BRAF and NRAS mutations and antitumor immunity in Korean malignant melanomas and their prognostic relevance: Gene set enrichment analysis and CIBERSORT analysis

AHNAK2 Promotes the Progression of Differentiated Thyroid Cancer through PI3K/AKT Signaling Pathway

AIMS

AHNAK2 may be used as a candidate marker for TC diagnosis and treatment.

BACKGROUND

Thyroid cancer (TC) is the most frequent malignancy in endocrine carcinoma, and the incidence has been increasing for decades.

OBJECTIVE

To understand the molecular mechanism of DTC, we performed next-generation sequencing (NGS) on 79 paired DTC tissues and normal thyroid tissues. The RNA-sequencing (RNA-seq) data analysis results indicated that AHNAK nucleoprotein 2 (AHNAK2) was significantly upregulated in the thyroid cancer patient's tissue.

METHODS

We also analyzed AHNAK2 mRNA levels of DTC tissues and normal tissues from The Cancer Genome Atlas (TCGA). The association between the expression level of AHNAK2 and clinicopathological features was evaluated in the TCGA cohort. Furthermore, AHNAK2 gene expression was analyzed by quantitative real-time polymerase chain reaction (qRT-PCR) in 40 paired DTC tissues and adjacent normal thyroid tissues. The receiver operating characteristic (ROC) curve was performed to evaluate the diagnostic value of AHNAK2. For cell experiments in vitro, AHNAK2 was knocked down using small interfering RNA (siRNA), and the biological function of AHNAK2 in TC cell lines was investigated. The expression of AHNAK2 was significantly upregulated in both the TCGA cohort and the local cohort.

RESULTS

The analysis results of the TCGA cohort indicated that the upregulation of AHNAK2 was associated with tumor size (P < 0.001), lymph node metastasis (P < 0.001), and disease stage (P < 0.001). The area under the curve (AUC, TCGA: P < 0.0001; local validated cohort: P < 0.0001) in the ROC curve revealed that AHNAK2 might be considered a diagnostic biomarker for TC. The knockdown of AHNAK2 reduced TC cell proliferation, colony formation, migration, invasion, cell cycle, and induced cell apoptosis.

CONCLUSION

Furthermore, the protein levels of phospho-PI3 Kinase p85 and phospho-AKT were downregulated in the transfected TC cell. Our study results indicate that AHNAK2 may promote metastasis and proliferation of thyroid cancer through PI3K/AKT signaling pathway. Thus, AHNAK2 may be used as a candidate marker for TC diagnosis and treatment.

Variations in genetics, biology, and phenotype of cutaneous disorders in skin of color - Part I: Genetic, biologic, and structural differences in skin of color

Skin of color (SOC) populations include those who identify as Black/African, Hispanic/Latinx, Asian/Pacific Islander, American Indian/Native Alaskan, Indigenous Australian, Middle Eastern, biracial/multiracial, or non-White; this list is far from exhaustive and may vary between and within cultures. Recent genetic and immunological studies have suggested that cutaneous inflammatory disorders (atopic dermatitis, psoriasis, and hidradenitis suppurativa) and malignancies (melanoma, basal cell carcinoma, and cutaneous T-cell lymphoma) may have variations in their immunophenotype among SOC. Additionally, there is growing recognition of the substantial role social determinants of health play in driving health inequalities in SOC communities. It is critically important to understand that social determinants of health often play a larger role than biologic or genetic factors attributed to "race" in health care outcomes. Herein, we describe the structural, genetic, and immunological variations and the potential implications of these variations in populations with SOC. This article underscores the importance of increasing the number of large, robust genetic studies of cutaneous disorders in SOC to create more targeted, effective therapies for this often underserved and understudied population. Part II of this CME will highlight the clinical differences in the phenotypic presentation of and the health disparities associated with the aforementioned cutaneous disorders in SOC.

Multiple datasets to explore the molecular mechanism of sepsis

Background

This study aimed to identify potential biomarkers, by means of bioinformatics, affecting the occurrence and development of septic shock.

