Over expression of METRN predicts poor clinical prognosis in colorectal cancer

Identification of macrophage-related genes in bladder cancer patients using single-cell sequencing and construction of a prognostic model

Single-cell sequencing is an emerging technology that can effectively identify cell types in tumors. In the tumor microenvironment of bladder cancer, macrophages play a crucial role in invasion and immune escape. This study aimed to assess the expression of macrophage-related genes (MRGs) in the tumor microenvironment of bladder cancer patients and construct a prognostic model based on MRGs using bioinformatics methods.

METHODS

Single-cell sequencing data from bladder cancer patients was downloaded from the GEO. After quality control and cell type identification, macrophages in the samples were extracted for re-clustering. Feature genes were then identified, and MRGs were assessed. Genetic data from TCGA database bladder cancer patients was also downloaded and organized. The intersection of MRGs and the TCGA gene set was determined. Clinical information was connected with this intersection, and the data was divided into training and validation sets. The training set was used for model construction and the validation set for model verification. A prognostic model based on MRGs was built using the LASSO algorithm and Cox regression. Patients were divided into high-risk and low-risk groups based on their prognostic features, and survival information in the training and validation sets was observed. The predictive ability of the model was assessed using a ROC curve, followed by a calibration plot to predict 1-, 3-, and 5-year survival rates.

RESULTS

Four cell types were identified, and after extracting macrophages, three cell subgroups were clustered, resulting in 1,078 feature genes. The top 100 feature genes from each macrophage subgroup were extracted and intersected with TCGA expressed genes to construct the model. A risk prediction model composed of CD74, METRN, PTPRR, and CDC42EP5 was obtained. The survival and ROC curves showed that this model had good predictive ability. A calibration curve also demonstrated good prognostic ability for patients.

CONCLUSION

This study, based on single-cell data, TCGA data, and clinical information, constructed an MRG-based prognostic model for bladder cancer using multi-omics methods. This model has good accuracy and reliability in predicting the survival and prognosis of patients with bladder cancer, providing a reference for understanding the interaction between MRGs and bladder cancer.

Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Background

Multiple myeloma (MM), an incurable plasma cell malignancy. The significance of the relationship between natural killer (NK) cell-related genes and clinical factors in MM remains unclear.

Methods

Initially, we extracted NK cell-related genes from peripheral blood mononuclear cells (PBMC) of healthy donors and MM samples by employing single-cell transcriptome data analysis in TISCH2. Subsequently, we screened NK cell-related genes with prognostic significance through univariate Cox regression analysis and protein-protein interaction (PPI) network analysis. Following the initial analyses, we developed potential subtypes and prognostic models for MM using consensus clustering and lasso regression analysis. Additionally, we conducted a correlation analysis to explore the relationship between clinical features and risk scores. Finally, we constructed a weighted gene co-expression network analysis (WGCNA) and identified differentially expressed genes (DEGs) within the MM cohort.

Results

We discovered that 153 NK cell-related genes were significantly associated with the prognosisof MM patients (P <0.05). Patients in NK cluster A exhibited poorer survival outcomes compared to those in cluster B. Furthermore, our NK cell-related genes risk model revealed that patients with a high risk score had significantly worse prognoses (P <0.05). Patients with a high risk score were more likely to exhibit adverse clinical markers. Additionally, the nomogram based on NK cell-related genes demonstrated strong prognostic performance. The enrichment analysis indicated that immune-related pathways were significantly correlated with both the NK subtypes and the NK cell-related genes risk model. Ultimately, through the combined use of WGCNA and DEGs analysis, and by employing Venn diagrams, we determined that ITM2C is an independent prognostic marker for MM patients.

Conclusion

In this study, we developed a novel model based on NK cell-related genes to stratify the prognosis of MM patients. Notably, higher expression levels of ITM2C were associated with more favorable survival outcomes in these patients.

