Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

Molecular Profiling and the Interaction of Somatic Mutations with Transcriptomic Profiles in Non-Melanoma Skin Cancer (NMSC) in a Population Exposed to Arsenic

Exposure to inorganic arsenic (As) is recognized as a risk factor for non-melanoma skin cancer (NMSC). We followed up with 7000 adults for 6 years who were exposed to As. During follow-up, 2.2% of the males and 1.3% of the females developed basal cell carcinoma (BCC), while 0.4% of the male and 0.2% of the female participants developed squamous cell carcinoma (SCC). Using a panel of more than 400 cancer-related genes, we detected somatic mutations (SMs) in the first 32 NMSC samples (BCC = 26 and SCC = 6) by comparing paired (tissue-blood) samples from the same individual and then comparing them to the SM in healthy skin tissue from 16 participants. We identified (a) a list of NMSC-associated SMs, (b) SMs present in both NMSC and healthy skin, and (c) SMs found only in healthy skin. We also demonstrate that the presence of non-synonymous SMs in the top mutated genes (like PTCH1, NOTCH1, SYNE1, PKHD1 in BCC and TP53 in SCC) significantly affects the magnitude of differential expressions of major genes and gene pathways (basal cell carcinoma pathways, NOTCH signaling, IL-17 signaling, p53 signaling, Wnt signaling pathway). These findings may help select groups of patients for targeted therapy, like hedgehog signaling inhibitors, IL17 inhibitors, etc., in the future.

Cyfip2 controls the acoustic startle threshold through FMRP, actin polymerization, and GABAB receptor function

Animals process a constant stream of sensory input, and to survive they must detect and respond to dangerous stimuli while ignoring innocuous or irrelevant ones. Behavioral responses are elicited when certain properties of a stimulus such as its intensity or size reach a critical value, and such behavioral thresholds can be a simple and effective mechanism to filter sensory information. For example, the acoustic startle response is a conserved and stereotyped defensive behavior induced by sudden loud sounds, but dysregulation of the threshold to initiate this behavior can result in startle hypersensitivity that is associated with sensory processing disorders including schizophrenia and autism. Through a previous forward genetic screen for regulators of the startle threshold a nonsense mutation in Cytoplasmic Fragile X Messenger Ribonucleoprotein (FMRP)-interacting protein 2 (cyfip2) was found that causes startle hypersensitivity in zebrafish larvae, but the molecular mechanisms by which Cyfip2 establishes the acoustic startle threshold are unknown. Here we used conditional transgenic rescue and CRISPR/Cas9 to determine that Cyfip2 acts though both Rac1 and FMRP pathways, but not the closely related FXR1 or FXR2, to establish the acoustic startle threshold during early neurodevelopment. To identify proteins and pathways that may be downstream effectors of Rac1 and FMRP, we performed a candidate-based drug screen that indicated that Cyfip2 can also act acutely to maintain the startle threshold branched actin polymerization and N-methyl D-aspartate receptors (NMDARs). To complement this approach, we used unbiased discovery proteomics to determine that loss of Cyfip2 alters cytoskeletal and extracellular matrix components while also disrupting oxidative phosphorylation and GABA receptor signaling. Finally, we functionally validated our proteomics findings by showing that activating GABAB receptors, which like NMDARs are also FMRP targets, restores normal startle sensitivity in cyfip2 mutants. Together, these data reveal multiple mechanisms by which Cyfip2 regulates excitatory/inhibitory balance in the startle circuit to control the processing of acoustic information.

Identification and verification of PTPN3 as a novel biomarker in predicting cancer prognosis, immunity, and immunotherapeutic efficacy

BACKGROUND

The importance of protein tyrosine phosphatase non-receptor type 3 (PTPN3) in controlling multifaceted tumor cell behaviors throughout cancer development has received widespread attention. Nevertheless, little is known about the biological roles of PTPN3 in drug sensitivity, immunotherapeutic effectiveness, tumor immune microenvironment, and cancer prognosis.

