Search

According to a recent report by GLOBOCAN, colorectal cancer is the third most common and second most deadly cancer in 2020. In our previous proteomic study, we found that the expression of GSTM2 in colon tissues was significantly lower than that in para-cancer tissues, and its lower expression was associated with reduced overall survival rate of patients, suggesting that this gene might play a role in the occurrence of colon cancer. As a member of the detoxifying enzyme family, GSTM2 is likely to play an important role in the initiation of tumors. Whereas, the functions of GSTM2 in colon cancer are barely known. In this study, using the RNA-Seq datasets of colon cancer patients from public database (ntumor = 457, nnormal = 41), we confirmed the reduced expression of GSTM2 and its prognostic value in colon cancer. Furthermore, we used our own Chinese cohort (ntumor = 100, nnormal = 72) verified the lower GSTM2 expression in colon cancer, and also its effects on patient prognosis. Subsequently, we uncovered two potential reasons for the lower expression of GSTM2 in colon cancer tissues, including the deep deletion of GSTM2 on genome, and the up-regulation of RAD21 or SP1. Moreover, we disclosed that GSTM2 might be involved in several immune-related pathways in colon cancer, such as chemokine signaling and leukocyte transendothelial migration. Finally, we revealed that the GSTM2 expression was closely related to the immune-related scores of colon cancer and the infiltration ratios of various immune cells, suggesting that GSTM2 might regulate the development of colon cancer by modulating immune microenvironment. In conclusion, we uncovered the prognostic value of GSTM2 based on the public data and our own data, revealed its potential regulatory role in tumor immune microenvironment, and disclosed the probable reasons for its lower expression in colon cancer. The findings of our study provide a potential prognostic biomarker and drug target for clinical diagnosis and treatment of colon cancer.

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

As recently reported by The International Agency for Research on Cancer (IARC), breast cancer has the highest incidence of all cancers in 2020. Many studies have revealed that golgi apparatus is closely associated with the development of breast cancer. However, the role of golgi apparatus in immune microenvironment is still not clear. In this study, using RNA-Seq datasets of breast cancer patients from the public database (n = 1080), we revealed that GOLT1B, encoding a golgi vesicle transporter protein, was significantly higher expressed in human breast cancer tissues versus normal tissues. Besides, we verified GOLT1B expression in five breast cancer cell line using our original data and found GOLT1B was significantly up-regulated in MDA-MB-231, MCF-7, SKBR3. Subsequently, we identified GOLT1B as a potential independent prognostic factor for breast cancer. After a multi-omics analysis, we uncovered that the higher expression of GOLT1B in breast cancer tissues versus normal tissues might be due to the amplification of GOLT1B and altered phosphorylation of its potential transcriptional factors, including JUN and SIN3A. Subsequently, we discovered that GOLT1B potentially regulated the immune microenvironment basing on the finding that its expression was closely related to the tumor microenvironment score and infiltration of immune cells. Moreover, we revealed that GOLT1B might affect the overall survival rates of breast cancer through regulating the immune cell infiltration. Finally, we disclosed the potential pathways involved in the functions of GOLT1B in breast cancer, including metabolism and ECM-receptor interaction pathways. To sum up, we identified GOLT1B as a potential prognostic gene for breast cancer and disclosed its role in regulating the immune microenvironment.

BackgroundProficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) metastasizes to a greater extent than MMR-deficient CRAC. Prognostic biomarkers are preferred in clinical practice. However, traditional biomarkers screened directly from sequencing are often not robust and thus cannot be confidently utilized.MethodsTo circumvent the drawbacks of blind screening, we established a new strategy to identify prognostic biomarkers in the conserved and specific oncogenic pathway and its regulatory RNA network. We performed RNA sequencing (RNA-seq) for messenger RNA (mRNA) and noncoding RNA in six pMMR CRAC patients and constructed a glycosylation-related RNA regulatory network. Biomarkers were selected based on the network and their correlation with the clinicopathologic information and were validated in multiple centers (n = 775).ResultsWe constructed a competing endogenous RNA (ceRNA) regulatory network using RNA-seq. Genes associated with glycosylation pathways were embedded within this scale-free network. Moreover, we further developed and validated a seven-glycogene prognosis signature, GlycoSig (B3GNT6, GALNT3, GALNT8, ALG8, STT3B, SRD5A3, and ALG6) that prognosticate poor-prognostic subtype for pMMR CRAC patients. This biomarker set was validated in multicenter datasets, demonstrating its robustness and wide applicability. We constructed a simple-to-use nomogram that integrated the risk score of GlycoSig and clinicopathological features of pMMR CRAC patients.ConclusionsThe seven-glycogene signature served as a novel and robust prognostic biomarker set for pMMR CRAC, highlighting the role of a dysregulated glycosylation network in poor prognosis.

Gliomas are the most common malignant primary brain tumors in adults and exhibit a spectrum of aberrantly aggressive phenotypes. MicroRNAs (miRNAs) play a regulatory role in various cancers, including gliomas; however, their specific roles and mechanisms have not been fully investigated. Studies have indicated that miR-29a is a tumor-suppressive miRNA, but the data are limited. In this study, we investigated the role of miR-29a-5p in glioma and further explored its underlying mechanisms. On the basis of bioinformatics, dehydrogenase/reductase 4 (DHRS4) was considered a potential target of miR-29a-5p and was also found to be highly expressed in gliomas in our experiments. Moreover, with a luciferase reporter assay, DHRS4 was found to be a target gene of miR-29a-5p and to be correlated with glioma proliferation, invasion, and migration in our in vivo and in vitro experiments. Simultaneously, we observed that the knockdown of DHRS4 rescued the downregulation of glioma proliferation, invasion, and migration caused by treatment with a mir-29a-5p inhibitor. The present findings demonstrate that miR-29a-5p suppresses cell proliferation, invasion, and migration by targeting DHRS4, and DHRS4 may be a potential new oncogene and prognostic factor in gliomas.

Background: The tumor immune microenvironment (TIME) is an external immune system that regulates tumorigenesis. However, cellular interactions involving the TIME in hepatocellular carcinoma (HCC) are poorly characterized.Methods: In this study, we used multidimensional bioinformatic methods to comprehensively analyze cellular TIME characteristics in 735 HCC patients. Additionally, we explored associations involving TIME molecular subtypes and gene types and clinicopathological features to construct a prognostic signature.Results: Based on their characteristics, we classified TIME and gene signatures into three phenotypes (TIME T1–3) and two gene clusters (Gene G1–2), respectively. Further analysis revealed that Gene G1 was associated with immune activation and surveillance and included CD8+ T cells, natural killer cell activation, and activated CD4+ memory T cells. In contrast, Gene G2 was characterized by increased M0 macrophage and regulatory T cell levels. After calculation of principal component algorithms, a TIME score (TS) model, including 78 differentially expressed genes, was constructed based on TIME phenotypes and gene clusters. Furthermore, we observed that the Gene G2 cluster was characterized by high TS, and Gene G1 was characterized by low TS, which correlated with poor and favorable prognosis of HCC, respectively. Correlation analysis showed that TS had a positive association with several clinicopathologic signatures [such as grade, stage, tumor (T), and node (N)] and known somatic gene mutations (such as TP53 and CTNNB1). The prognostic value of the TS model was verified using external data sets.Conclusion: We constructed a TS model based on differentially expressed genes and involving immune phenotypes and demonstrated that the TS model is an effective prognostic biomarker and predictor for HCC patients.

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology. [ABSTRACT FROM AUTHOR]