BACKGROUND: Several cathepsins have been identified as being involved in the development of cancer. Nevertheless, the connection between cathepsins and skin cancers remained highly elusive.
METHODS: A bidirectional Mendelian randomization (MR) analysis was performed to investigate the causal association between cathepsins and skin malignancies. The genome-wide association studies (GWAS) data for cathepsins, malignant melanoma (MM), and basal cell carcinoma (BCC) were obtained from European research. The primary method employed was inverse variance weighted. In addition, MR-Egger, weighted median, weighted mode, and simple mode were also executed. Sensitivity analysis was performed using Cochran\'s Q test, MR-Egger, and MR-PRESSO.
RESULTS: From univariable MR (UVMR), cathepsin H, and S were determined to have a causal relationship with BCC. Additionally, cathepsin H was identified as associated with MM. Multivariable MR (MVMR) showed that after correcting for risk factors of skin carcinoma, cathepsin H was detected to be protective against BCC, whereas cathepsin S has been observed as a risk factor for BCC. No substantial pleiotropy and heterogeneity were identified in the sensitivity analysis.
CONCLUSIONS: This study was the first to establish a direct link between cathepsins and skin malignancies. Cathepsin H and S have the potential to serve as new biomarkers for BCC, offering valuable assistance in the prompt identification, treatment, and prevention of the disease. Nevertheless, additional clinical trials are required to validate our findings.

暂无摘要。

UNASSIGNED: Circulating metabolites, which play a crucial role in our health, have been reported to be disordered in basal cell carcinoma (BCC). Despite these findings, evidence is still lacking to determine whether these metabolites directly promote or prevent BCC\'s progression. Therefore, our study aims to examine the potential effects of circulating metabolites on BCC progression.
UNASSIGNED: We conducted a two-sample Mendelian randomization (MR) analysis using data from two separate genome-wide association studies (GWAS). The primary study included data for 123 blood metabolites from a GWAS with 25,000 Finnish individuals, while the secondary study had data for 249 blood metabolites from a GWAS with 114,000 UK Biobank participants.GWAS data for BCC were obtained from the UK Biobank for the primary analysis and the FinnGen consortium for the secondary analysis. Sensitivity analyses were performed to assess heterogeneity and pleiotropy.
UNASSIGNED: In the primary analysis, significant causal relationships were found between six metabolic traits and BCC with the inverse variance weighted (IVW) method after multiple testing [P < 4 × 10-4 (0.05/123)]. Four metabolic traits were discovered to be significantly linked with BCC in the secondary analysis, with a significance level of P < 2 × 10-4 (0.05/249). We found that all the significant traits are linked to Polyunsaturated Fatty Acids (PUFAs) and their degree of unsaturation.
UNASSIGNED: Our research has revealed a direct link between the susceptibility of BCC and Polyunsaturated Fatty Acids and their degree of unsaturation. This discovery implies screening and prevention of BCC.

UNASSIGNED: Basal cell carcinoma (BCC) is the most common skin cancer, lacking reliable biomarkers or therapeutic targets for effective treatment. Genome-wide association studies (GWAS) can aid in identifying drug targets, repurposing existing drugs, predicting clinical trial side effects, and reclassifying patients in clinical utility. Hence, the present study investigates the association between plasma proteins and skin cancer to identify effective biomarkers and therapeutic targets for BCC.
UNASSIGNED: Proteome-wide mendelian randomization was performed using inverse-variance-weight and Wald Ratio methods, leveraging 1 Mb cis protein quantitative trait loci (cis-pQTLs) in the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study, to determine the causal relationship between plasma proteins and skin cancer and its subtypes in the FinnGen R10 study and the SAIGE database of Lee lab. Significant association with skin cancer and its subtypes was defined as a false discovery rate (FDR) < 0.05. pQTL to GWAS colocalization analysis was executed using a Bayesian model to evaluate five exclusive hypotheses. Strong colocalization evidence was defined as a posterior probability for shared causal variants (PP.H4) of ≥0.85. Mendelian randomization-Phenome-wide association studies (MR-PheWAS) were used to evaluate potential biomarkers and therapeutic targets for skin cancer and its subtypes within a phenome-wide human disease category.
UNASSIGNED: PTGES2, RNASET2, SF3B4, STX8, ENO2, and HS3ST3B1 (besides RNASET2, five other plasma proteins were previously unknown in expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL)) were significantly associated with BCC after FDR correction in the UKB-PPP and deCODE studies. Reverse MR showed no association between BCC and these proteins. PTGES2 and RNASET2 exhibited strong evidence of colocalization with BCC based on a posterior probability PP.H4 >0.92. Furthermore, MR-PheWAS analysis showed that BCC was the most significant phenotype associated with PTGES2 and RNASET2 among 2,408 phenotypes in the FinnGen R10 study. Therefore, PTGES2 and RNASET2 are highlighted as effective biomarkers and therapeutic targets for BCC within the phenome-wide human disease category.
UNASSIGNED: The study identifies PTGES2 and RNASET2 plasma proteins as novel, reliable biomarkers and therapeutic targets for BCC, suggesting more effective clinical application strategies for patients.

