Risk of Acute Lung Injury after Esophagectomy.

To develop a new approach for identifying acute lung injury (ALI) in surgical ward setting and to assess incidence rate, clinical outcomes, and risk factors for ALI cases after esophagectomy. We also compare the degree of lung injury between operative and non-operative sides. Consecutive esophageal cancer patients (n=1022) who underwent esophagectomy from Dec 2012 to Nov 2018 in our hospital were studied. An approach for identifying ALI was proposed that integrated radiographic assessment of lung edema (RALE) score to quantify degree of lung edema. Stepwise logistic regression identified risk factors for postoperative ALI incidence. The degree of bilateral lung injury was compared using the RALE score. The approach for identifying ALI in surgical ward setting was defined as acute onset, PaO2/FiO2≤300 mmHg, bilateral opacities on bedside chest radiograph with a RALE score≥16, and exclusion of cardiogenic pulmonary edema. Incidence rate of ALI was estimated to be 9.7%. ALI diagnosis was associated with multiple clinical complications, prolonged hospital stay, higher medical bills, and higher perioperative mortality. Nine risk factors including BMI, ASA class, DLCO%, duration of surgery, neutrophil percentage, high-density lipoprotein, and electrolyte disorders were identified. The RALE score of the lung lobes of the operative side was higher than the non-operative side. A new approach for identifying ALI in esophageal cancer patients receiving esophagectomy was proposed and several risk factors were identified. ALI is common and has severe outcomes. The lung lobes on the operative side are more likely to be affected than the non-operative side.

Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological characteristics for lung cancer screening in patients with small pulmonary nodules (SPN). METHODS: This study included three data sets. First, a CNN model was developed and tested on data set 1. Then, a hybrid prediction model was developed on data set 2 by multivariable binary logistic regression analysis. We combined the CNN model score and the selected epidemiological risk factors, and a risk prediction nomogram was presented. An independent multicenter cohort was used for model external validation. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: The final hybrid model included the CNN model score and the screened risk factors included age, gender, smoking status and family history of cancer. The nomogram showed good discrimination and calibration with an area under the curve (AUC) of 91.6% (95% CI: 89.4%-93.5%), compare with the CNN model, the improvement was significance. The performance of the nomogram still showed good discrimination and good calibration in the multicenter validation cohort, with an AUC of 88.3% (95% CI: 83.1%-92.3%). CONCLUSIONS: Our study showed that epidemiological characteristics should be considered in lung cancer screening, which can significantly improve the efficiency of the artificial intelligence (AI) model alone. We combined the CNN model score with Asian lung cancer epidemiological characteristics to develop a new nomogram to facilitate and accurately perform individualized lung cancer screening, especially for Asians.

Desmoglein 3 and Keratin 14 for Distinguishing Between Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the leading major histological phenotypes of all non-small cell lung cancer (NSCLC). In this study, the candidate genes and the potential tumorigenesis distinguishing between LUAD and LUSC were analyzed. METHODS: The present study investigated two microarray datasets (GSE28571 and GSE10245) downloaded from the Gene Expression Omnibus (GEO) database. A protein-protein interaction (PPI) network was applied to screen out the candidate genes. In addition, differently expressed genes (DEGs) between lung adenocarcinoma and lung squamous cell carcinoma of the two datasets were functionally analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. R 4.0.2 was used to perform Kaplan-Meier analysis of DSG3 (desmoglein 3) and KRT14 (keratin 14) by analyzing the expression and clinical data from The Cancer Genome Atlas (TCGA) database. RESULTS: The results revealed that 47 DEGs of the two datasets were ascertained in our study. It was found that the DEGs were mainly involved in pathways related to p63 transcription factor network and validated transcriptional factor targeting TAp63, etc. Based on the analysis, we finally identified DSG3 and KRT14 as potential biomarkers for distinguishing between LUAD and LUSC. These results suggested that DSG3 and KRT14 could have the potential to play an important role in NSCLC patients, as diagnostic markers. At the same time, DSG3 or KRT14 indicated a worse prognosis in LUSC patients, which were associated with pathways relevant to the TRAIL signaling pathway and TNF receptor signaling pathway according to bioinformatic analysis. CONCLUSION: The DSG3 and KRT14 have the potential to be used as diagnostic markers, which presented here may facilitate improvements in distinguishing between LUAD and LUSC in advanced NSCLC patients.

Identification of a putative competitive endogenous RNA network for lung adenocarcinoma using TCGA datasets.

The mechanisms underlying the oncogenesis and progression of lung adenocarcinoma (LUAD) are currently unclear. The discovery of competitive endogenous RNA (ceRNA) regulatory networks has provided a new direction for the treatment and prognosis of patients with LUAD. However, the mechanism of action of ceRNA in LUAD remains elusive. In the present study, differentially expressed mRNAs, microRNAs (miRs) and long non-coding RNAs from the cancer genome atlas database were screened. CeRNAs for LUAD were then identified using online prediction software. Among the ceRNAs identified, family with sequence similarity 83 member A (FAM83A), miR-34c-5p, KCNQ1OT1 and FLJ26245 were observed to be significantly associated with the overall survival of patients with LUAD. Of note, FAM83A has potential significance in drug resistance, and may present a candidate biomarker for the prognosis and treatment of patients with LUAD.

