Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

Downregulation of SMOC1 is associated with progression of colorectal traditional serrated adenomas

BACKGROUND

Aberrant DNA methylation is prevalent in colorectal serrated lesions. We previously reported that the CpG island of SMOC1 is frequently methylated in traditional serrated adenomas (TSAs) and colorectal cancers (CRCs) but is rarely methylated in sessile serrated lesions (SSLs). In the present study, we aimed to further characterize the expression of SMOC1 in early colorectal lesions.

METHODS

SMOC1 expression was analyzed immunohistochemically in a series of colorectal tumors (n = 199) and adjacent normal colonic tissues (n = 112).

RESULTS

SMOC1 was abundantly expressed in normal colon and SSLs while it was significantly downregulated in TSAs, advanced adenomas and cancers. Mean immunohistochemistry scores were as follows: normal colon, 24.2; hyperplastic polyp (HP), 18.9; SSL, 23.8; SSL with dysplasia (SSLD)/SSL with early invasive cancer (EIC), 15.8; TSA, 5.4; TSA with high grade dysplasia (HGD)/EIC, 4.7; non-advanced adenoma, 21.4; advanced adenoma, 11.9; EIC, 10.9. Higher levels SMOC1 expression correlated positively with proximal colon locations and flat tumoral morphology, reflecting its abundant expression in SSLs. Among TSAs that contained both flat and protruding components, levels of SMOC1 expression were significantly lower in the protruding components.

CONCLUSION

Our results suggest that reduced expression of SMOC1 is associated with progression of TSAs and conventional adenomas and that SMOC1 expression may be a biomarker for diagnosis of serrated lesions and risk prediction in colorectal tumors.

Bioinformatics-based analysis of the roles of basement membrane-related gene AGRN in systemic lupus erythematosus and pan-cancer development

Introduction

Systemic lupus erythematosus (SLE) is an autoimmune disease involving many systems and organs, and individuals with SLE exhibit unique cancer risk characteristics. The significance of the basement membrane (BM) in the occurrence and progression of human autoimmune diseases and tumors has been established through research. However, the roles of BM-related genes and their protein expression mechanisms in the pathogenesis of SLE and pan-cancer development has not been elucidated.

Methods

In this study, we applied bioinformatics methods to perform differential expression analysis of BM-related genes in datasets from SLE patients. We utilized LASSO logistic regression, SVM-RFE, and RandomForest to screen for feature genes and construct a diagnosis model for SLE. In order to attain a comprehensive comprehension of the biological functionalities of the feature genes, we conducted GSEA analysis, ROC analysis, and computed levels of immune cell infiltration. Finally, we sourced pan-cancer expression profiles from the TCGA and GTEx databases and performed pan-cancer analysis.

Results

We screened six feature genes (AGRN, PHF13, SPOCK2, TGFBI, COL4A3, and COLQ) to construct an SLE diagnostic model. Immune infiltration analysis showed a significant correlation between AGRN and immune cell functions such as parainflammation and type I IFN response. After further gene expression validation, we finally selected AGRN for pan-cancer analysis. The results showed that AGRN's expression level varied according to distinct tumor types and was closely correlated with some tumor patients' prognosis, immune cell infiltration, and other indicators.

Discussion

In conclusion, BM-related genes play a pivotal role in the pathogenesis of SLE, and AGRN shows immense promise as a target in SLE and the progression of multiple tumors.

Identification and validation of a risk model and molecular subtypes based on tryptophan metabolism-related genes to predict the clinical prognosis and tumor immune microenvironment in lower-grade glioma

Background

Lower-grade glioma (LGG) is one of the most common malignant tumors in the central nervous system (CNS). Accumulating evidence have demonstrated that tryptophan metabolism is significant in tumor. Therefore, this study aims to comprehensively clarify the relationship between tryptophan metabolism-related genes (TRGs) and LGGs.

Methods

The expression level of TRGs in LGG and normal tissues was first analyzed. Next, the key TRGs with prognostic value and differential expression in LGGs were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, a risk model was constructed and Consensus clustering analysis was conducted based on the expression level of key TRGs. Then, the prognostic value, clinicopathological factors, and tumor immune microenvironment (TIME) characteristics between different risk groups and molecular subtypes were analyzed. Finally, the expression, prognosis, and TIME of each key TRGs were analyzed separately in LGG patients.

Results

A total of 510 patients with LGG from The Cancer Genome Atlas (TCGA) dataset and 1,152 normal tissues from the Genotype-Tissue Expression (GTEx) dataset were included to evaluate the expression level of TRGs. After LASSO regression analysis, we identified six key TRGs and constructed a TRGs risk model. The survival analysis revealed that the risk model was the independent predictor in LGG patients. And the nomogram containing risk scores and independent clinicopathological factors could accurately predict the prognosis of LGG patients. In addition, the results of the Consensus cluster analysis based on the expression of the six TRGs showed that it could classify the LGG patients into two distinct clusters, with significant differences in prognosis, clinicopathological factors and TIME between these two clusters. Finally, we validated the expression, prognosis and immune infiltration of six key TRGs in patients with LGG.

Conclusion

This study demonstrated that tryptophan metabolism plays an important role in the progression of LGG. In addition, the risk model and the molecular subtypes we constructed not only could be used as an indicator to predict the prognosis of LGG patients but also were closely related to the clinicopathological factors and TIME of LGG patients. Overall, our study provides theoretical support for the ultimate realization of precision treatment for patients with LGG.