Hypoxia inhibits the iMo/cDC2/CD8+ TRMs immune axis in the tumor microenvironment of human esophageal cancer.

BACKGROUND: Esophageal cancer (ESCA) is a form of malignant tumor associated with chronic inflammation and immune dysregulation. However, the specific immune status and key mechanisms of immune regulation in this disease require further exploration. METHODS: To investigate the features of the human ESCA tumor immune microenvironment and its possible regulation, we performed mass cytometry by time of flight, single-cell RNA sequencing, multicolor fluorescence staining of tissue, and flow cytometry analyses on tumor and paracancerous tissue from treatment-naïve patients. RESULTS: We depicted the immune landscape of the ESCA and revealed that CD8+ (tissue-resident memory CD8+ T cells (CD8+ TRMs) were closely related to disease progression. We also revealed the heterogeneity of CD8+ TRMs in the ESCA tumor microenvironment (TME), which was associated with their differentiation and function. Moreover, the subset of CD8+ TRMs in tumor (called tTRMs) that expressed high levels of granzyme B and immune checkpoints was markedly decreased in the TME of advanced ESCA. We showed that tTRMs are tumor effector cells preactivated in the TME. We then demonstrated that conventional dendritic cells (cDC2s) derived from intermediate monocytes (iMos) are essential for maintaining the proliferation of CD8+ TRMs in the TME. Our preliminary study showed that hypoxia can promote the apoptosis of iMos and impede the maturation of cDC2s, which in turn reduces the proliferative capacity of CD8+ TRMs, thereby contributing to the progression of cancer. CONCLUSIONS: Our study revealed the essential antitumor roles of CD8+ TRMs and preliminarily explored the regulation of the iMo/cDC2/CD8+ TRM immune axis in the human ESCA TME.

Bioinformatics Analysis Reveals a Novel Prognostic Model for Esophageal Squamous Cell Carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC), a gastrointestinal cancer, is associated with poor prognosis. Prognostic models predict the likelihood of disease progression and are important for the management of patients with ESCC. The objective of this study was to develop a prognostic model for ESCC using bioinformatics analysis. Methods: Two transcriptome microarray Gene Expression Omnibus ESCC datasets (GSE53624 and GSE53622) were analyzed using bioinformatics methods. Differentially expressed genes (DEGs) were identified using the R package limma, and genes associated with survival outcomes in both datasets were identified by Kaplan-Meier analysis. Genes with diagnostic or prognostic value were selected for further analysis, and hazard ratios and their relationship with pathological TNM (pTNM) staging were investigated using univariate and multivariate Cox analysis. After selecting the independent factors from pTNM staging, Cox analysis and nomogram plotting were performed. The ability of the model to stratify risk and predict survival was evaluated and compared with the pTNM staging system to determine its potential clinical value. Key genes were analyzed by immunohistochemistry and RT-PCR. Results: Four candidate genes (B3GNT3, MACC1, NELL2, and USH1G) with prognostic value were identified from the two transcriptome microarray datasets. Age, pTNM stage, and B3GNT3, MACC1, and NELL2 were identified as independent factors associated with survival in the multivariate Cox analysis and used to establish a prognostic model. The model demonstrated significantly higher accuracy in predicting 3-year survival than the pTNM staging system and was useful for further risk stratification in patients with ESCC. B3GNT3 was significantly downregulated in ESCC tumor tissues and negatively associated with lymph node metastasis. Bioinformatics analysis indicated that B3GNT3 may play a role in immune regulation by regulating M2 macrophages. Conclusion: This study developed a new prognostic model for ESCC and identified B3GNT3 as a potential biomarker negatively associated with lymph node metastasis, which warrants further validation.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study.

Background: Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms. Methods: Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software. Results: A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

Enhanced efficacy of a novel aspirator in uniportal video-assisted thoracoscopic mediastinal lymph node dissection: a non-randomized prospective clinical trial.