The Function and Mechanism of Anti-Inflammatory Factor Metrnl Prevents the Progression of Inflammatory-Mediated Pathological Bone Osteolytic Diseases

Metrnl, recently identified as an adipokine, is a secreted protein notably expressed in white adipose tissue, barrier tissues, and activated macrophages. This adipokine plays a pivotal role in counteracting obesity-induced insulin resistance. It enhances adipose tissue functionality by promoting adipocyte differentiation, activating metabolic pathways, and exerting anti-inflammatory effects. Extensive research has identified Metrnl as a key player in modulating inflammatory responses and as an integral regulator of muscle regeneration. These findings position Metrnl as a promising biomarker and potential therapeutic target in treating inflammation-associated pathologies. Despite this, the specific anti-inflammatory mechanisms of Metrnl in immune-mediated osteolysis and arthritis remain elusive, warranting further investigation. In this review, we will briefly elaborate on the role of Metrnl in anti-inflammation function in inflammation-related osteolysis, arthritis, and pathological bone resorption, which could facilitate Metrnl's clinical application as a novel therapeutic strategy to prevent bone loss. While the pathogenesis of elbow stiffness remains elusive, current literature suggests that Metrnl likely exerts a pivotal role in its development.

Role of asprosin and meteorin-like peptide in progression of actinic keratosis to squamous cell carcinoma

Squamous cell carcinoma (SCC) often develops from an underlying premalignant lesion. Factors that affect the progression of actinic keratosis (AK) to invasive SCC are not fully known. Asprosin (ASP) and meteorin-like peptide (METRNL) are adipokines that are involved primarily in glucose metabolism. We investigated the expression of ASP and METRNL in AK and SCC to evaluate the role of these adipokines in the development of SCC. We used 15 SCC specimens, 12 AK specimens and 12 healthy control skin specimens. ASP and METRNL protein expression in tumor and surrounding tissue was evaluated using immunohistochemistry. ASP expression in tumor tissue was significantly greater in the SCC group than in the control and AK groups, but it did not differ significantly between the AK and control groups. A positive correlation was observed for both ASP and METRNL expressions between tumor tissue and adjacent epidermis, hair follicles, sebaceous gland, eccrine gland, inflammatory cells and vascular structures. ASP and METRNL may exert pro-tumor effects toward development of invasive SCC. The expression intensity of ASP and METRNL can be used as a biomarker of risk of progression to SCC.

The Role of Meteorin-Like Peptide and Asprosin in Colon Carcinoma

INTRODUCTION

 Colon cancer is one of the most frequent gastrointestinal system cancers on a global scale. Common colonoscopy tests have reduced the incidence of colorectal cancer (CRC). Although nutrition, microorganisms, and their metabolites are related to colon cancer, the exact mechanism of CRC is still not clear. For this reason, it is of great importance to elucidate the molecular mechanisms of colon oncogenesis.

METHODS

This study was conducted retrospectively with samples obtained from the laboratory of Firat University Faculty of Medicine, Department of Pathology. A total of 30 patient samples were used. The control group consisted of healthy colon tissues from the same patients, and the other group consisted of colon carcinoma tissues from the same patients. Tissue samples of both groups were evaluated immunohistochemically with meteorin-like (METRNL) peptide and Asprosin.

RESULTS

The immunoreactivity of METRNL was found to be lower in colon carcinoma tissues than in healthy colon tissues (0.2 ± 0.06 and 0.08 ± 0.03, respectively). Asprosin immunoreactivity was found to be higher in colon carcinoma tissues than in healthy colon tissues (0.4 ± 0.07 and 1.08 ± 0.15, respectively).

CONCLUSION

As a result of this study, it was observed that there was a significant difference between healthy colon tissue and colon carcinoma tissue in terms of METRNL and Asprosin expression. Both proteins might be involved in the molecular mechanism of colon carcinoma. This situation is important in terms of diagnosis.

Asprosin, irisin, and meteorin-like protein immunoreactivity in different stages of colorectal adenocarcinoma

OBJECTIVE

In this study, we aimed to investigate the immunoreactivity of asprosin, irisin, and meteorin-like protein (METRNL) in different stages of colorectal adenocarcinoma, which is the most common malignancy of the gastrointestinal tract.

MATERIALS AND METHODS

Overall, 60 patients with colorectal adenocarcinoma, including 20 well (Group 1), moderately (Group 2), and poorly differentiated (Group 3) cases, respectively, and 20 with normal colonic mucosa, were examined using light microscopy for immunohistochemical staining of asprosin, METRNL, and irisin.

RESULTS

Compared with the control group, a significant increase in irisin and asprosin immunoreactivity was found in the grade 1 and 2 colorectal adenocarcinoma groups. Moreover, compared with the grade 1 and 2 groups, this immunoreactivity was significantly decreased in the grade 3 colorectal adenocarcinoma group. Although there was no significant difference in METRNL immunoreactivity between the grade 1 and control groups, a statistically significant increase in this immunoreactivity was found in the grade 2 group. In contrast, METRNL immunoreactivity was significantly decreased in the grade 3 group compared with the grade 2 group.

CONCLUSION

We found that in early-stage colorectal adenocarcinoma there was an increase in the immunoreactivity of asprosin and irisin, but in the advanced stage there was a decrease in immunoreactivity. Although METRNL immunoreactivity did not change in the control and grade 1 groups, it was found to increase significantly in the grade 2 group and decrease in the grade 3 group.

Identification of Melanoma Subsets Based on DNA Methylation Sites and Construction of a Prognosis Evaluation Model

Background

Melanoma is a lethal skin malignant tumor, and its formation or development is regulated by various genetic and epigenetic molecules. Although there are traditional methods provided for the doctors to evaluate the patients' prognosis or make the diagnosis, the novel method based on epigenetic markers is still needed to make the early diagnosis.

Results

We identified 256 melanoma-independent prognosis-related methylation sites (P < 0.0001) and divided patients into seven methylation subgroups. Methylation levels and survival time in the C2 subgroup were lower than that of other clusters (P < 0.05). We established the predicted model of prognosis risk for melanoma using the significantly changed methylation sites in C2. The model efficiently divided patients into high- and low-risk groups (area under the receiver operating characteristic curve, 0.833). Risk scores and patient survival time were negatively correlated (r_s = −0.325, P < 0.0001). Genes corresponding to the independent prognosis-associated methylation sites were enriched in cancer- and immunology-related pathways. We identified 35 hub genes. DOK2, GBP4, PSMB9, and NLRC5 were significantly changed according to methylation subgroups, survival, tumor stages, and T categories and were positively correlated, which was validated in the testing group (P < 0.05). The levels of DOK2, GBP4, PSMB9, and NLRC5 had an opposite trend to their methylation sites in patients with poor prognosis.

Conclusions

We identified seven DNA methylation subtypes and constructed a highly effective prognosis risk assessment model. The transcript levels of key genes corresponding to the independent prognosis-related methylation sites were significantly changed in patients according to prognosis and positively correlated with each other, indicating they may collaboratively promote melanoma formation. These findings further our understanding of the mechanism of melanoma and provide new targets for diagnosis and treatment.

