Lack of association between CDKN2A germline mutations and survival in patients with melanoma: A retrospective cohort study

Revealing the impact of autophagy-related genes in rheumatoid arthritis: Insights from bioinformatics

Background

Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis.

Methods

In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results.

Results

The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated.

Conclusion

This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

Clinical and Immunologic Features of Germline Pathogenic Variant-Positive Patients with Melanoma

PURPOSE

Malignant melanoma represents the most lethal skin cancer with germline predispositions thought to comprise 10% to 15% of all melanoma cases. No studies to date examine the immunologic features that may differentiate survival differences between germline pathogenic variant (gPV)-positive patients with melanoma from gPV-negative patients with melanoma.

EXPERIMENTAL DESIGN

Adult patients with melanoma and clinical characteristics suggesting hereditary predisposition to cancer were prospectively recruited to undergo germline testing and flow cytometric analysis of peripheral immune suppressor cells.

RESULTS

In this cohort, gPV-positive patients (n = 72) had a significantly improved melanoma-specific survival (MSS) compared with gPV-negative patients (n = 411; HRadj, 0.32; 95% CI, 0.13-0.82; P = 0.01). These survival improvements among gPV-positive patients were most apparent among cutaneous melanoma subtypes (HRadj, 0.12; 95% CI, 0.016-0.86; P = 0.03) and numerically improved in later-stage (IIB-IV) patients (HRadj, 0.34; 95% CI, 0.10-1.11; P = 0.06). Further, gPV-positive patients had a significantly lower level of total circulating PMN-MDSC compared with gPV-negative patients (P = 0.01), which was most apparent in those diagnosed with later stages (IIB-IV) of melanoma (P = 0.009). Finally, a significant upregulation of inflammatory transcriptome signatures in later-stage gPV-positive patients (n = 21) was observed in comparison with gPV-negative patients (n = 173) in the cutaneous melanoma cohort (SKCM) of The Cancer Genome Atlas (TCGA).

CONCLUSIONS

gPV-positive patients with melanoma exhibit improved MSS in addition to reduced peripheral PMN-MDSC and an enhanced inflammatory microenvironment.

Prognosis of CDKN2A germline mutation in patients with familial melanoma: a systematic review and meta-analysis

Familial melanoma is defined as melanoma occurring in two or more first-degree relatives by the WHO. Germline mutations are isolated in a subset of them. It is well known that CDKN2A is the most frequently mutated high-risk gene in familial melanoma, however, the prognosis it confers to patients who carry its mutations is still controversial. This review aims to assess whether germline mutations imply a worse prognosis in patients with familial melanoma. A systematic review and meta-analysis were conducted by searching the electronic databases PubMed/MEDLINE, EMBASE, and Cochrane Library. Data from 3 independent populations were eventually included in the meta-analysis, involving 291 cases and 57 416 controls. The results of this systematic review and meta-analysis suggest that there is a tendency for patients with germline mutations in the CDKN2A gene to have a worse overall survival (HR = 1.30, 95% CI = 0.99-1.69, P  = 0.05) and melanoma-specific survival (HR = 1.5, 95% CI = 0.97-2.31, P  = 0.07). Carrier patients would not only have more incidence of melanoma and a higher risk of a second melanoma, but they also seem to have a worse prognosis. The inclusion of gene panel testing in clinical practice and the collaboration within consortia are needed to provide further evidence on the prognosis of these patients.

A novel cuproptosis-related prognostic gene signature in adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is an aggressive and rare malignant tumor associated with poor outcomes. Cuproptosis, a new pattern of cell death, relies on mitochondrial respiration and is associated with protein lipoylation. Increasing evidence has demonstrated the potential roles of cuproptosis in several tumor entities. However, the relationship between cuproptosis and ACC remains unclear.

METHODS

In total, 10 cuproptosis-related genes (CRGs) of patients with ACC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and differential expression analysis of CRGs was analyzed. Functional enrichment of the CRGs was performed and protein-protein interaction analysis was utilized to explore the association between the CRGs. Cuproptosis-related risk score (CRRS) was constructed by Lasso Cox regression and validated.

RESULTS

In the current study, the alteration and expression patterns of 10 CRGs in TCGA-ACC datasets were analyzed. We identified different expression patterns of CRGs in ACCs, discovered strong associations between CRGs and ACCs, and found that the CRGs were associated with immune infiltration in ACCs. A CRRS was created thereafter to predict overall survival (OS). CRRS = (0.083103718) *FDX1 + (-0.278423862) *LIAS+(0.090985682) *DLAT+(-0.018784047) *PDHA1 + (0.297218951) *MTF1 + (0.310197964) *CDKN2A. Patients were divided into high- and low-risk groups based on their CRRS, and independent prognostic factors were investigated. Finally, CDKN2A and FDX1 were found to be independent prognostic predictors of patients with ACC.