Background: Video-assisted thoracoscopic surgery (VATS) is the standard approach in early-stage non-small cell lung cancer (NSCLC) and surgical aspirators play a crucial role. Traditional aspirators lack the ability to pull and lift tissue and cannot achieve optimal exposure. Therefore, we designed a new surgical aspirator that combined the function of thoracoscopic forceps. In this study, we aimed to validate the efficacy and safety of this new surgical aspirator. Methods: We performed a prospective non-randomized intervention trial and enrolled 504 consecutive patients scheduled for uniportal VATS in early NSCLC requiring mediastinal lymph node dissection. A novel aspirator we developed with a clamping function via a front pliers-like structure was implemented in intervention group, whereas traditional aspirator was used in control group. Time spent for nodal dissection in No. 2/4R and No. 7R/L (No. 7 lymph nodes resected through right or left side) lymph nodes and perioperative adverse events related to lymph node dissection were recorded. Mann-Whitey U test was applied to analyze sex and pathological type, an independent-samples t-test was applied to analyze surgery time and age. Results: In total, 250 of enrolled patients were allocated into traditional aspirator group and 254 of them were allocated into new aspirator group. Surgeons spent 544.71±120.80 (range, 332-917, median 541) seconds dissecting No. 2/4R lymph nodes with traditional aspirators and 507.54±100.00 (range, 348-702, median 520) seconds dissecting with new aspirators (P=0.008). The traditional aspirator group had an average surgery time of 507.11±104.61 (range, 310-785, median 510) seconds for No. 7R lymph nodes and 608.47±128.50 (range, 397-919, median 606) seconds for No. 7L lymph nodes, while that in the new aspirator group was 465.09±94.94 (range, 271-744, median 476) seconds (P=0.001) and 549.39±102.11 (range, 368-782, median 538) seconds (P<0.001). The new aspirator showed an efficacy advantage in mediastinal lymph node dissection in VATS, without additional risk. Conclusions: This is the first report about a new suction device combining the functions of both traditional surgical aspirators and forceps, which can effectively shorten the time of mediastinal lymph node dissection and improve the efficiency of thoracoscopic surgery without increasing lymph node dissection-related adverse events.

Preoperative serum bilirubin is an independent prognostic factor for curatively resected esophageal squamous cell carcinoma.

PURPOSE: This study examines prognostic value of preoperative serum bilirubin, including unconjugated bilirubin (UCB), conjugated bilirubin (CB), and total bilirubin (TB), in esophageal squamous cell carcinoma (ESCC) patients who underwent curative resection. METHODS: Between May 2010 and December 2012, a total of 351 ESCC patients were retrospectively reviewed. All the patients underwent curative resection as their primary treatment. Clinicopathological features and overall survival (OS) rate were investigated. Kaplan-Meier curves were used to calculate the OS rate, and the prognostic factors were identified by Cox regression model. Besides, the potential inhibition effect of UCB on ESCC was investigated with both in vitro and in vivo models. RESULTS: The higher-level groups of UCB, CB, and TB demonstrated longer OS than their low counterparts, with hazard ratio (HR) values of 0.567 (95% CI: 0.424-0.759), 0.698 (95% CI: 0.522-0.933), and 0.602 (95% CI: 0.449-0.807), respectively. All three forms of bilirubin were identified as independent prognostic factors for patients with ESCC, and they were found to effectively stratify the survival risk of patients at TNM stage III. In vivo and in vitro models further confirmed the inhibitory effect of unconjugated bilirubin (UCB) on the proliferation of ESCC. CONCLUSION: The findings of our study have shed new light on the prognostic value and biological functions of bilirubin in relation to ESCC. These results may contribute to a better understanding of the underlying mechanisms involved in ESCC tumorigenesis and provide potential therapeutic pathways for treating ESCC.

Simulation and experimental studies of debris penetrating skull.