METHODS

The Cancer Genome Atlas (TCGA) database's RNAseq data were used to examine the expression of PTPN3 in 33 different cancer types. In addition, immunohistochemistry (IHC) was performed to validate the expression of PTPN3 across various cancer types within our clinical cohorts. The features of PTPN3 alterations were demonstrated throughout the cBioPortal database. This study focused on examining the prognostic and clinicopathological importance of PTPN3 through the acquisition of clinical data from the TCGA database. The investigation of PTPN3's probable role in the tumor immune microenvironment was demonstrated by the application of CIBERSORT, ESTIMATE algorithms, and the TISIDB database. Using Spearman's rank correlation coefficient, the relationships between PTPN3 expression and tumor mutation burden (TMB) and microsatellite instability (MSI) were evaluated. To further investigate the putative biological activities and downstream pathways of PTPN3 in various cancers in humans, Gene Set Enrichment Analysis (GSEA) was carried out. In addition, an examination was conducted to explore the associations between PTPN3 and the effectiveness of PD-1/PD-L1 inhibitors, utilizing data extracted from the GEO database.

RESULTS

PTPN3 was abnormally expressed in multiple cancer types and was also strictly associated with the prognosis of cancer patients. IHC was used to investigate and confirm the various expression levels of PTPN3 in various malignancies, including breast cancer, lung cancer, sarcoma, and kidney renal clear cell carcinoma in our clinical cohorts. There is a high correlation between the levels of PTPN3 expression in different cancers and infiltrating immune cells, including mast cells, B cells, regulatory T cells, CD8 + T cells, macrophages, and dendritic cells. Infiltrating immune cells, such as regulatory T cells, CD8 + T cells, macrophages, B cells, dendritic cells, and mast cells, are strongly correlated with PTPN3 expression levels in various tumors. The expression of PTPN3 exhibited a substantial correlation with many immune-related biomolecules and the expression of TMB and MSI in multiple types of cancer. In addition, PTPN3 has demonstrated promise in predicting the therapeutic benefits of PD-1/PD-L1 inhibitors and the susceptibility to anti-cancer medications in the treatment of clinical cancer.

CONCLUSIONS

Our findings highlight the importance of PTPN3 as a prognostic biomarker and predictor of immunotherapy success in various forms of cancer. Furthermore, PTPN3 appears to have an important role in modifying the tumor immune microenvironment, highlighting its potential as a promising biomarker for prognosis prediction, immunotherapeutic efficacy evaluation, and identification of immune-related characteristics in diverse cancer types.

Disease-related biomarkers as experimental endpoints in 3D skin culture models

The success of in vitro 3D models in either recapitulating the normal tissue physiology or altered physiology or disease condition depends upon the identification and/or quantification of relevant biomarkers that confirm the functionality of these models. Various skin disorders, such as psoriasis, photoaging, vitiligo, etc., and cancers like squamous cell carcinoma and melanoma, etc. have been replicated via organotypic models. The disease biomarkers expressed by such cell cultures are quantified and compared with the biomarkers expressed in cultures depicting the normal tissue physiology, to identify the most prominent variations in their expression. This may also indicate the stage or reversal of these conditions upon treatment with relevant therapeutics. This review article presents an overview of the important biomarkers that have been identified in in-vitro 3D models of skin diseases as endpoints for validating the functionality of these models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10616-023-00574-2.

Non-Melanoma Skin Cancer: A Genetic Update and Future Perspectives

Simple Summary

Non-melanoma skin cancer (NMSC) is the main type of cancer in the Caucasian population, and the number of cases continues to rise. Research mostly focuses on clinical characteristics analysis, but genetic features are crucial to malignancies’ establishment and advance. We aim to explore the genetic basics of skin cancer, surrounding microenvironment interactions, and regulation mechanisms to provide a broader perspective for new therapies’ development.

Abstract

Skin cancer is one of the main types of cancer worldwide, and non-melanoma skin cancer (NMSC) is the most frequent within this group. Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are the most common types. Multifactorial features are well-known for cancer development, and new hallmarks are gaining relevance. Genetics and epigenetic regulation play an essential role in cancer susceptibility and progression, as well as the variety of cells and molecules that interact in the tumor microenvironment. In this review, we provide an update on the genetic features of NMSC, candidate genes, and new therapies, considering diverse perspectives of skin carcinogenesis. The global health situation and the pandemic have been challenging for health care systems, especially in the diagnosis and treatment of patients with cancer. We provide innovative approaches to overcome the difficulties in the current clinical dynamics.

