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Background: Metabolic reprogramming plays a crucial role in the development of colorectal cancer (CRC), influencing tumor heterogeneity, the tumor microenvironment, and metastasis. While the interaction between metabolism and CRC is critical for developing personalized treatments, gaps remain in understanding how tumor cell metabolism affects prognosis. Our study introduces novel insights by integrating single-cell and bulk transcriptome analyses to explore the metabolic landscape within CRC cells and its mechanisms influencing disease progression. This approach allows us to uncover metabolic heterogeneity and identify specific metabolic genes impacting metastasis, which have not been thoroughly examined in previous studies. Methods: We sourced microarray and single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) and bulk sequencing data for CRC from The Cancer Genome Atlas (TCGA). We employed Gene Set Variation Analysis (GSVA) to assess metabolic pathway activity, consensus clustering to identify CRC-specific transcriptome subtypes in bulkseq, and rigorous quality controls, including the exclusion of cells with high mitochondrial gene expression in scRNA seq. Advanced analyses such as AUCcell, infercnvCNV, Non-negative Matrix Factorization (NMF), and CytoTRACE were utilized to dissect the cellular landscape and evaluate pathway activities and tumor cell stemness. The hdWGCNA algorithm helped identify prognosis-related hub genes, integrating these findings using a random forest machine learning model. Results: Kaplan-Meier survival curves identified 21 significant metabolic pathways linked to prognosis, with consensus clustering defining three CRC subtypes (C3, C2, C1) based on metabolic activity, which correlated with distinct clinical outcomes. The metabolic activity of the 13 cell subpopulations, particularly the epithelial cell subpopulation with active metabolic levels, was evaluated using AUCcell in scRNA seq. To further analyze tumor cells using infercnv, NMF disaggregated these cells into 10 cellular subpopulations. Among these, the C2 subpopulation exhibited higher stemness and tended to have a poorer prognosis compared to C6 and C0. Conversely, the C8, C3, and C1 subpopulations demonstrated a higher level of the five metabolic pathways, and the C3 and C8 subpopulations tended to have a more favorable prognosis. hdWGCNA identified 20 modules, from which we selected modules primarily expressed in high metabolic tumor subgroups and highly correlated with clinical information, including blue and cyan. By applying variable downscaling of RF to a total of 50 hub genes, seven gene signatures were obtained. Furthermore, molecules that were validated to be protective in GEO were screened alongside related molecules, resulting in the identification of prognostically relevant molecules such as UQCRFS1 and GRSF1. Additionally, the expression of GRSF1 was examined in colon cancer cell lines using qPCR and phenotypically verified by in vitro experiments. Conclusion: Our findings emphasize that high activity in specific metabolic pathways, including pyruvate metabolism and the tricarboxylic acid cycle, correlates with improved colon cancer outcomes, presenting new avenues for metabolic-based therapies. The identification of hub genes like GRSF1 and UQCRFS1 and their link to favorable metabolic profiles offers novel insights into tumor neovascularization and metastasis, with significant clinical implications for targeting metabolic pathways in CRC therapy.
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BACKGROUND: Depression is one of the most prevalent and serious mental disorders. Xiaoyaosan, a well-known Chinese prescription, has been widely used for the treatment of depression in China. Both clinical studies and animal experiments indicate that Xiaoyaosan has an obvious antidepressant activity. Additionally, a large number of candidate biomarkers have emerged that can be used for early disease detection and for monitoring ongoing treatment response to therapy because of their correlations with the characteristics of the disease. However, there have been few reports on biomarkers that measure the treatment response to the clinical use of Xiaoyaosan using a metabolomics approach. The current study is aimed at discovering biomarkers and biochemical pathways to facilitate the diagnosis of depression and the efficient evaluation of Xiaoyaosan using plasma metabolomics profiles based on (1)H NMR. METHODS: Sixteen depressed patients diagnosed by standard methods (HAMD and CGI-SI) and sixteen healthy volunteers were recruited. (1)H NMR-based metabolomics techniques and multivariate statistical methods were used to analyze the plasma metabolites of the depressed patients before and after treatment and to compare them with healthy controls. RESULTS: The plasma levels of trimethylamine oxide, glutamine and lactate in depressed patients increased significantly (p≤0.05) compared with healthy controls, whereas the levels of phenylalanine, valine, alanine, glycine, leucine, citrate, choline, lipids and glucose decreased significantly (p≤0.05). Additionally, alanine, choline, trimethylamine oxide, glutamine, lactate and glucose were returned to normal levels after Xiaoyaosan treatment. These statistically significant perturbations are involved in energy metabolism, amino acid metabolism and gut microbiota metabolism. LIMITATIONS: Additional experimentation with gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) is required to confirm our findings. CONCLUSIONS: Application of these biomarkers in clinical practice may help to optimize the diagnosis of depression and to evaluate the efficacy of Xiaoyaosan. Metabolomics is promising as a biomarker discovery tool.
