An update on methods for detection of prognostic and predictive biomarkers in melanoma

Knowledge graph and frontier trends in melanoma-associated ncRNAs: a bibliometric analysis from 2006 to 2023

Objectives

Malignant melanoma (MM) is a highly malignant skin tumor. Although research on non-coding RNAs (ncRNAs) of MM has advanced swiftly in recent years, no specific bibliometric analyses have been conducted on this topic. The present study aims to summarize the knowledge graphs and frontier trends and to provide new perspectives and direction of collaboration for researchers.

Method

Research data on melanoma and ncRNA published from January 1, 2006 to October 9, 2023 were retrieved and extracted from Web of Science. R Studio (Version 4.3.1), Scimago Graphica (Version 1.0.36), VOSviewer version (1.6.19), and Citespace (6.2.4R) were used to analyze the publications, countries, journals, institutions, authors, keywords, references, and other relevant data and to build collaboration network graphs and co-occurrence network graphs accordingly.

Results

A total of 1,222 articles were retrieved, involving 4,894 authors, 385 journals, 43,220 references, 2413 keywords, and 1,651 institutions in 47 countries. The average annual growth rate in the number of articles was 25.02% from 2006 to 2023; among all the journals, Plos One had the highest number of publications and citations, which are 42 publications and 2,228 citations, respectively. Chinese researchers were the most prolific publishers in this field, having published a total of 657 articles, among which 42 were published by Shanghai Jiao Tong University, which was the most productive institution. In recent years, the most explored keywords included long non-coding RNAs, immunotherapy, and exosm. According to the timeline chart of reference co-citation, "functional role" has been the most explored hotspot since 2015, and human cancer is a newly emerged hotspot after 2021.

Conclusion

Through a bibliometric analysis, this study included all publications on ncRNAs and melanoma that were published in English from 2006 to 2023 in Web of Science to analyze the trends in the number of publications, international research focuses, and the direction of collaboration. The results of this study may provide information on knowledge graph, frontier trends and identify research topics in melanoma. More current research proved that ncRNA plays a crucial role in the biological behavior of melanoma including proliferation, invasion, metastasis, drug resistance, etc. With the development of research on ncRNA and melanoma, ncRNA may great potential in development of early diagnosis, targeted therapy and efficacy evaluation in the future. The results of this study also provide new perspectives and research partners for researchers in this field.

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients

BACKGROUND

Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy.

METHODS

We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC).

RESULTS

The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively.

CONCLUSIONS

Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

PIKFYVE inhibitors trigger interleukin-24-dependent cell death of autophagy-dependent melanoma

Inhibitors specifically targeting the 1-phosphatidylinositol 3-phosphate 5-kinase (PIKFYVE) disrupt lysosome homeostasis, thereby selectively terminating autophagy-dependent human cancer cells in vivo as well as in vitro without harming the viability of nonmalignant cells. To elucidate the mechanism by which PIKFYVE inhibition induces cell death, autophagy-dependent melanoma cells were compared with normal foreskin fibroblasts. RNA sequence profiling suggested that PIKFYVE inhibitors upregulated an endoplasmic reticulum (ER) stress response involving interleukin-24 (IL24; also known as MDA7) selectively in melanoma cells. Subsequent biochemical and genetic analyses confirmed these results and extended them to tumor xenografts in which tumor formation and expansion were inhibited. IL24 expression was upregulated by the DDIT3/CHOP/CEBPz transcription factor, a component of the PERK-dependent ER-stress response. Ectopic expression of IL24-induced cell death in melanoma cells, but not in foreskin fibroblasts, whereas ablation of the IL24 gene in melanoma cells prevented death. IL24 upregulation was triggered specifically by PIKFYVE inhibition. Thus, unlike thapsigargin and tunicamycin, which induce ER-stress indiscriminately, PIKFYVE inhibitors selectively terminated PIKFYVE-sensitive melanoma by inducing IL24-dependent ER-stress. Moreover, induction of cell death by a PIKFYVE inhibitor together with ectopic expression of IL24 protein was cumulative, thereby confirming the therapeutic potential of PIKFYVE inhibitors in the treatment of melanoma.