Methods

Download GSE131761 septic shock data set from NCBI geo database, including 33 control samples and 81 septic shock samples. GSE131761 and sequencing data were used to identify and analyze differentially expressed genes in septic shock patients and normal subjects. In addition, with sequencing data as training set and GSE131761 as validation set, a diagnostic model was established by lasso regression to identify key genes. ROC curve verified the stability of the model. Finally, immune infiltration analysis, enrichment analysis, transcriptional regulation analysis and correlation analysis of key genes were carried out to understand the potential molecular mechanism of key genes affecting septic shock.

Results

A total of 292 differential genes were screened out from the self-test data, 294 differential genes were screened out by GSE131761, Lasso regression was performed on the intersection genes of the two, a diagnostic model was constructed, and 5 genes were identified as biomarkers of septic shock. These 5 genes were SIGLEC10, VSTM1, GYPB, OPTN, and GIMAP7. The five key genes were strongly correlated with immune cells, and the ROC results showed that the five genes had good predictive performance on the occurrence and development of diseases. In addition, the key genes were strongly correlated with immune regulatory genes.

Conclusion

In this study, a series of algorithms were used to identify five key genes that are associated with septic shock, which may become potential candidate targets for septic shock diagnosis and treatment.

Trial registration

Approval number:2019XE0149-1.

Characterization of the treatment-naive immune microenvironment in melanoma with BRAF mutation

Background

Patients with BRAF-mutant and wild-type melanoma have different response rates to immune checkpoint blockade therapy. However, the reasons for this remain unknown. To address this issue, we investigated the precise immune composition resulting from BRAF mutation in treatment-naive melanoma to determine whether this may be a driver for different response to immunotherapy.

Methods

In this study, we characterized the treatment-naive immune context in patients with BRAF-mutant and BRAF wild-type (BRAF-wt) melanoma using data from single-cell RNA sequencing, bulk RNA sequencing, flow cytometry and immunohistochemistry (IHC).

Results

In single-cell data, BRAF-mutant melanoma displayed a significantly reduced infiltration of CD8⁺ T cells and macrophages but also increased B cells, natural killer (NK) cells and NKT cells. We then validated this finding using bulk RNA-seq data from the skin cutaneous melanoma cohort in The Cancer Genome Atlas and deconvoluted the data using seven different algorithms. Interestingly, BRAF-mutant tumors had more CD4⁺ T cells than BRAF-wt samples in both primary and metastatic cohorts. In the metastatic cohort, BRAF-mutant melanoma demonstrated more B cells but less CD8⁺ T cell infiltration when compared with BRAF-wt samples. In addition, we further investigated the immune cell infiltrate using flow cytometry and multiplex IHC techniques. We confirmed that BRAF-mutant melanoma metastases were enriched for CD4⁺ T cells and B cells and had a co-existing decrease in CD8⁺ T cells. Furthermore, we then identified B cells were associated with a trend for improved survival (p=0.078) in the BRAF-mutant samples and Th2 cells were associated with prolonged survival in the BRAF-wt samples.

Conclusions

In conclusion, treatment-naive BRAF-mutant melanoma has a distinct immune context compared with BRAF-wt melanoma, with significantly decreased CD8⁺ T cells and increased B cells and CD4⁺ T cells in the tumor microenvironment. These findings indicate that further mechanistic studies are warranted to reveal how this difference in immune context leads to improved outcome to combination immune checkpoint blockade in BRAF-mutant melanoma.

Characterization of the Different Subtypes of Immune Cell Infiltration to Aid Immunotherapy

Background?PD-1 ablation or PD-L1 specific monoclonal antibody against PD-1 can recruit the accumulation of functional T cells, leading to tumor rejection in the microenvironment and significantly improving the prognosis of various cancers. Despite these unprecedented clinical successes, intervention remission rates remain low after treatment, rarely exceeding 40%. The observation of PD-1/L1 blocking in patients is undoubtedly multifactorial, but the infiltrating degree of CD8+T cell may be an important factor for immunotherapeutic resistance.

Methods:We proposed two computational algorithms to reveal the immune cell infiltration (ICI) landscape of 1646 lung adenocarcinoma patients. Three immune cell infiltration patterns were defined and the relative ICI scoring depended on principal-component analysis.

