Exosomes are a subpopulation of the tumour microenvironment (TME) that transmit various biological molecules to promote intercellular communication. Exosomes are derived from nearly all types of cells and exist in all body fluids. Noncoding RNAs (ncRNAs) are among the most abundant contents in exosomes, and some ncRNAs with biological functions are specifically packaged into exosomes. Recent studies have revealed that exosome-derived ncRNAs play crucial roles in the tumorigenesis, progression and drug resistance of gastric cancer (GC). In addition, regulating the expression levels of exosomal ncRNAs can promote or suppress GC progression. Moreover, the membrane structures of exosomes protect ncRNAs from degradation by enzymes and other chemical substances, significantly increasing the stability of exosomal ncRNAs. Specific hallmarks within exosomes that can be used for exosome identification, and specific contents can be used to determine their origin. Therefore, exosomal ncRNAs are suitable for use as diagnostic and prognostic biomarkers or therapeutic targets. Regulating the biogenesis of exosomes and the expression levels of exosomal ncRNAs may represent a new way to block or eradicate GC. In this review, we summarized the origins and characteristics of exosomes and analysed the association between exosomal ncRNAs and GC development.
First-line PD-1 blockade plus chemotherapy significantly improves the survival benefits in late-stage gastric cancer (GC) patients. However, the pathological response rate and effects on the immune microenvironment of neoadjuvant PD-1 blockade plus chemotherapy in patients with cTNM-stage III GC remain to be elucidated. Patients with cTNM-stage III GC who underwent neoadjuvant PD-1 blockade plus chemotherapy and surgery were enrolled. Four in vivo models bearing GC were jointly established to investigate the specific roles of chemotherapy and PD-1 blockade for GC treatment. The tumor immune microenvironment was analyzed by hematoxylin and eosin (H&E) and IHC staining, multicolor flow cytometry and immunofluorescence. A total of 75 patients with cTNM-stage III (cT2-4N1-3M0) gastric cancer who received neoadjuvant PD-1 blockade plus chemotherapy (SOX/XELOX) were included in this study. After treatment, 21 (28.0%) and 57 (76.0%) patients achieved pathological complete response (pCR) and post-therapy pathological downstaging. Subgroup analyses revealed that patients with CPS >1 (32.6% vs 8.3%) and dMMR (35.7% vs 25.4%) subtype had better efficacy. Additionally, the resected specimens showed more anti-tumor immune infiltration indicating a response to neoadjuvant PD-1 blockade plus chemotherapy. Multicolor immunofluorescence and in vivo experiments on mouse models revealed that elevated M1/M2 ratio of macrophages, CD8 + T cells and plasma cells indicated effective response to treatment. Furthermore, neoadjuvant PD-1 blockade plus chemotherapy neither delayed surgery nor increased postoperative complication rate. The analyses indicate neoadjuvant PD-1 blockade plus chemotherapy is a promising therapeutic strategy in patients with cTNM-stage III GC with an encouraging pCR rate.
At present, there is no validated marker to identify the subpopulation of patients with advanced gastric cancer (AGC) who might benefit from neoadjuvant chemotherapy (NACT). In view of this clinical challenge, the identification of non-invasive biomarkers for efficacy prediction of NACT in patients with AGC is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy-based assay by using an exosome-derived RNAs model based on multi-omics characteristics of RNAs. We firstly used a multi-omics strategy to characterize the mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) profiles of circulating exosome enriched fractions in responders to NACT paired with non-responders, using RNA sequencing. Finally, numerous miRNAs, mRNAs and lncRNAs were identified to be associated with the response to NACT in patients with AGC, and it was validated in an independent cohort with promising AUC values. Furthermore, we established a 6-exosome-RNA panel that could robustly identified responders from non-responders treated with fluorouracil-based neoadjuvant chemotherapy.
Background: The human copper Cu proteome, also termed Cu-binding proteins (CBP), is responsible for transporting “free” Cu to the cell that is related to cuproptosis. However, their role in gastric cancer (GC) has not been reported. Methods: RNA expression data of 946 GC patients were collected. A series of machine learning and bioinformatic approaches were combined to build a CBP signature to predict survival and immune microenvironment and guide the priority treatment. Immunohistochemistry and multicolor immunofluorescence (mIF) in 1076 resection slides were used to verify immune features. Results: A CBP signature was constructed using the machine learning method from TCGA that classifies cases as CBP_low and CBP_high groups. Multivariable Cox analysis confirmed that the CBP signature was an independent prognostic factor in the training and validation cohorts. Additionally, GC patients with low CBPscores showed an increase in anti-tumor immune microenvironment, which was further verified by mIF in pathological resections following immunotherapy. Importantly, patients with low CBPscores had higher levels of TMB/MSI and responded well to immunotherapy. Conclusions: We conducted the first and comprehensive CBP analysis of GC patients and established a clinically feasible CBP signature for predicting survival and response to treatment, which will be helpful for guiding personalized medicine.
BackgroundThe current tumor-node-metastasis (TNM) staging system is insufficient for predicting the efficacy of chemotherapy in patients with gastric cancer (GC). This study aimed to analyze the association between the focal adhesion pathway and therapeutic efficacy of chemotherapy in patients with GC.MethodsRNA sequencing was performed on 33 clinical samples from patients who responded or did not respond to treatment prior to neoadjuvant chemotherapy. The validation sets containing 696 GC patients with RNA data from three cohorts (PKUCH, TCGA, and GSE14210) were analyzed. A series of machine learning and bioinformatics approaches was combined to build a focal adhesion-related signature model to predict the treatment efficacy and prognosis of patients with GC.ResultsAmong the various signaling pathways associated with cancer, focal adhesion was identified as a risk factor related to the treatment efficacy of chemotherapy and prognosis in patients with GC. The focal adhesion-related gene model (FAscore) discriminated patients with a high FAscore who are insensitive to neoadjuvant chemotherapy in our training cohort, and the predicted value was further verified in the GSE14210 cohort. Survival analysis also demonstrated that patients with high FAscores had a relatively shorter survival compared to those with low FAscores. In addition, we found that the levels of tumor mutation burden (TMB) and microsatellite instability (MSI) increased with an increase in FAscore, and the tumor microenvironment (TME) also shifted to a pro-tumor immune microenvironment.ConclusionThe FAscore model can be used to predict the treatment efficacy of chemotherapy and select appropriate treatment strategies for patients with GC.
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