Background/Objectives: Complete excision of squamous cell carcinoma (SCC) while preserving healthy tissue relies on accurate diagnosis and assessment of tumor margins. Ex vivo confocal laser scanning microscopy (EVCM) allows rapid, high-resolution visualization of freshly excised tissue. This study was conducted to comprehensively assess the diagnostic accuracy of EVCM for SCC diagnosis in tissue specimens and margin assessment in margin-controlled (micrographic) surgery using conventional histopathology as the reference standard. Methods: A systematic literature search of MEDLINE and Embase was conducted on 1 January 2026, in accordance with PRISMA guidelines. Pooled sensitivity and specificity were estimated using bivariate random-effects models. The QUADAS-2 and GRADE frameworks were applied to assess risk of bias and certainty of evidence. Results: Six studies comprising a total of 288 specimens were included. For SCC diagnosis in tissue specimens, the pooled sensitivity was 85.1% (95% confidence interval [CI]: 71.6-92.8) and the pooled specificity was 95.5% (95% CI: 90.9-97.8), with low between-study heterogeneity and moderate certainty of evidence. For margin assessment, pooled sensitivity and specificity were 89.9% (95% CI: 51.6-98.7) and 96.1% (95% CI: 85.8-99.0), respectively, with low heterogeneity but also low certainty of evidence owing mainly to the limited number of included studies and specimens. Conclusions: EVCM demonstrates moderate sensitivity and high specificity for the diagnosis of SCC in tissue specimens and may be used selectively as an adjunct to conventional histology, for example as a rapid confirmatory diagnostic tool capitalizing on its high specificity. Current evidence for margin assessment, although promising, remains limited.

AbstractBackgroundCancer immunotherapy has transformed metastatic cancer treatment, yet challenges persist regarding therapeutic efficacy. RECQL4, a RecQ‐like helicase, plays a central role in DNA replication and repair as part of the DNA damage response, a pathway implicated in enhancing efficacy of immune checkpoint inhibitor (ICI) therapies. However, its role in patient response to ICI remains unclear.MethodsWe analysed whole exome and bulk RNA sequencing data from a pan‐cancer cohort of 25 775 patients and cutaneous melanoma cohorts (untreated: n = 471, anti‐progressive disease [PD]‐1 treated: n = 212). RECQL4 copy number variations and expression levels were assessed for patient outcomes. We performed gene set enrichment analysis to identify RECQL4‐dependent signalling pathways and explored the association between RECQL4 levels and immunoscores. We evaluated the interplay of ICI response and RECQL4 expression in melanoma cohorts of 95 responders and 85 non‐responders prior to and after ICI‐targeted therapy and tested the prognostic power of RECQL4. Finally, we generated genetically engineered RECQL4 variants and conducted comprehensive multi‐omic profiling, employing techniques such as liquid chromatography with tandem mass spectrometry, to elucidate mechanistic insights.ResultsWe identified RECQL4 as a critical negative regulator of poor prognosis and response to ICI therapy, but also demonstrated its suitability as an independent biomarker in melanoma. High tumour purity and limited signatures of tumour immunogenicity associated with response to anti‐PD‐1 correlated with high RECQL4 activity. We found alterations in the secretion profile of immune regulatory factors and immune‐related pathways robustly suppressed in tumours with high RECQL4 levels, underscoring its crucial role in fostering immune evasion. Mechanistically, we identified RECQL4‐mediated regulation of major histocompatibility complex class II molecule expression and uncovered class II major histocompatibility complex transactivator as a mediator bridging this regulation.ConclusionsOur findings unraveled the pivotal role of RECQL4 in immune modulation and its potential as both a predictive biomarker and therapeutic target for optimising immunotherapeutic strategies across various cancer types.Highlights High RECQL4 expression limits survival and can act as an independent prognostic factor in melanoma patients. RECQL4 has the potential to act as a negative feedback mediator of immune checkpoint‐targeted therapy by limiting signatures associated with therapeutic efficacy. RECQL4 favours an immune‐evasive phenotype by downregulating major histocompatibility complex class II molecules.

Erythroderma is an acute and potentially life-threatening inflammatory condition characterized by redness and scaling of > 90% of the skin. Its treatment is challenging because various underlying skin diseases can cause erythroderma and are difficult to distinguish. Here, we performed in-depth proteomics and transcriptomics analyses of skin from 96 patients with erythroderma caused by five different diseases, including pityriasis rubra pilaris, psoriasis, atopic dermatitis, cutaneous T-cell lymphoma, and drug-induced maculopapular rash. High-throughput workflows enabled in-depth molecular profiling, identifying over 9,300 proteins and 17,200 protein coding transcripts, revealing distinct molecular signatures for each disease. The proteome showed elevated expression of type 2 immunity associated Charcot-Leyden crystal in skin of atopic dermatitis, potentially contributing to NLRP3-driven chronic inflammation in this disease. Complementary transcriptomic analysis demonstrated selective upregulation of IL17C in pityriasis rubra pilaris, strongly correlating with increased IL1 family cytokine expression. Interestingly, only a subset of these patients expressed this IL17C-IL1 signature, suggesting treatment-relevant disease endotypes. Through multi-omics integration, we uncovered disease-specific molecular signatures consistently altered at both protein and transcript levels. In particular, we identified elevated expression of T-cell regulator RASAL3 in cutaneous T-cell lymphoma, which has not been explored in its pathogenesis so far. To translate these molecular profiles into clinical utility, we expanded our adaptive machine-learning algorithm (ADAPT-Mx) for tissue based-disease classification. This achieved 76.6% diagnostic accuracy, substantially outperforming combined conventional clinical and histopathological methods (59.5%). This study provides a template for precision diagnostics in erythroderma and demonstrates the clinical potential of multi-omic profiling in severe inflammatory skin diseases.