暂无摘要。

OBJECTIVE: Observational studies have identified a dual effect of circulating inflammatory proteins and immune cells on cancer progression. However, the specific mechanisms of action have not been clarified in the exacerbation of cutaneous-origin tumors. Therefore, this study aims to investigate whether the causal relationship between circulating inflammatory factors and basal cell carcinoma (BCC), cutaneous malignant melanoma (SKCM), and cutaneous squamous cell carcinoma (cSCC) is regulated by immune cells.
METHODS: This study employed the Two-Sample Mendelian Randomization (TSMR) approach to investigate the causal relationships between 91 circulating inflammatory factors and three prevalent types of skin cancer from a genetic perspective. Bayesian Weighted Mendelian Randomization (BWMR) was also used to validate correlation and reverse MR to assess inverse relationships. Subsequent sensitivity analyses were conducted to limit the impact of heterogeneity and pleiotropy. Finally, the two-step Mendelian Randomization (two-step MR) method was utilized to ascertain the mediating effects of specific immune cell traits in the causal pathways linking circulating inflammatory factors with BCC, SKCM, and cSCC.
RESULTS: The Inverse Variance Weighted (IVW) method and the Bayesian Weighted Algorithm collectively identified nine inflammatory factors causally associated with BCC, SKCM, and cSCC. The results from Cochran\'s Q test, mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger intercept were not statistically significant (p < 0.05). Additionally, the proportions mediated by CD4+ CD8dim T cell %leukocyte, CD4-CD8-Natural Killer T %T cell, and CD20 on IgD-CD38-B cell for FIt3L, CCL4, and OSM were 9.26%, 8.96%, and 10.16%, respectively.
CONCLUSIONS: Immune cell levels potentially play a role in the modulation process between circulating inflammatory proteins and cutaneous-origin exacerbated tumors. This finding offers a new perspective for the in-depth exploration of cutaneous malignancies.

OBJECTIVE: Research has previously established connections between the intestinal microbiome and the progression of some cancers. However, there is a noticeable gap in the literature in regard to using Mendelian randomisation (MR) to delve into potential causal relationships between the gut microbiota (GM) and basal cell carcinoma (BCC). Therefore, the purpose of our study was to use MR to explore the causal relationship between four kinds of GM (Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae) and BCC.
METHODS: We used genome-wide association study (GWAS) data and MR to explore the causal relationship between four kinds of GM and BCC. This study primarily employed the random effect inverse variance weighted (IVW) model for analysis, as complemented by additional methods including the simple mode, weighted median, weighted mode and MR‒Egger methods. We used heterogeneity and horizontal multiplicity to judge the reliability of each analysis. MR-PRESSO was mainly used to detect and correct outliers.
RESULTS: The random-effects IVW results showed that Bacteroides (OR = 0.936, 95% CI = 0.787-1.113, p = 0.455), Streptococcus (OR = 0.974, 95% CI = 0.875-1.083, p = 0.629), Proteobacteria (OR = 1.113, 95% CI = 0.977-1.267, p = 0.106) and Lachnospiraceae (OR = 1.027, 95% CI = 0.899-1.173, p = 0.688) had no genetic causal relationship with BCC. All analyses revealed no horizontal pleiotropy, heterogeneity or outliers.
CONCLUSIONS: We found that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae do not increase the incidence of BCC at the genetic level, which provides new insight for the study of GM and BCC.

Surgical management of basal cell carcinoma (BCC) typically involves surgical excision with post-operative margin assessment using the bread-loafing technique; or gold-standard Mohs micrographic surgery (MMS), where margins are iteratively examined for residual cancer after tumour removal, with additional excisions performed upon detecting residual tumour at margins. There is limited sampling of resection margins with bread loafing, with detection of positive margins 44% of the time using 2 mm intervals. To resolve this, we have developed three-dimensional (3D) Tissue Imaging for: (1) complete examination of cancer margins and (2) detection of tumour proximity to nerves and blood vessels. 3D Tissue optical clearing with a light sheet imaging protocol was developed for margin assessment in two datasets assessed by two independent evaluators: (1) 48 samples from 29 patients with varied BCC subtypes, sizes and pigmentation levels; (2) 32 samples with matching Mohs\' surgeon reading of tumour margins using two-dimensional haematoxylin & eosin-stained sections. The 3D Tissue Imaging protocol permits a complete examination of deeper and peripheral margins. Two independent evaluators achieved negative predictive values of 92.3% and 88.24% with 3D Tissue Imaging. Images obtained from 3D Tissue Imaging recapitulates histological features of BCC, such as nuclear crowding, palisading and retraction clefting and provides a 3D context for recognising normal skin adnexal structures. Concurrent immunofluorescence labelling of nerves and blood vessels allows visualisation of structures closer to tumour-positive regions, which may have a higher risk for neural and vascular infiltration. Together, this method provides more information in a 3D spatial context, enabling better cancer management by clinicians.

This study has used machine learning algorithms to develop a predictive model for differentiating between dermoscopic images of basal cell carcinoma (BCC) and actinic keratosis (AK). We compiled a total of 904 dermoscopic images from two sources - the public dataset (HAM10000) and our proprietary dataset from the First Affiliated Hospital of Dalian Medical University (DAYISET 1) - and subsequently categorised these images into four distinct cohorts. The study developed a deep learning model for quantitative analysis of image features and integrated 15 machine learning algorithms, generating 207 algorithmic combinations through random combinations and cross-validation. The final predictive model, formed by integrating XGBoost with Lasso regression, exhibited effective performance in the differential diagnosis of BCC and AK. The model demonstrated high sensitivity in the training set and maintained stable performance in three validation sets. The area under the curve (AUC) value reached 1.000 in the training set and an average of 0.695 in the validation sets. The study concludes that the constructed discriminative diagnostic model based on machine learning algorithms has excellent predictive capabilities that could enhance clinical decision-making efficiency, reduce unnecessary biopsies, and provide valuable guidance for further treatment.

BACKGROUND: Chronic inflammation has been shown to promote cancer progression. Rosacea is indeed a long-term inflammatory skin condition and had been reported to link with increased risk for several types of malignancies, but evidence for causality is lacking.
OBJECTIVE: To systematically estimate the causal relationship between rosacea and several types of cancer, including cutaneous malignant melanoma (CMM), cutaneous squamous cell carcinoma (cSCC), basal cell carcinoma (BCC), actinic keratosis (AK), thyroid cancer, breast cancer, glioma and hepatic cancer, as well as explore the potential underlying pathogenesis.
METHODS: We conducted a bidirectional two-sample Mendelian randomization study to probe the potential causal relationships between rosacea and several types of cancer. Instrumental variables were established using genome-wide significant single nucleotide polymorphisms associated with rosacea and cancers. The assessment of causality was carried out through multiple methods, and the robustness of the results was evaluated via sensitivity analyses.
RESULTS: There was no significant indication of causal effects of rosacea on CMM (pivw = 0.71), cSCC (pivw = 0.45), BCC (pivw = 0.90), AK (pivw = 0.73), thyroid cancer (pivw = 0.59), glioma (pivw = 0.15), and hepatic cancer (pivw = 0.07), but the genetic risk of rosacea was associated with an increased susceptibility to human epidermal growth factor receptor (HER)-negative malignant neoplasm of breast (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.02-1.18; pivw = 0.01). TANK (TRAF family member associated nuclear factor kappa B (NFKB) activator) was identified as a common protective gene for both rosacea (OR, 0.90; 95% CI, 0.82-0.99; pivw = 0.048) and HER-negative malignant neoplasm of the breast (OR, 0.86; 95% CI, 0.75-0.98; pivw = 0.032), which was primarily enriched in the negative regulation of NF-κB signal transduction and may contribute to the genetic links between rosacea and this subtype of breast cancer.
CONCLUSIONS: Our findings provide suggestive evidence for causal links between rosacea and HER-negative malignant neoplasm of the breast risk.