CONCLUSIONS

CDKN2A and FDX1 are independent prognostic predictors of patients with ACC. Cuproptosis may play a role in the development of ACC, providing a new perspective on therapeutic strategies related to CRGs for cancer prevention and treatment.

Melanoma-specific survival before and after inclusion in a familial melanoma dermatologic surveillance program in CDKN2A mutation carriers and non-carriers

BACKGROUND

Inherited mutations in the CDKN2A gene are among the strongest known risk factors for cutaneous melanoma. Further, previous studies have reported inferior melanoma-specific survival in CDKN2A mutation carriers.

OBJECTIVES

Here, the melanoma-specific survival was studied, depending on CDKN2A carrier status and if the melanomas had been diagnosed before or after families were included in a surveillance program.

METHODS

Melanoma-prone families participating in this study were identified through a nationwide preventive program starting in 1987. Information on melanoma tumours and deaths was obtained through the Swedish Cancer Registry and Cause of Death Registry. Kaplan-Meier and Cox proportional hazards regression models were used to assess melanoma-specific survival in four defined cohorts, CDKN2A mutation (MUT) carriers with first invasive melanoma before or after inclusion [MUT-pre (n = 53) and MUT-post (n = 43)] and likewise in CDKN2A wild type (WT) cases [WT-pre (n = 255) and WT-post (n = 122)].

RESULTS

The MUT-pre and MUT-post cases were diagnosed with their first invasive melanoma at a significantly younger ages (38 and 42 years, respectively) than the WT-pre and WT-post cases (48 and 57 years, respectively). The melanomas in the MUT-pre had significantly higher T stage compared with MUT-post (p = 0.006), whereas no such difference was seen comparing WT-pre with WT-post (p = 0.849). MUT-pre had compared with WT-pre, significantly worse melanoma-specific survival, unadjusted (HR 2.33, 95% CI 1.33-4.08, p = 0.003) adjusted (HR 2.70, 95% CI 1.46-5.00, p = 0.001). However, the MUT-post cases had compared with the WT-post cases, no significant survival differences.

CONCLUSION

This study is the first to address the impact on survival from introducing a dermatologic surveillance program to familial melanoma cases with or without CDKN2A mutations. The CDKN2A-mut carriers appeared to have a clear benefit with less advanced melanomas diagnosed and better melanoma-specific survival after inclusion. Among the CDKN2A-wt cases, the effect of the inclusion on the studied outcomes was less evident.

Comprehensive proteomic signature and identification of CDKN2A as a promising prognostic biomarker and therapeutic target of colorectal cancer

BACKGROUND

The carcinogenesis of colorectal cancer (CRC) involves many different molecules and multiple pathways, and the specific mechanism has not been elucidated until now. Existing studies on the proteomic signature profiles of CRC are relatively limited. Therefore, we herein aimed to provide a more comprehensive proteomic signature profile and discover new prognostic markers and therapeutic targets by performing proteomic analysis of CRC and paired normal tissues.

AIM

To investigate the proteomic signature and identify novel protein prognostic biomarkers of CRC.

METHODS

Cancer tissues and paired normal tissues were collected from 48 patients who underwent surgical removal at the China-Japan Friendship Hospital from January 2020 to June 2021. Data independent acquisition (DIA) quantitative proteomic analysis was performed using high-performance liquid chromatography–mass spectrometry/mass spectrometry (nano-UHPLC–MS/MS) to identify differentially expressed proteins, among which those with a P adj value (t test, BH correction) < 0.05 and an absolute fold change (|log₂FC|) > 2 were identified as potential markers. Differentially expressed proteins were selected by bioinformatics analysis and validated by immunohistochemical tissue microarrays, and their association with prognosis was further analyzed with the Gene Expression Profiling Interactive Analysis database to identify prognostic protein biomarkers of CRC.

RESULTS

Significantly differential protein expression was observed between cancer tissues and normal tissues. Compared with normal tissues, 1115 proteins were upregulated and 705 proteins were downregulated in CRC based on P adj < 0.05 and |log₂FC| > 2, and bioinformatics analysis revealed that the differentially expressed proteins were involved in multiple biological processes associated with tumorigenesis, including ribosome biogenesis in eukaryotes, focal adhesion, extracellular matrix-receptor interactions and other tumor metabolism processes. Moreover, cyclin-dependent kinase inhibitor 2A (CDKN2A) expression was markedly upregulated in CRC, as validated by immunohistochemistry (0.228 vs 0.364, P = 0.0044), and was significantly enriched in tumor proliferation and signal transduction pathways such as the cell cycle and p53 signaling pathways. High CDKN2A expression was significantly correlated with poor prognosis (P = 0.021). These results demonstrated that CDKN2A functions as a driver of CRC.

CONCLUSION

Our study provides a comprehensive proteomic signature of CRC and highlights CDKN2A as a potential powerful prognostic marker and precision therapeutic target.