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IntroductionEsophagectomy patients who experience unplanned ICU admission (UIA) may experience a heavier economic burden and worse clinical outcomes than those who experience routine intensive care unit (ICU) admission. The aim of this study was to identify the risk factors for postoperative UIA in patients who underwent esophagectomy.MethodsWe retrospectively included patients with esophageal cancer who underwent esophagectomy. The characteristics of postoperative UIA were described, and univariable and multivariable analyses were performed based on the logistic regression model. Furthermore, a recursive partitioning analysis was adopted to stratify the patients according to the risk of UIA.ResultsA total of 628 patients were included in our final analysis, among whom 57 (9.1%) had an UIA. The patients in the UIA cohort had a higher rate of in-hospital mortality (P

Given their good antitumor effects, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are standard first-line therapy for EGFR-sensitive mutations, including exon 19 deletions and exon 21 L858R mutations. EGFR fusion mutations and EGFR amplification are very rare in non-small cell lung cancer (NSCLC). We describe 2 patients with NSCLC harboring EGFR fusion mutations (EGFR-MACF1 and EGFR-GNAT3) combined with EGFR amplification. Both patients received EGFR-TKI treatment, and 1 of them showed an antitumor response.

Background and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.Materials and methodsA total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.ResultsThe radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.ConclusionWe established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features.

Third-generation tyrosine kinase inhibitors (TKIs), such as osimertinib, almonertinib and furmonertinib, overcome the mechanisms of resistance to first-generation inhibitors (such as gefitinib, erlotinib and icotinib) by incorporating an acrylamide group that alkylates the Cys797 of EGFR T790M. However, drug resistance is inevitable, even for third-generation TKIs. Screening for drug-resistant mutations by repeat biopsy and repeat gene sequencing is necessary after TKI treatment. Among various third-generation TKI-resistant mutations, secondary mutation of the L718 residue of EGFR exon 18 was found in approximately 8% of patients and is responsible for drug resistance in vitro and in vivo. Furthermore, there is limited clinical experience of targeted therapy for this mutation. Herein, we report for the first time that afatinib and cetuximab combination therapy can overcome such drug resistance.

BackgroundLung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.MethodsThe gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.ResultsWe constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (p

BackgroundAtelectasis is a common complication of lung cancer, and there are few reports about the treatment methods. This study retrospectively analyzed the safety and effectiveness of endotracheal metal stent implantation combined with arterial infusion chemoembolization in the treatment of non-small cell lung cancer with complete atelectasis.MethodsThe clinical data of patients with non-small cell lung cancer and complete atelectasis treated by self-expandable metallic stent implantation combined with arterial infusion chemotherapy were retrospectively analyzed. The clinical efficacy was evaluated and postoperative adverse reactions were observed. Progression-free survival and overall survival were analyzed by Kaplan-Meier method.ResultsIn all, 42 endotracheal metallic stents were implanted in 42 patients under fluoroscopy. 5–7 days after stent implantation, CT showed that 24 patients (57.1%) had complete lung recruitment, and that 13 (31.0%) had partial lung recruitment. The technical success rate was 100%, and the clinical success rate was 88.1% (37/42). 5–7 days after stent implantation, bronchial artery infusion chemoembolization was performed in all patients. The median progression-free survival and overall survival were 6 months (95% CI: 2.04-9.66) and 10 months (95% CI: 7.22-12.79), respectively.ConclusionSelf-expandable metallic stent implantation combined with arterial infusion chemoembolization may be an effective and safe strategy in the treatment of lung cancer with atelectasis clinically.

Mesenchymal stem cells (MSCs) have tumor-homing ability and play critical roles in tumor treatment, but their dual influences on tumor progression limit their therapeutic applications. Exosomes derived from MSCs (MSC-exosomes) exhibit great potential in targeted tumor treatment due to their advantages of high stability, low immunogenicity, good biocompatibility, long circulation time and homing characteristics. Furthermore, the artificial modification of MSC-exosomes could amplify their advantages and their inhibitory effect on tumors and could overcome the limit of tumor-promoting effect. In this review, we summarize the latest therapeutic strategies involving artificially modified MSC-exosomes in tumor treatment, including employing these exosomes as nanomaterials to carry noncoding RNAs or their inhibitors and anticancer drugs, and genetic engineering modification of MSC-exosomes. We also discuss the feasibility of utilizing artificially modified MSC-exosomes as an emerging cell-free method for tumor treatment and related challenges.

N6-Methyladenosine (m6A), a pervasive posttranscriptional modification which is reversible, has been among hotspot issues in the past several years. The balance of intracellular m6A levels is dynamically maintained by methyltransferase complex and demethylases. Meanwhile, m6A reader proteins specifically recognize modified residues and convey messages so as to set up an efficient and orderly network of m6A regulation. The m6A mark has proved to affect every step of RNA life cycle, from processing in nucleus to translation or degradation in cytoplasm. Subsequently, disorders in m6A methylation are directly related to aberrant RNA metabolism, which results in tumorigenesis and altered drug response. Therefore, uncovering the underlying mechanism of m6A in oncogenic transformation and tumor progression seeks opportunities for novel targets in cancer therapy. In this review, we conclude the extensive impact of m6A on RNA metabolism and highlight its relevance with human cancer, implicating the far-reaching value in clinical application.