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Background: Esophageal cancer (EC) is one of the most aggressive and lethal malignancies in the world. The quantity and distribution of immune cells are very important factors in determining cancer. Tumorinfiltrating mast cells (TIM) are a class of immune cells with an important immune regulation function for tumor progression. However, tumor-infiltrating immune cells (TIICs) and their role in EC have not yet been investigated. Methods: The RNA-seq data of an EC cohort were downloaded from The Cancer Genome Atlas (TCGA) website. In this study, we used the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to compare different soakage of inflammatory cells in esophageal squamous cell carcinoma (ESCC) and normal tissue. Kaplan-Meier survival analysis was performed on different immune cell subpopulations and overall survival (OS) in 22 human immune cell phenotypes.Immunohistochemistry (IHC) was also carried out using our clinical tissue samples.Results: The proportion of tumor-infiltrating mast cells (TIM) significantly increased at the late of EC and a high percentage of mast cells indicated a poor OS of EC patients in TCGA database. The IHC staining of tryptase revealed that high level of TIM expression was an independent prognostic factor of survival time in the ESCC patients in our database. In addition, TIM accumulation and infiltration of CD8+T cells were shown to be negatively correlated.Conclusions: This work revealed that TIM are related to prognosis in patients with EC and TIM may be an independent prognostic factor for EC.

Background. Controlling nutritional status (CONUT) and tumor markers are associated with prognosis in patients with non-small-cell lung cancer (NSCLC). This study is aimed at exploring the potential usefulness of T-CONUT, constructed by combining CONUT and tumor markers, for NSCLC patients undergoing radical surgery. Methods. A total of 483 patients with NSCLC underwent radical surgical resection. The receiver characteristic operating curve (ROC) was used to select the tumor marker with the highest predictive performance, and CONUT was combined with this marker to construct the T-CONUT. The Kaplan–Meier method and log-rank test were used to analyze the overall survival (OS), and chi-square analysis was used to analyze the association between T-CONUT and clinicopathological characteristics. The independent risk factors were analyzed by Cox regression. A nomogram was constructed by R studio. Calibration plots, the c -index, and decision curves were evaluated for the performance of the nomogram. Results. ROC analysis showed that the predictive performance of CYFRA21–1 was better than that of CEA, NSE, and SCC. CYFRA21–1 was selected for combining with CONUT to construct T-CONUT. Elevated T-CONUT indicates poor prognosis of patients. Histological type, pTNM, and T-CONUT are independent risk factors associated with patient prognosis. The areas under the curve of the nomogram for predicting 3- and 5-year OS were 0.760 and 0.761, respectively. Conclusion. T-CONUT comprising CYFRA21–1 and CONUT can effectively predict the prognosis of NSCLC patients.

Background Activated Cdc42-associated kinase 1 (ACK1) is a promising druggable target for cancer, but its inhibitors only showed moderate effects in clinical trials. The study aimed to investigate the underlying mechanisms and improve the antitumor efficacy of ACK1 inhibitors. Methods RNA-seq was performed to determine the downstream pathways of ACK. Using Lasso Cox regression analysis, we built a risk signature with ACK1-related autophagy genes in the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) project. The performance of the signature in predicting the tumor immune environment and response to immunotherapy and chemotherapy were assessed in LUAD. CCK8, mRFP-GFP-LC3 assay, western blot, colony formation, wound healing, and transwell migration assays were conducted to evaluate the effects of the ACK1 inhibitor on lung cancer cells. A subcutaneous NSCLC xenograft model was used for in vivo study. Results RNA-seq revealed the regulatory role of ACK1 in autophagy. Furthermore, the risk signature separated LUAD patients into low- and high-risk groups with significantly different prognoses. The two groups displayed different tumor immune environments regarding 28 immune cell subsets. The low-risk groups showed high immune scores, high CTLA4 expression levels, high immunophenoscore, and low DNA mismatch repair capacity, suggesting a better response to immunotherapy. This signature also predicted sensitivity to commonly used chemotherapy and targeted drugs. In vitro, the ACK1 inhibitors (AIM-100 and Dasatinib) appeared to trigger adaptive autophagy-like response to protect lung cancer cells from apoptosis and activated the AMPK/mTOR signaling pathway, partially explaining its moderate antitumor efficacy. However, blocking lysosomal degradation with chloroquine/Bafilamycine A1 or inhibiting AMPK signaling with compound C/shPRKAA1 enhanced the ACK1 inhibitor’s cytotoxic effects on lung cancer cells. The efficacy of the combined therapy was also verified using a mouse xenograft model. Conclusions The resulting signature from ACK1-related autophagy genes robustly predicted survival and drug sensitivity in LUAD. The lysosomal degradation inhibition improved the therapeutic effects of the ACK1 inhibitor, suggesting a potential role for autophagy in therapy evasion.