Studying the critical kinetic energy (CKE) and critical specific kinetic energy (CSKE) of the debris penetrating the human skull is of great importance for the development of head protection devices. In general, obtaining human skull specimens and conducting direct penetration experiments is difficult. Finite element simulation, on the other hand, is an effective method for determining critical values. However, it is necessary to validate the modeling and simulation method before conducting simulations on the human skull. To validate the method, this study first established a finite element model of a pig skull and performed simulations of debris penetrating the skull. The modeling and simulation methods were verified by comparing the corresponding penetration experimental results on pig skulls with those of the simulations. As the modeling and simulation methods were indirectly validated, an anatomical human skull finite element model was developed to conduct simulations of spherical and cubic debris penetrating the skull to iteratively obtain the CKE and CSKE of the two pieces of debris. Finally, the influence of different attitudes of cubic debris on the penetration energy of the human skull was investigated.

Dorsal incision shows more efficacy and safety to traditional incision in single-portal video-assisted thoracoscopic surgery segmentectomy of LS9+10: a non-randomized prospective clinical trial.

Background: Segmentectomy is increasingly performed as a surgical technique. Traditional anterior axillary line and mid-axillary line video-assisted thoracoscopic surgery (VATS) incision meets difficulties when dealing with left segment 9+10 (LS9+10), as the distance and angle of view make it uneasy to expose the trachea, blood vessels and intersegment plane. As an alternative, dorsal incision may be advantageous as which faces the key structures of LS9+10, may facilitate the management of trachea and blood vessels of LS9+10, but there was no clinical proof reported ever. This study is targeted to compare the efficacy and safety of these two incisions in segmentectomy of LS9+10. Methods: The dorsal incision is made behind the posterior axillary line, 8th intercostal space. Patients with ground glass opacity (GGO) which solid ingredients is less than 25%, locates at LS9+10, and segmentectomy of LS9+10 could ensure the cut edge were enrolled in the study and were allocated to the traditional incision group or dorsal incision group based on the parity of hospital number. Efficacy outcomes such as the duration of surgery and safety outcomes including postoperative air-leakage duration, length of hospital stay, amount of bleeding and pain score were recorded. The Independent-samples t-test and Mann-Whitey U test were applied in data analysis. Results: A total of 68 patients were enrolled and allocated into the traditional incision group and dorsal incision group. In the traditional incision group, the average surgery time was 71.03±6.87 min (median 71.5 min), while that in the dorsal incision group was an average of 62.72±6.24 min (median 61.0 min, P=0.001). The postoperative duration of air-leakage was 2.16±1.63 and 1.36±1.33 days for traditional incision group and dorsal incision group (P=0.030), respectively. The traditional incision group had a greater length of postoperative hospital stay (3.69±1.36 days) than the dorsal incision group (3.08±1.03 days, P=0.041), when amount of bleeding and pain score showed no differences between these two groups. Data suggested a statistically significant advantage for the dorsal incision procedure. Conclusions: Dorsal incision can facilitate the segmentectomy of LS9+10, and significantly reduce the surgery time, postoperative duration of air-leakage and length of hospital stay.

Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation.

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma (ESCC) that combines plasma metabolomics with machine learning algorithms. Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls. The dataset was split into a training set and a test set. After identification of differential metabolites in training set, single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites. Finally, twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated. The predictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows: arachidonic acid (accuracy: 0.887), sebacic acid (accuracy: 0.867), indoxyl sulfate (accuracy: 0.850), phosphatidylcholine (PC) (14:0/0:0) (accuracy: 0.825), deoxycholic acid (accuracy: 0.773), and trimethylamine N-oxide (accuracy: 0.653). The prediction accuracies of the machine learning models in the test set were partial least-square (accuracy: 0.947), random forest (accuracy: 0.947), gradient boosting machine (accuracy: 0.960), and support vector machine (accuracy: 0.980). Additionally, survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor (hazard ratio (HR): 1.752), while PC (14:0/0:0) (HR: 0.577) was a favorable prognostic factor for ESCC. This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC.

Peripheral pure ground-glass opacity: segmentectomy versus wedge resection.