Identification of DNA methylation-driven genes in esophageal squamous cell carcinoma: a study based on The Cancer Genome Atlas.

BACKGROUND: Aberrant DNA methylations are significantly associated with esophageal squamous cell carcinoma (ESCC). In this study, we aimed to investigate the DNA methylation-driven genes in ESCC by integrative bioinformatics analysis. METHODS: Data of DNA methylation and transcriptome profiling were downloaded from TCGA database. DNA methylation-driven genes were obtained by methylmix R package. David database and ConsensusPathDB were used to perform gene ontology (GO) analysis and pathway analysis, respectively. Survival R package was used to analyze overall survival analysis of methylation-driven genes. RESULTS: Totally 26 DNA methylation-driven genes were identified by the methylmix, which were enriched in molecular function of DNA binding and transcription factor activity. Then, ABCD1, SLC5A10, SPIN3, ZNF69, and ZNF608 were recognized as significant independent prognostic biomarkers from 26 methylation-driven genes. Additionally, a further integrative survival analysis, which combined methylation and gene expression data, was identified that ABCD1, CCDC8, FBXO17 were significantly associated with patients' survival. Also, multiple aberrant methylation sites were found to be correlated with gene expression. CONCLUSION: In summary, we studied the DNA methylation-driven genes in ESCC by bioinformatics analysis, offering better understand of molecular mechanisms of ESCC and providing potential biomarkers precision treatment and prognosis detection.

Impact of visceral pleural invasion on the association of extent of lymphadenectomy and survival in stage I non-small cell lung cancer.

Visceral pleural invasion (VPI) has been identified as an adverse prognostic factor for non-small cell lung cancer (NSCLC). Accurate nodal staging for NSCLC correlates with improved survival, but it is unclear whether tumors with VPI require a more extensive lymph nodes (LNs) dissection to optimize survival. We aimed to evaluate the impact of VPI status on the optimal extent of LNs dissection in stage I NSCLC, using the Surveillance, Epidemiology, and End Results (SEER) database. We identified 9297 surgically treated T1-2aN0M0 NSCLC patients with at least one examined LNs. Propensity score matching was conducted to balance the baseline clinicopathologic characteristics between the VPI group and non-VPI group. Log-rank tests along with Cox proportional hazards regression methods were performed to evaluate the impact of extent of LNs dissection on survival. VPI was correlated with a significant worse survival, but there was no significant difference in survival rate between PL1 and PL2. Patients who underwent sublobectomy had slightly decreased survival than those who underwent lobectomy. Pathologic LNs examination was significantly correlated with survival. Examination of 7-8 LNs and 14-16 LNs conferred the lowest hazard ratio for T1-sized/non-VPI tumors (stage IA) and T1-sized/VPI tumors (stage IB), respectively. The optimal extent of LNs dissection varied by VPI status, with T1-sized/VPI tumors (stage IB) requiring a more extensive LNs dissection than T1-sized/non-VPI tumors (stage IA). These results might provide guidelines for surgical procedure in early stage NSCLC.

Circ-ZKSCAN1 regulates FAM83A expression and inactivates MAPK signaling by targeting miR-330-5p to promote non-small cell lung cancer progression.

BACKGROUND: Circular RNAs (circRNAs) belong to a new type of endogenous non-coding RNA and plays a key role in carcinogenesis. Circ-ZKSCAN1 (hsa_circ_0001727) has been proven to be a tumor-dependent circRNA. However, its role in non-small cell lung cancer (NSCLC) has been underreported. METHODS: The expression patterns of circ-ZKSCAN1 were determined using qRT-PCR in NSCLC samples and cell lines. Cell proliferation was examined utilizing the CCK-8 assay. Cell migration and invasion were evaluated using the Transwell assay. The combination of circ-ZKSCAN1 and miR-330-5p in NSCLC cells was analyzed by RNA pull-down and luciferase reporter assay. We used the bioinformatics software circbank, CircInteractome, TargetScan and Miranda to predict circRNA-miRNA and miRNA-mRNA interactions. RESULTS: Our results showed that circ-ZKSCAN1 was significantly up-regulated in NSCLC, closely related to malignant characteristics and poor prognosis, and clinically related to tumor size and clinical stage. Subsequent experiments showed that circ-ZKSCAN1 could inhibit the growth of NSCLC cells in vitro and in vivo. Importantly, circ-ZKSCAN1 can act as a sponge of carcinogenic miR-330-5p to increase the expression of FAM83A, resulting in the inhibition of MAPK signal transduction pathway, thus promoting the progress of NSCLC. Interestingly, the increase in FAM83A expression caused by circ-ZKSCAN1 overexpression could in turn promote the expression of circ-ZKSCAN1. CONCLUSIONS: Circ-ZKSCAN1 is a key positive regulator of NSCLC, and clarifies the potential molecular mechanism of the new circ-ZKSCAN1/miR-330-5p/FAM83A feedback loop in promoting the progress of NSCLC.