The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma

Background: N6-methyladenosine (m6A), 5-methylcytosine (m5C) and N1-methyladenosine (m1A) are the main RNA methylation modifications involved in the progression of cancer. However, it is still unclear whether m6A/m5C/m1A-related long non-coding RNAs (lncRNAs) affect the prognosis of head and neck squamous cell carcinoma (HNSCC). Methods: We summarized 52 m6A/m5C/m1A-related genes, downloaded 44 normal samples and 501 HNSCC tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) database, and then searched for m6A/m5C/m1A-related genes co-expressed lncRNAs. We adopt the least absolute shrinkage and selection operator (LASSO) Cox regression to obtain m6A/m5C/m1A-related lncRNAs to construct a prognostic signature of HNSCC. Results: This prognostic signature is based on six m6A/m5C/m1A-related lncRNAs (AL035587.1, AC009121.3, AF131215.5, FMR1-IT1, AC106820.5, PTOV1-AS2). It was found that the high-risk subgroup has worse overall survival (OS) than the low-risk subgroup. Moreover, the results showed that most immune checkpoint genes were significantly different between the two risk groups (p < 0.05). Immunity microenvironment analysis showed that the contents of NK cell resting, macrophages M2, and neutrophils in samples of low-risk group were significantly lower than those of high-risk group (p < 0.05), while the contents of B cells navie, plasma cells, and T cells regulatory (Tregs) were on the contrary (p < 0.05). In addition, patients with high tumor mutational burden (TMB) had the worse overall survival than those with low tumor mutational burden. Conclusion: Our study elucidated how m6A/m5C/m1A-related lncRNAs are related to the prognosis, immune microenvironment, and TMB of HNSCC. In the future, these m6A/m5C/m1A-related lncRNAs may become a new choice for immunotherapy of HNSCC.

The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.

Neurensin-2 promotes proliferation, invasion and migration of colorectal cancer cells via interaction with SOX12

Colorectal cancer (CRC) is the third most common malignant type of tumor worldwide. Neurensin-2 (NRSN2) is a small neuronal membrane protein associated with tumorigenesis. Therefore, the present study aimed to investigate the association between NRSN2 and CRC, and further examined the underlying mechanism of its effect on CRC metastasis. Human CRC SW620 cells were used to determine the biological functions of NRSN2 in CRC. Cell counting Kit-8 (CCK8), colony formation, wound-healing and transwell assays were performed to evaluate the role of NRSN2 on survival and metastasis of SW620 cells. The interaction between NRSN2 and SOX12 was determined via bioinformatics analysis and confirmed using immunoprecipitation. It was identified that NRSN2 was highly expressed in CRC cells and served a critical role in CRC cell survival compared with in healthy colon epithelial cells. Furthermore, NRSN2-knockdown inhibited the proliferation, invasion and migration of SW620 cells, while NRSN2 overexpression promoted these cellular processes. Additionally, it was demonstrated that NRSN2 could recruit SOX12 in SW620 cells. NRSN2-knockdown decreased SOX12 expression, while NRSN2 overexpression upregulated SOX12 expression. Overall, the present results suggested NRSN2 as a novel biomarker for CRC diagnosis and identified NRSN2 as a potential therapeutic target for CRC treatment.

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

BACKGROUND

Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment.

RESULTS

We found 610 independent prognosis-related 5'-cytosine-phosphate-guanine-3' (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients' survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166.

CONCLUSIONS

This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

Over expression of METRN predicts poor clinical prognosis in colorectal cancer

BACKGROUND

The role of meteorin (METRN) in colorectal cancer has not been reported previously. We aimed to explore the relationship between METRN and colorectal cancer (CRC) prognosis.

METHODS

Data were retrieved from the Gene Expression Omnibus database. Gene expression values were log2 transformed and normalized by quantile normalization. Missing values were imputed with the R impute package. Differentially expressed genes were analyzed using the R limma package. METRN expression was compared between normal and CRC tissues and among different stages and subtypes of CRC. We assessed the relationship between METRN and KRAS/BRAF mutations in CRC. Five-year overall (OS), disease-free (DFS), and disease-specific survival (DSS) rates were determined by Kaplan-Meier analysis and analyzed by log-rank test.

RESULTS

METRN was expressed at a higher level in CRC (p = .0011) than in normal tissues, especially in advanced stages (p = .0343). METRN expression levels were higher in the MSI (dMMR) subtype (p < .001) and usually with BRAF mutations (p < .0001). METRN overexpression was associated with poor prognosis and low OS (p = .01014), DFS (p = .0146), and DSS (p < .0001) rates.

CONCLUSION

METRN overexpression is a predictive factor for poor prognosis in patients with CRC.