Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer.

BACKGROUND: This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs. METHODS: Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group. RESULTS: In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy. CONCLUSION: A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Lycium barbarum (Wolfberry) glycopeptide prevents stress-induced anxiety disorders by regulating oxidative stress and ferroptosis in the medial prefrontal cortex.

BACKGROUND: Lycium barbarum (Wolfberry) extract has been shown to be effective in neuroprotection against aging or neural injury. Knowledge of its potential roles and biological mechanisms in relieving mental disorders, however, remains limited. PURPOSE: To investigate the potency of Lycium barbarum glycopeptide (LbGp) in alleviating anxiety disorders and the related biological mechanisms. METHODS: LbGp was administrated to mice subjected to 14 days of chronic restrain stress (CRS) via the intragastric route. The anxiolytic effect was evaluated by a battery of behavioral assays. The morphology of neurons and glial cells was evaluated, and cortical neuronal calcium transients were recorded in vivo. The molecular mechanism of LbGp was also investigated. RESULTS: LbGp effectively relieved anxiety-like and depressive behaviors under CRS. Mechanistic studies further showed that LbGp treatment relieved oxidative stress and lipid peroxidation in the medial prefrontal cortex (mPFC). In particular, the ferroptosis pathway was inhibited by LbGp, revealing a previously unrecognized mechanism of the anxiolytic role of wolfberry extract. CONCLUSION: In summary, our results supported the future development of LbGp to prevent or ameliorate stress-induced anxiety disorders. Our work provides a promising strategy for early intervention for pateitents with mental disorders by applying natural plant extracts.

MYO10 contributes to the malignant phenotypes of colorectal cancer via RACK1 by activating integrin/Src/FAK signaling.

Liver metastases still remain a major cause of colorectal cancer (CRC) patient death. MYO10 is upregulated in several tumor types; however, its significance and the underlying mechanism in CRC are not entirely clear. Here, we found that MYO10 was highly expressed in CRC tumor tissues, especially in liver metastasis tissues. MYO10 knockout reduced CRC cell proliferation, invasion, and migration in vitro and CRC metastasis in vivo. We identified RACK1 by LC-MS/MS and demonstrated that MYO10 interacts with and stabilizes RACK1. Mechanistically, MYO10 promotes CRC cell progression and metastasis via ubiquitination-mediated RACK1 degradation and integrin/Src/FAK signaling activation. Therefore, the MYO10/RACK1/integrin/Src/FAK axis may play an important role in CRC progression and metastasis.

Characterization of the immune cell infiltration landscape in lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have played an important role in the treatment of lung adenocarcinoma (LUAD). However, their effectiveness is limited, and many patients exhibit a weak response. In this study, we propose a new and more effective immunophenotyping method for evaluating the prognosis and tumor microenvironment (TME) cellular infiltration characteristics in LUAD patients and their response to immunotherapy. Based on the transcriptomic and prognostic data of 584 patients with LUAD collected from TCGA cohort and GEO dataset, we combined tumor immune infiltration, the TME, and immune-related genes to score each sample using principal component analysis (PCA) and divided the patients into two subgroups with high and low tumor immune infiltration (TII) scores. The high-TII score group was characterized by increased immune activation and apoptosis signaling pathways. Moreover, clinical subgroup analysis demonstrated that the TII immune score was also applicable to different clinical groups and the high-TII score group still exhibited good prognosis and better response to ICIs. This study mapped the TII landscape in LUAD patients and confirmed that the TII score is helpful for predicting patient response to immunotherapy and may guide more effective immunotherapeutic strategies.

Overexpression of LncRNA GHET1 predicts an unfavourable survival and clinical parameters of patients in various cancers.

Recently, increasing studies have reported that long non-coding RNA (lncRNA) gastric carcinoma highly expressed transcript 1 (GHET1) is highly expressed in variety of cancers and relevant to poor prognosis of cancer patients. Nevertheless, the results were inconsistent and the systematic analysis of lncRNA GHET1 in cancers has not been inspected. Thus, we aim to evaluate the relationship between lncRNA GHET1 expression and clinical outcomes in human cancers. We searched keywords in PubMed, Web of Science, Embase, Cochrane Library and ClinicalTrial.gov. Stata SE12.0 software was used in the quantitative meta-analysis. Pooled hazard ratio (HR) and odds ratio with 95% confidence interval (95% Cl) were calculated to evaluate the clinical significance of lncRNA GHET1. Twelve studies totalling 761 patients with cancers were included for analysis. The pooled results of this study indicated that high lncRNA GHET1 expression level was significantly associated with poor overall survival (OS, HR = 2.30, 95% CI: 1.75-3.02) in human cancers. The statistical significance was also detected in subgroup analysis stratified by analysis method, cancer type, sample size and follow-up time respectively. In addition, the elevated lncRNA GHET1 expression was also significantly related to more advanced clinical stage, earlier lymph node metastasis, earlier distant metastasis and bigger tumour size. LncRNA GHET1 may serve as a promising biomarker for prognosis in Asians with cancers.