SPARCL1 exhibits different expressions in left- and right-sided colon cancer and is downregulated via DNA methylation

Does side matter? Deciphering mechanisms that underpin side-dependent pathogenesis and therapy response in colorectal cancer

Colorectal cancer (CRC) is stratified by heterogeneity between disease sites, with proximal right-sided CRC (RCRC) multifactorial in its distinction from distal left-sided CRC (LCRC). Notably, right-sided tumors are associated with aggressive disease characteristics which culminate in poor clinical outcomes for these patients. While factors such as mutational profile and patterns of metastasis have been suggested to contribute to differences in therapy response, the exact mechanisms through which RCRC resists effective treatment have yet to be elucidated. In response, recent analyzes, including those utilizing whole genome sequencing, transcriptional profiling, and single-cell analyses, have demonstrated that key molecular differences exist between disease sites, with differentially expressed genes spanning a diverse range of cellular functions. Here, we review and contextualize the most recent data on molecular biomarkers found to exhibit discordance between RCRC and LCRC, and highlight candidates for further investigation, including those which present promise for future clinical application. Given the present disparity in survival outcomes for RCRC patients, we expect the prognostic biomarkers presented in our review to be useful in establishing future directions for the side-specific treatment of CRC.

The oncogenic functions of SPARCL1 in bladder cancer

Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) belongs to the SPARC family of matricellular proteins. However, underlying functions of SPARCL1 in bladder cancer (BCa) remain understudied. We performed an integrated search for the expression patterns of SPARCL1 in relation to various clinicopathological features of BCa. We then carried out Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA). Furthermore, we investigated the correlations between SPARCL1 and immunological features, such as tumour mutation burden (TMB), immune activation processes, immune checkpoint expression, tumour immune dysfunction and exclusion (TIDE) scores, and chemotherapeutic sensitivity in BCa. Our analysis revealed that SPARCL1 was downregulated across multiple cancers. In BCa, elevated SPARCL1 was linked with advanced histopathologic stage, higher T and N stage, and poorer prognosis in the clinical cohort. In vitro experiments demonstrated that increased SPARCL1 expression inhibited cell proliferation, migration, and invasion. Additionally, highly expressed SPARCL1 was linked to elevated immune, stromal and ESTIMATE scores, as well as an increase in naive B cells, M2 macrophages, and resting mast cells. We observed a moderate correlation between SPARCL1 expression and CD163, VSIG4 and MS4A4A, which are markers of M2 macrophages. Furthermore, SPARCL1 expression was positively related to TMB, immune activation processes, TIDE scores, immune checkpoint expression, and chemotherapeutic sensitivity in BCa. Our study highlights the potential involvement of SPARCL1 in macrophage recruitment and polarization and suggests its utility as a biomarker for prognosis in BCa.

Integrating the characteristic genes of macrophage pseudotime analysis in single-cell RNA-seq to construct a prediction model of atherosclerosis

BACKGROUND

Macrophages play an important role in the occurrence and development of atherosclerosis. However, few existing studies have deliberately analyzed the changes in characteristic genes in the process of macrophage phenotype transformation.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define the cells involved and determine their transcriptomic characteristics. KEGG enrichment analysis, CIBERSORT, ESTIMATE, support vector machine (SVM), random forest (RF), and weighted correlation network analysis (WGCNA) were applied to bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

Nine cell clusters were identified. M1 macrophages, M2 macrophages, and M2/M1 macrophages were identified as three clusters within the macrophages. According to pseudotime analysis, M2/M1 macrophages and M2 macrophages can be transformed into M1 macrophages. The ROC curve values of the six genes in the test group were statistically significant (AUC (IL1RN): 0.899, 95% CI: 0.764-0.990; AUC (NRP1): 0.817, 95% CI: 0.620-0.971; AUC (TAGLN): 0.846, 95% CI: 0.678-0.971; AUC (SPARCL1): 0.825, 95% CI: 0.620-0.988; AUC (EMP2): 0.808, 95% CI: 0.630-0.947; AUC (ACTA2): 0.784, 95% CI: 0.591-0.938). The atherosclerosis prediction model showed significant statistical significance in both the train group (AUC: 0.909, 95% CI: 0.842-0.967) and the test group (AUC: 0.812, 95% CI: 0.630-0.966).

CONCLUSIONS

IL1RNHigh M1, NRP1High M2, ACTA2High M2/M1, EMP2High M1/M1, SPACL1High M2/M1 and TAGLNHigh M2/M1 macrophages play key roles in the occurrence and development of arterial atherosclerosis. These marker genes of macrophage phenotypic transformation can also be used to establish a model to predict the occurrence of atherosclerosis.

Based on Molecular Subtypes, Immune Characteristics and Genomic Variation to Constructing and Verifying Multi-Gene Prognostic Characteristics of Colorectal Cancer

Background: Colon cancer (COAD) has been identified as being among the most prevalent tumors globally and ranked the third major contributor to cancer-related mortality. COAD is a molecularly heterogeneous disease. There are great differences in clinical manifestations and prognosis among different molecular subtypes.

Methods:379 TCGA-COAD samples were divided into four subtypes: primary proliferative, with collective, crypt-like, and EMT invasion. The differences among the four subtypes were analyzed from the multidimensional perspectives of immunity, genomic variation, and prognosis. The limma package was utilized to identify differentially expressed genes (DEGs) amongst different molecular subtypes. Phenotype-related coexpressed gene modules were identified using WGCNA. The polygenic prognosis model was created utilizing the lasso Cox analysis and verified by time-dependent subject operating characteristics (ROC).

Results: There are some differences in prognosis, TMB and common gene variation, immune score, and immunotherapy/chemotherapy between proliferative and three invasive molecular subtypes. 846 differential genes (DEGs) were obtained by limma packet analysis. Differential gene analysis was utilized to screen the DEGs among distinct subtypes, which were significantly enriched in the pathways related to tumorigenesis and development. Co-expression network analysis found 46 co-expressed genes correlated with proliferative and three invasive phenotypes. Based on differentially co-expressed genes, we developed a prognostic risk model of 8-genes signature, which exhibited strong stability regardless of external and internal validation. RT-PCR experiments proved the expression of eight genes in tumor and normal samples.

Conclusion: We have developed an eight-gene signature prognostic stratification system. Furthermore, we proposed that this classifier can serve as a molecular diagnostic tool to assess the prognosis of colon cancer patients.