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Cancers
Special Issues
Genomics and Transcriptomics in Sarcoma
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Genomics and Transcriptomics in Sarcoma

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Molecular Cancer Biology".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 1061

Special Issue Editors

Prof. Dr. George Jour

E-Mail Website
Guest Editor
Department of Dermatology, NYU Grossman School of Medicine, New York, NY, USA
Interests: genomic; transcriptomic; sarcoma; translational research; clinical development
Dr. Jose G. Mantilla

E-Mail Website
Guest Editor
Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
Interests: oncology; bone; sarcoma; chordoma

Special Issue Information

Dear Colleagues,

This Special Issue will focus on novel methodologies in the genomic field for different types of sarcomas. Emphasis will be placed on DNA-based genomic and epigenetic methodologies and their application in diagnostic, prognostic, and therapeutic biomarker discovery and validation in sarcomas, both in tumor tissue and blood. Additionally, this issue will focus on research and reviews on transcriptomics and spatial transcriptomics in soft tissue. The purpose is to shed light on insights regarding the tumor microenvironment and its relationship to tumor biology and drug response. Special interest will be given to research focusing on AI models aiding the interpretation of complex datasets for diagnostics and treatment strategies in soft-tissue sarcomas.

Prof. Dr. George Jour
Dr. Jose G. