A novel prognosis prediction of esophageal cancer based on chromatin regulator-related lncRNA.

It has been reported that chromatin regulators (CRs), as one of the essential upstream regulators of tumor development, were screened to construct a prognostic model for predicting the outcome of tumor patients. However, the prognostic model based on CRs-related long noncoding RNAs (lncRNAs) in esophageal cancer (EC) has never been researched. This study aims to construct a novel CRs-related lncRNA signature to evaluate the prognostic ability of EC patients. We obtained the transcriptome data and clinical information of patients with EC from the Cancer Genome Atlas database, 870 CRs-related genes from previous topic research. Univariate, multivariate Cox, the least absolute shrinkage and selection operator regression analyses were used to establish the risk model. The receiver operating characteristic curve, principal component analysis, nomogram, quantitative real-time PCR were performed to evaluate the independence and accuracy of the model. The biological functions and immune microenvironment of the risk model were analyzed by gene set enrichment analyses and R softwares. A novel 3 CRs-related lncRNAs risk model composed of AC079684.1, TMEM75, LINC00365, as an independent and superior factor, was established for prognosis prediction of EC patients. Quantitative real-time PCR analysis verified upregulated AC079684.1 and TMEM75 mRNA levels and downregulated LINC00365 mRNA level in EC tissues compared with normal tissues. Gene set enrichment analysis analysis displayed Kyoto encyclopedia of genes and genomes and gene ontology pathways enriched in risk groups, such as focal adhesion, pathways in cancer, epidermal cell differentiation. Immune cells and immune checkpoints were more likely to be activated in the high-risk group. Finally, we found most of the compounds in the high-risk group exhibited higher sensitivity through therapeutic drug screening. The 3 CRs-related lncRNAs risk model could independently predict the prognosis of EC and provide immunotherapy guidance for patients with EC.

One-stage VATS surgery for synchronous bilateral lung lesion: a safe and feasible procedure.

BACKGROUND: Synchronous bilateral lung lesions are emerging as a common but tricky disease for surgical management. Whether one or two-stage surgery should be taken remains in debate. We retrospectively analysed 151 patients who underwent one and two-stage Video Assisted Thoracic Surgery (VATS) to investigate the safety and feasibility of the two surgical approaches. METHODS: A total of 151 patients were included in the study. Propensity score matching was performed to minimize the baseline characteristics difference between one and two-stage groups. Clinical factors including in-hospital days after surgery, chest tube drainage days, types and severity of post-operative complications were compared between the two groups. Logistic univariate and multivariate analyses were used to find the risk factors for post-operative complications. Nomogram was built to select the low risk candidates for the one-stage VATS. RESULTS: After propensity score matching, 36 one-stage and 23 two-stage patients were enrolled. The age (p = 0.669), gender (p = 0.3655), smoking status (p = 0.5555), pre-operative comorbidity (p = 0.8162), surgical resection (p = 0.798) and lymph node dissection (p = 9036) were balanced between the two groups. There was no difference in post-surgery hospital days (8.67 ± 2.68 versus 8.46 ± 2.92, p = 0.7711) and chest tube retaining days (5.47 ± 2.20 versus 5.46 ± 1.95, p = 0.9772). Moreover, post-operative complications also showed no difference between one-stage and two-stage groups (p = 0.3627). Univariate and multivariate analysis revealed that advanced age (p = 0.0495), pre-surgical low haemoglobin (p = 0.045) and blood loss (p = 0.002) were risk factors for post-operative complications. Nomogram built with the three risk factors showed reasonable predictive value. CONCLUSIONS: One-stage VATS for synchronous bilateral lung lesion patients was proved to be a safety procedure. Advanced age, pre-surgical low haemoglobin and blood loss may predict complications after surgery.

Adjuvant EGFR-TKI therapy in resected EGFR-mutation positive non-small cell lung cancer: A real-world study.

Background: Although several clinical studies have laid the foundation for the adjuvant application of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), some questions remain unresolved. This real-world study aimed to address questions such as the effect of adjuvant chemotherapy prior to adjuvant EGFR-TKI therapy on survival outcomes, and the duration of adjuvant EGFR-TKI therapy, etc. Methods: Between October 2005 and October 2020, 227 consecutive patients with non-small cell lung cancer (NSCLC) who underwent complete pulmonary resections were included in this retrospective study. Patients received postoperative adjuvant chemotherapy followed by EGFR-TKI or adjuvant EGFR-TKI monotherapy. The disease-free survival (DFS) and overall survival (OS) were evaluated. Results: Of the total 227 patients, 55 (24.2%) patients underwent 3-4 cycles of chemotherapy prior to receiving adjuvant EGFR-TKI therapy. The 5-year DFS rate was 67.8%, while the 5-year OS rate was 76.4%. The stages were significantly associated with both DFS (P<0.001) and OS (P<0.001), while no significant differences were observed in the DFS (P=0.093) and OS (P=0.399) between the adjuvant chemotherapy followed by EGFR-TKI and adjuvant EGFR-TKI monotherapy groups. A longer duration of EGFR-TKI therapy was associated with better DFS (P<0.001) and OS (P<0.001) benefit. Additionally, pTNM stage and duration of EGFR-TKI therapy were considered independent prognostic factors for long-term survival (All P<0.05). Conclusions: This study supports the use of EGFR-TKI as a postoperative adjuvant treatment for patients with stage II-IIIA EGFR-mutation positive NSCLC. Additionally, patients with stage I who had pathological risk factors were also suitable for receiving adjuvant EGFR-TKI therapy. Postoperative EGFR-TKI based, chemotherapy-free adjuvant regimen may be a potential therapeutic option for patients with EGFR-mutation positive NSCLC.

PEG-rhG-CSF for prophylaxis of neutropenia after chemotherapy in patients with non-small cell lung cancer: A multicenter, prospective, randomized study.

BACKGROUND: To evaluate the efficacy and safety of pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) in preventing neutropenia during multiple cycles of chemotherapy in patients with non-small cell lung cancer (NSCLC). METHOD: In a multicenter, prospective, randomized trial, patients with NSCLC were randomly assigned in a 2:1 ratio to treatment group (PEG-rhG-CSF as primary prophylactic therapy) or control group. Patients in the control group were administered rhG-CSF when white blood cell count was <2.0 × 109 /L or absolute neutrophil count <1.0 × 109 /L. The primary endpoint was the incidence of grade 3/4 neutropenia. Secondary endpoints included the incidence and duration of grade 3/4 neutropenia in each cycle, the incidence of febrile neutropenia (FN), delay rate of chemotherapy, prolonged time of chemotherapy, and safety. RESULTS: Between January 2019 and July 2021, 130 patients were enrolled (treatment group: n = 87, control group: n = 43). The incidence of grade 3/4 neutropenia in the treatment group was significantly lower than that in the control group (1.15% vs. 11.63%, p < 0.05). The mean duration of grade 3/4 neutropenia for the treatment and control group was 2.00 and 3.75 days, respectively. There were no statistical differences in the incidence of FN, delay rate of chemotherapy, prolonged time of chemotherapy, and antibiotic use between the two groups (all p > 0.05). Adverse events were reported in 47.13% of patients in the treatment group and 48.84% patients in the control group. CONCLUSIONS: Primary prophylactic treatment with PEG-rhG-CSF could reduce the incidence of neutropenia in patients with NSCLC during multiple cycles of chemotherapy, with acceptable safety and tolerability.

CAF-derived exosomes deliver LINC01410 to promote epithelial-mesenchymal transition of esophageal squamous cell carcinoma.

Exosomes mediate cellular communications in cancer by transmitting active molecules. However, the CAFs-derived molecular determinants that regulate esophageal squamous cell carcinoma (ESCC) metastasis have not been fully characterized. The purpose of this study was to investigate the potential roles of exosomal LINC01410 of ESCC cells. The characteristics of exosomes were identified using transmission electron microscope (TEM), Nanoparticle Tracking Analysis (NTA). The expression of LINC01410 and miR-122-5p was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) assay. The biological roles of LINC01410 in ESCC cells were investigated using transwell assay. Western blot assay was employed to detect protein levels. The potential downstream molecular mechanism of LINC01410 was demonstrated with dual-luciferase reporter assay, RNA immunoprecipitation (RIP) assay and RNA pull down. CAFs promote the metastasis and epithelial-mesenchymal transition (EMT) of ESCC cells. CAFs exert their roles by transferring exosomes to ESCC cells, leading to a significant increase of LINC01410 level in ESCC cells. Mechanically, LINC01410 secreted by CAFs-Exo could contribute to metastasis and EMT by sponging miR-122-5p and increasing PKM2 level in TE-1 and Eca-109 cells. Additionally, LINC01410/miR-122-5p/PKM2 axis affecting ESCC metastasis and EMT in vitro and in vivo.

Deep Learning Based on Hierarchical Self-Attention for Finance Distress Prediction Incorporating Text.

To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, digitalization of lengthy but indispensable textual disclosure, such as Management Discussion and Analysis (MD&A), has been emphasized by researchers. However, most studies divide the long text into words and count words to treat the text as word count vectors, bringing massive invalid information but ignoring meaningful contexts. Aiming to efficiently represent the text of large size, an end-to-end neural networks model based on hierarchical self-attention is proposed in this study after the state-of-the-art pretrained model is introduced for text embedding including contexts. The proposed model has two notable characteristics. First, the hierarchical self-attention only affords the essential content with high weights in word-level and sentence-level and automatically neglects lots of information that has no business with risk prediction, which is suitable for extracting effective parts of the large-scale text. Second, after fine-tuning, the word embedding adapts the specific contexts of samples and conveys the original text expression more accurately without excessive manual operations. Experiments confirm that the addition of text improves the accuracy of financial distress forecasting and the proposed model outperforms benchmark models better at AUC and F2-score. For visualization, the elements in the weight matrix of hierarchical self-attention act as scalers to estimate the importance of each word and sentence. In this way, the "red-flag" statement that implies financial risk is figured out and highlighted in the original text, providing effective references for decision-makers.

An adaptive denoising method based on local mode estimation.

An adaptive method is presented in this paper to cope with gaussian noise through local structure estimation. Even regions and uneven regions are distinguished firstly, then each uneven region is further segmented into two subregions by fuzzy c-means clustering algorithm and fisher discriminant analysis. Finally, linear approximation is used to estimate the intensities of each region. With the introduction of local structure analysis, gaussian noise is reduced effectively with edges preserved very well. Experimental results also demonstrate its effectiveness in terms of medical images enhancement, while it is also applicable to other similar images that have many tiny details.