Results:A high ICI score was associated with the increased tumor mutation burden and cell proliferation-related signaling pathways. Different cellular signaling pathways were observed in low ICI score subtypes, indicating active cell proliferation, and may be associated with poor prognosis.

Conclusion:Our research identified that the ICI scores worked as an effective immunotherapy index, which may provide promising therapeutic strategies on immune therapeutics for lung adenocarcinoma.

Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

This study aims to explore biomarkers associated with vitiligo and analyze the pathological role of immune cell infiltration in the disease. We used the robust rank aggregation (RRA) method to integrate three vitiligo data sets downloaded from gene expression omnibus database, identify the differentially expressed genes (DEGs) and analyze the functional correlation. Then, the comprehensive strategy of combined weighted gene coexpression network analysis (WGCNA) and logical regression of the selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) machine learning algorithm are employed to screen and biomarkers associated with vitiligo. Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. Herein, we identified 131 robust DEGs, and enrichment analysis results showed that robust DEGs and melanogenesis were closely associated with vitiligo development and progression. TYR, TYRP1, DCT and LARP7 were identified as vitiligo-related biomarkers. Immune infiltration analysis demonstrated that CD4 T Cell, CD8 T Cell, Tregs, NK cells, dendritic cells, and macrophages were involved in vitiligo’s pathogenesis. In summary, we adopted a comprehensive strategy to screen biomarkers related to vitiligo and explore the critical role of immune cell infiltration in vitiligo.

Abbreviations: TYR, Tyrosinase; TYRP1, Tyrosinase-related protein-1; DCT, dopachrome tautomerase; LARP7, La ribonucleoprotein domain family, member-7; RRA, robust rank aggregation; DEGs, differentially expressed genes; WGCNA, weighted gene coexpression network analysis; LASSO, logical regression of the selection operator; SVM-RFE, support vector machine recursive feature elimination; RF, random forest; GWAS, Genome-wide association study; FasL, Fas-Fas ligand; Tregs, T-regulatory cells; NK, natural killer; GEPCs, gene expression profiling chips; GO, gene ontology; GSEA, gene set enrichment analysis; FDR, false discovery rate; AUC, area under the curve; ROC, receiver-operating characteristic; BP, biological process; CC, cellular component; MF, molecular function.

Profiles of immune infiltration and its relevance to survival outcome in meningiomas

Tumor-infiltrating immune cells play a decisive part in prognosis and survival. Until now, previous researches have not made clear about the diversity of cell types involved in the immune response. The objective of this work was to confirm the composition of tumor-infiltrating immune cells and their correlation with prognosis in meningiomas based on a metagene approach (known as CIBERSORT) and online databases. A total of 22 tumor-infiltrating immune cells were detected to determine the relationship between the immune infiltration pattern and survival. The proportion of M2 macrophages was more abundant in 68 samples, reaching more than 36%. Univariate Cox regression analysis displayed that the proportion of dendritic cells was obviously related to prognosis. Hierarchical clustering analysis identified two clusters by the method of within sum of squares errors, which exhibited different infiltrating immune cell composition and survival. To summarize, our results indicated that proportions of tumor-infiltrating immune cells as well as cluster patterns were associated with the prognosis, which offered clinical significance for research of meningiomas.

Mutation profile of primary subungual melanomas in Caucasians

Background: Specific genomic profile of cutaneous melanomas is related to UVR exposure, which exerts biological and therapeutic impact. Subungual melanoma (SUM) is an exceedingly rare disease; therefore, it is not well characterized. SUM pathogenesis is not related to UVR induced DNA damage and expected to differ from other melanoma subtypes. Our study aimed to define the mutation profile of SUM in Caucasians.

Materials and Methods: Next-generation sequencing-based genomic analysis was used to identify frequently mutated loci in 50 cancer-related genes in 31 SUM primary tumors.

Results: The most abundant mutations in SUM were found in KIT – in 13% of cases and NRAS – also in 13%, while BRAF - only in 3% of cases.

Conclusions: Our findings confirmed a high frequency of KIT and NRAS mutations in SUM, as well as a low incidence of BRAF mutations. We reported novel KRAS, CTNNB1, TP53, ERBB2, and SMAD4 mutations in SUM. Our findings provide new insights into the molecular pathogenesis of SUM.