INTRODUCTION: Sublobar resection has been widely accepted for treating pure ground-glass opacities (GGOs). As GGOs have good prognosis, preserving postoperative pulmonary function is the major concern in surgery. No studies have yet compared the success rates of pulmonary function reservation between segmentectomy and wedge resection. METHOD: The three-dimensional rebuild of computed tomography (CT) images was performed, the segmentectomy and wedge resection of the GGO in the target segment were simulated, and the area of cut surface was measured, which was important data for successful postoperative pulmonary recruitment maneuvers. RESULT: With equal volumes of tissue removed, segmentectomy and wedge resection showed similar surface area loss for RS4 and RS5, followed by LS7 + 8, LS6 and LS1 + 2 segments. Compared with other segments, wedge resection performed in RS10, LS3, LS10, RS9 and RS7 may lead to a loss of lot more surface area than segmentectomy. CONCLUSION: Wedge resection is suggested for segments RS4, RS5, LS1 + 2 and LS7 + 8, whereas segmentectomy is advised for segments RS1, LS4 + 5 and RS2. Meanwhile, deep wedge resection should be avoided for segments RS8, RS7, RS10, LS3, LS10. RS9 and LS9, in order to preserve a larger lung surface area.

Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma.

Prognosis for esophageal squamous cell carcinoma (ESCC) is poor, so it is essential to develop a more complete understanding of the disease. The purpose of this study was to explore metabolic biomarkers and potential therapeutic targets for ESCC. An ultra-high-performance liquid chromatography coupled with high resolution mass (UPLC/MS)-based metabolomic analysis was performed in 141 ESCC cancerous tissue samples and 70 non-cancerous counterparts. The results showed that 41 differential metabolites were annotated in the training set, and 37 were validated in the test set. Single-metabolite-based receiver operating characteristic (ROC) curves as well as metabolite-based machine learning models, including Partial Least Squares (PLS), Support Vector Machine (SVM), and Random Forest (RF), were investigated for cancerous and non-cancerous tissue classification. Six most prevalent diagnostic metabolites-adenylsuccinic acid, UDP-GalNAc, maleylacetoacetic acid, hydroxyphenylacetylglycine, galactose, and kynurenine-showed testing predictive accuracies of 0.89, 0.95, 0.97, 0.89, 0.84, and 0.84, respectively. Moreover, the metabolite-based models (PLS, SVM, and RF) had testing predictive accuracies of 0.95, 0.95, and 1.00, respectively. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis demonstrated that 2-hydroxymyristoylcarnitine (HR: 0.55, 95 % CI: 0.32 to 0.92), 3-hydroxyhexadecanoylcarnitine (HR: 0.49, 95 % CI: 0.29 to 0.83), and 2,3-Dinor-TXB1 (HR: 0.56, 95 % CI: 0.33 to 0.95) to be significantly associated with OS. Based on the observation of accumulation in amino acids, immunohistochemistry (IHC) staining revealed that the amino acid transporters SLC7A5/LAT1, SLC1A5/ASCT2, and SLC16A10/MCT10 were up-regulated in ESCC cancerous tissues when compared to non-cancerous equivalents. Consistently, the same panel of amino acids were downregulated in cells with SLC1A5 knockdown. Herein, it is concluded that this study not only identified several metabolites with diagnostic and/or prognostic value, but also provided accurate metabolite-based prediction models for ESCC tissue classification. Furthermore, the three up-regulated amino acid transporters were identified as potential therapeutic targets for ESCC, especially SLC1A5.

Plasma-metabolite-based machine learning is a promising diagnostic approach for esophageal squamous cell carcinoma investigation / 药物分析学报

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma(ESCC) that combines plasma metabolomics with machine learning algorithms.Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls.The dataset was split into a training set and a test set.After identification of differential me-tabolites in training set,single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls.Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites.Finally,twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated.The pre-dictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows:arachidonic acid (accuracy:0.887),sebacic acid (accuracy:0.867),indoxyl sulfate (accuracy:0.850),phosphatidylcholine (PC) (14:0/0:0) (accuracy:0.825),deoxycholic acid(accuracy:0.773),and trimethylamine N-oxide (accuracy:0.653).The prediction accuracies of the ma-chine learning models in the test set were partial least-square (accuracy:0.947),random forest (accu-racy:0.947),gradient boosting machine (accuracy:0.960),and support vector machine (accuracy:0.980).Additionally,survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor(hazard ratio (HR):1.752),while PC (14:0/0:0) (HR:0.577) was a favorable prognostic factor for ESCC.This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC.