Machine Learning of Single Cell Transcriptomic Data From anti-PD-1 Responders and Non-responders Reveals Distinct Resistance Mechanisms in Skin Cancers and PDAC

Immune checkpoint therapies such as PD-1 blockade have vastly improved the treatment of numerous cancers, including basal cell carcinoma (BCC). However, patients afflicted with pancreatic ductal carcinoma (PDAC), one of the deadliest malignancies, overwhelmingly exhibit negative responses to checkpoint therapy. We sought to combine data analysis and machine learning to differentiate the putative mechanisms of BCC and PDAC non-response. We discover that increased MHC-I expression in malignant cells and suppression of MHC and PD-1/PD-L expression in CD8⁺ T cells is associated with nonresponse to treatment. Furthermore, we leverage machine learning to predict response to PD-1 blockade on a cellular level. We confirm divergent resistance mechanisms between BCC, PDAC, and melanoma and highlight the potential for rapid and affordable testing of gene expression in BCC patients to accurately predict response to checkpoint therapies. Our findings present an optimistic outlook for the use of quantitative cross-cancer analyses in characterizing immune responses and predicting immunotherapy outcomes.

Blood transcriptome analysis revealed the immune changes and immunological adaptation of wildness training giant pandas

The giant panda (Ailuropoda melanoleuca) is a global flagship species for biodiversity conservation. As the time for captive giant pandas to be released into the wild matures, wildness training is provided to allow adaptation to their natural environment. It is assumed that changes in the immune system would be integral in this adaptation from captive to wild, where many more pathogens would be encountered in their natural habitats. Therefore, this study aims to determine the expression changes of immune-related genes and their potential as immunoassay markers for adaptation monitoring in wildness training giant pandas, and then to understand the adaptation strategy of wildness training giant pandas to the wild environment, thereby improving the success rate of panda reintroduction. We obtained 300 differentially expressed genes (DEGs) by RNA-seq, with 239 up-regulated and 61 down-regulated DEGs in wildness training giant pandas compared to captive pandas. Functional enrichment analysis indicated that up-regulated DEGs were enriched in several immune-related terms and pathways. There were 21 immune-related DEGs, in which most of them were up-regulated in wildness training giant pandas, including several critical innate and cellular immune genes. IL1R2 was the most significantly up-regulated gene and is a signature of homeostasis within the immune system. In the protein-protein interaction (PPI) analysis, CXCL8, CXCL10, and CCL5 were identified as the hub immune genes. Our results suggested that wildness training giant pandas have stronger innate and cellular immunity than captive giant pandas, and we proposed that a gene set of CXCL8, CXCL10, CCL5, CD3D, NFKBIA, TBX21, IL12RB2, and IL1R2 may serve as potential immunoassay markers to monitor and assess the immune status of wildness training giant pandas. Our study offers the first insight into immune alterations of wildness training giant pandas, paving the way for monitoring and evaluating the immune status of giant pandas when reintroducing them into the wild.

Cytoplasmic FMR1 interacting protein (CYFIP) family members and their function in neural development and disorders

In humans, the cytoplasmic FMR1 interacting protein (CYFIP) family is composed of CYFIP1 and CYFIP2. Despite their high similarity and shared interaction with many partners, CYFIP1 and CYFIP2 act at different points in cellular processes. CYFIP1 and CYFIP2 have different expression levels in human tissues, and knockout animals die at different time points of development. CYFIP1, similar to CYFIP2, acts in the WAVE regulatory complex (WRC) and plays a role in actin dynamics through the activation of the Arp2/3 complex and in a posttranscriptional regulatory complex with the fragile X mental retardation protein (FMRP). Previous reports have shown that CYFIP1 and CYFIP2 may play roles in posttranscriptional regulation in different ways. While CYFIP1 is involved in translation initiation via the 5'UTR, CYFIP2 may regulate mRNA expression via the 3'UTR. In addition, this CYFIP protein family is involved in neural development and maturation as well as in different neural disorders, such as intellectual disabilities, autistic spectrum disorders, and Alzheimer's disease. In this review, we map diverse studies regarding the functions, regulation, and implications of CYFIP proteins in a series of molecular pathways. We also highlight mutations and their structural effects both in functional studies and in neural diseases.