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The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17‐gene prognosis‐related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine‐learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C‐index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single‐cell RNA‐seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high‐risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC. [ABSTRACT FROM AUTHOR]

Currently, therapy response cannot be accurately predicted in HER2-negative breast cancer (BC). Measuring stromal tumour-infiltrating lymphocytes (sTILs) and mediators of the tumour microenvironment and characterizing tumour-infiltrating immune cells (TIICs) may improve treatment response in the neoadjuvant setting. Tumour tissue and peripheral blood samples were retrospectively collected from 118 patients, and sTILs were evaluated. Circulating exosomes and myeloid-derived suppressor cells were determined by flow cytometry. TIICs markers (CD4, CD8, CD20, CD1a, and CD68) were assessed immunohistochemically. High sTILs were significantly associated with pathological complete response (pCR; p = 0.048) and event-free survival (EFS; p = 0.027). High-CD68 cells were significantly associated with pCR in triple-negative (TN, p = 0.027) and high-CD1a cells with EFS in luminal-B (p = 0.012) BC. Cluster analyses of TIICs revealed two groups of tumours (C1 and C2) that had different immune patterns and clinical outcomes. An immunoscore based on clinicopathological variables was developed to identify high risk (C1) or low-risk (C2) patients. Additionally, cluster analyses revealed two groups of tumours for both luminal-B and TNBC. Our findings support the association of sTILs with pCR and show an immunological component in a subset of patients with HER2-negative BC. Our immunoscore may be useful for future escalation or de-escalation treatments. [ABSTRACT FROM AUTHOR]

Introduction: Regulator of G protein signalling 1 (RGS1) closely regulates malignant phenotypes and tumour immunity in several cancers, while its clinical value in nonsmall cell lung cancer (NSCLC) is by far rarely reported. Consequently, this study aimed to explore the linkage of blood RGS1 with clinical features and prognosis in surgical NSCLC patients. Methods: Two‐hundred and ten surgical NSCLC patients were consecutively enrolled in this study, whose RGS1 in peripheral blood mononuclear cells was determined before treatment via reverse transcriptional‐quantitative polymerase chain reaction. Additionally, the blood RGS1 was also collected from 30 healthy controls (HCs). Results: Blood RGS1 was increased in NSCLC patients compared with HCs (P < 0.001). Elevated blood RGS1 was related to lymph node (LYN) metastasis (P = 0.001), higher tumour‐nodes‐metastasis (TNM) stage (P = 0.004), neoadjuvant chemotherapy administration (P = 0.044), shortened accumulative disease‐free survival (DFS) (P = 0.008) and overall survival (OS) (P = 0.013) in NSCLC patients. A multivariate Cox's regression analysis showed that blood RGS1 high expression could independently reflect shortened DFS (hazard ratio = 1.499, P = 0.023), whereas it could not independently predict OS (P > 0.050). Furthermore, blood RGS1 high expression was associated with shortened OS (P = 0.020) in patients with neoadjuvant therapy and with worse DFS (P = 0.028) and OS (P = 0.026) in patients with adjuvant therapy, while blood RGS1 was not linked with DFS or OS in patients without neoadjuvant or adjuvant therapy (all P > 0.050). Conclusion: Elevated blood RGS1 correlates with LYN metastasis, neoadjuvant chemotherapy administration, worse DFS and OS, which might serve as a useful prognostic biomarker for surgical NSCLC patients. [ABSTRACT FROM AUTHOR]

Abstract Introduction Regulator of G protein signalling 1 (RGS1) closely regulates malignant phenotypes and tumour immunity in several cancers, while its clinical value in nonsmall cell lung cancer (NSCLC) is by far rarely reported. Consequently, this study aimed to explore the linkage of blood RGS1 with clinical features and prognosis in surgical NSCLC patients. Methods Two‐hundred and ten surgical NSCLC patients were consecutively enrolled in this study, whose RGS1 in peripheral blood mononuclear cells was determined before treatment via reverse transcriptional‐quantitative polymerase chain reaction. Additionally, the blood RGS1 was also collected from 30 healthy controls (HCs). Results Blood RGS1 was increased in NSCLC patients compared with HCs (P 0.050). Furthermore, blood RGS1 high expression was associated with shortened OS (P = 0.020) in patients with neoadjuvant therapy and with worse DFS (P = 0.028) and OS (P = 0.026) in patients with adjuvant therapy, while blood RGS1 was not linked with DFS or OS in patients without neoadjuvant or adjuvant therapy (all P > 0.050). Conclusion Elevated blood RGS1 correlates with LYN metastasis, neoadjuvant chemotherapy administration, worse DFS and OS, which might serve as a useful prognostic biomarker for surgical NSCLC patients.