gallbladder Carcinoma.

Gallbladder stone is a major risk factor for gallbladder carcinoma (GBC), while there is still a controversy whether period of follow-up since newly diagnoses of asymptomatic gallstones increases the risk of GBC. In this study, 10 GBC patients and 30 patients with gallstones were admitted to our hospital. Patients with gallstones were divided into 3 groups according to the follow-up time, involving 10 patients with follow-up period of 1-3 years (GS3 group), 10 patients with follow-up period of 5-10 years (GS5 group), and 10 patients with follow-up period of more than 10 years (GS10 group). Tumor and para-tumor tissues of GBC patients, and gallbladder tissues of gallstone patients were collected. RNA sequencing was performed on the 50 samples. Besides, 1,704 differentially expressed genes (DEGs) were identified in tumors compared with para-tumor tissues of 10 GBC patients, which were enriched into some well-known cancer-related pathways, such as PI3K-Akt, mitogen-activated protein kinase (MAPK), Ras, and Wnt signaling pathways, and the most significant pathway was neuroactive ligand-receptor interaction. Patients with gallstones with periods of follow-up equal to 1-3 and > 10 years showed to have higher cancer risk than those with 5-10 years. ALPP and GPR87 are potential biomarkers for predicting cancer risk in patients with gallstones. The in vitro results revealed that GPR-87 can promote the proliferation, migration, and invasion of GBC cells. Herein, we explored the relationship between GBC patients and patients with gallstones with different periods of follow-up in transcriptome level.

Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict COPD development. METHODS: A total of 441 COPD patients and 192 control subjects were recruited, and 101 single-nucleotide polymorphisms (SNPs) were determined using the MassArray assay. With 5 clinical features as well as SNPs, 6 predictive models were established and evaluated in the training set and test set by the confusion matrix AU-ROC, AU-PRC, sensitivity (recall), specificity, accuracy, F1 score, MCC, PPV (precision) and NPV. The selected features were ranked. RESULTS: Nine SNPs were significantly associated with COPD. Among them, 6 SNPs (rs1007052, OR = 1.671, P = 0.010; rs2910164, OR = 1.416, P < 0.037; rs473892, OR = 1.473, P < 0.044; rs161976, OR = 1.594, P < 0.044; rs159497, OR = 1.445, P < 0.045; and rs9296092, OR = 1.832, P < 0.045) were risk factors for COPD, while 3 SNPs (rs8192288, OR = 0.593, P < 0.015; rs20541, OR = 0.669, P < 0.018; and rs12922394, OR = 0.651, P < 0.022) were protective factors for COPD development. In the training set, KNN, LR, SVM, DT and XGboost obtained AU-ROC values above 0.82 and AU-PRC values above 0.92. Among these models, XGboost obtained the highest AU-ROC (0.94), AU-PRC (0.97), accuracy (0.91), precision (0.95), F1 score (0.94), MCC (0.77) and specificity (0.85), while MLP obtained the highest sensitivity (recall) (0.99) and NPV (0.87). In the validation set, KNN, LR and XGboost obtained AU-ROC and AU-PRC values above 0.80 and 0.85, respectively. KNN had the highest precision (0.82), both KNN and LR obtained the same highest accuracy (0.81), and KNN and LR had the same highest F1 score (0.86). Both DT and MLP obtained sensitivity (recall) and NPV values above 0.94 and 0.84, respectively. In the feature importance analyses, we identified that AQCI, age, and BMI had the greatest impact on the predictive abilities of the models, while SNPs, sex and smoking were less important. CONCLUSIONS: The KNN, LR and XGboost models showed excellent overall predictive power, and the use of machine learning tools combining both clinical and SNP features was suitable for predicting the risk of COPD development.