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.

Genomic Profiling and Prognostic Value Analysis of Genetic Alterations in Chinese Resected Lung Cancer With Invasive Mucinous Adenocarcinoma.

BACKGROUND: Invasive mucinous adenocarcinoma (IMA) of the lung is a distinct histological subtype with unique clinical and pathological features. Despite previous genomic studies on lung IMA, the genetic characteristics and the prognosis-related biomarkers in Chinese surgically resected lung IMA remain unclear. METHODS: We collected 76 surgically resected primary tumors of invasive lung adenocarcinoma, including 51 IMA and 25 non-mucinous adenocarcinomas (non-IMA). IMA was further divided into pure-IMA (mucinous features≥90%) and mixed-IMA subgroups. Comprehensive genomic profiling based on targeted next-generation sequencing (NGS) of 425 genes was explored and genomic characteristics were evaluated for the correlation with postoperative disease-free survival (DFS). RESULTS: IMA had a unique genetic profile, with more diverse driver mutations and more tumor drivers/suppressors co-occurrence than that of non-IMA. The frequency of EGFR (72.0% vs. 40.0% vs. 23.1%, p=0.002) and ALK (undetected vs. 20.0% vs. 26.9%, p=0.015) alterations showed a trend of gradual decrease and increase from non-IMA to mixed-IMA to pure-IMA, respectively. The frequency of KRAS mutations in pure-IMA was higher than that in mixed-IMA, albeit statistically insignificant (23.1% vs. 4.0%, p=0.10). TP53 mutation was significantly less in pure-IMA compared to mixed-IMA and non-IMA (23.1% vs. 52.0% vs. 56.0%, p=0.03). Besides, IMA exhibited less arm-level amplifications (p=0.04) and more arm-level deletions (p=0.004) than non-IMA, and the frequency of amplification and deletion also showed a trend of gradual decrease and increase from non-IMA to mixed-IMA to pure-IMA, respectively. Furthermore, prognosis analysis in stage III IMA patients showed that patients harboring alterations in EGFR (mDFS=30.3 vs. 16.0 months, HR=0.19, P=0.027) and PI3K pathway (mDFS=36.0 vs. 16.0 months, HR=0.12, P=0.023) achieved prolonged DFS, while patients with poorly differentiated tumors (mDFS=14.1 vs. 28.0 months, HR=3.75, p=0.037) or with KRAS mutations (mDFS=13.0 vs. 20.0 months, HR=6.95, p=0.027) had shorter DFS. Multivariate analysis showed that KRAS mutations, PI3K pathway alterations, and tumor differentiation status were independent factors that have statistically significant influences on clinical outcomes of IMA patients. CONCLUSION: Our study provided genomic insights into Chinese surgically resected lung IMA. We also identified several genomic features that may serve as potential biomarkers on postoperative recurrence in IMA patients with stage III disease.

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations.

Approximately half of the world's 500,000 new oesophageal squamous-cell carcinoma (ESCC) cases each year occur in China. Here, we show whole-genome sequencing of DNA and RNA in 94 Chinese individuals with ESCC. We identify six mutational signatures (E1-E6), and Signature E4 is unique in ESCC linked to alcohol intake and genetic variants in alcohol-metabolizing enzymes. We discover significantly recurrent mutations in 20 protein-coding genes, 4 long non-coding RNAs and 10 untranslational regions. Functional analyses show six genes that have recurrent copy-number variants in three squamous-cell carcinomas (oesophageal, head and neck and lung) significantly promote cancer cell proliferation, migration and invasion. The most frequently affected genes by structural variation are LRP1B and TTC28. The aberrant cell cycle and PI3K-AKT pathways seem critical in ESCC. These results establish a comprehensive genomic landscape of ESCC and provide potential targets for precision treatment and prevention of the cancer.

Cell-Free RNA Content in Peripheral Blood as Potential Biomarkers for Detecting Circulating Tumor Cells in Non-Small Cell Lung Carcinoma.

Circulating tumor cells (CTCs) have been implicated in tumor progression and prognosis. Techniques detecting CTCs in the peripheral blood of patients with non-small cell lung carcinoma (NSCLC) may help to identify individuals likely to benefit from early systemic treatment. However, the detection of CTCs with a single marker is challenging, owing to low specificity and sensitivity and due to the heterogeneity and rareness of CTCs. Herein, the probability of cell-free RNA content in the peripheral blood as a potential biomarker for detecting CTCs in cancer patients was investigated. An immunomagnetic enrichment of real-time reverse-transcription PCR (RT-PCR) technology for analysis of CTCs in NSCLC patients was also developed. The mRNA levels of four candidate genes, cytokeratin 7 (CK7), E74-like factor 3 (ELF3), epidermal growth factor receptor (EGFR), and erythropoietin-producing hepatocellular carcinoma receptor B4 (EphB4) that were significantly elevated in tumor tissues and peripheral blood mononuclear cells (PBMCs) were determined. The expression of CK7 and ELF3 in tumor tissues and EGFR in PBMCs was associated with lymph node metastasis (all p < 0.05). The expression of CK7 in PBMCs was correlated with age and EphB4 in PBMCs correlated with histopathological type, respectively (all p < 0.05). The expression of all four genes in tumor tissues and PBMCs was significantly correlated with the clinical stage (all p < 0.01). Survival analysis showed that the patients with enhanced expression of CK7, ELF3, EGFR, and EphB4 mRNA in PBMCs had poorer disease-free survival (DFS) and overall survival (OS) than those without (all p < 0.0001). The present study showed that this alteration of cell-free RNA content in peripheral blood might have clinical ramifications in the diagnosis and treatment of NSCLC patients.

Circulating miRNAs is a potential marker for gefitinib sensitivity and correlation with EGFR mutational status in human lung cancers.

miRNA expression is deregulated in non-small cell lung cancer (NSCLC), and some miRNAs are associated with gefitinib sensitivity. Here, we investigated if circulating miRNAs could be a useful biomarker for the prediction of EGFR mutation and the patient's prognosis. The differential miRNAs related to gefitinib sensitivity were screened and identified by microRNA array. Using Taqman-based real-time RT-PCR, we analyzed the expression of selected miRNAs in tumor tissues and plasma of 150 NSCLC patients. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to determine the association between miRNAs expression and survival. Receiver operating characteristic curve analysis was also performed. Compared with PC9 cell line, 41 microRNAs detected by microarray were significantly differentially expressed in A549 and H1299 cells. The 5 selected hsa-miRNAs were all found differently expressed between wild and mutant EGFR carriers (all P<0.01). Down-regulation of 5 selected miRNAs were independently associated with lymphatic invasion (all P<0.01) and clinical stage (all P<0.01), respectively. Both down-regulation of has-miR-195 (P=0.012) and has-miR-21 (P=0.004) were associated with poor differentiation. All up-regulation of 5 has-miRNAs were associated with smoking (All P<0.05). 5 hsa-miRNAs were up-regulated both in plasma and tissue samples. A model including 4 hsa-miRNAs may predict EGFR mutational status and gefitinib-sensitivity (both AUC: 0.869). Plasma levels of has-miR-125b expression were associated with disease-free survival (P=0.033) and overall survival in the patients (P=0.028). In a word, Circulating 5 selected miRNAs may especially be useful in predicting EGFR mutation, and circulating hsa-miR-125b may have prognostic values in NSCLC patients.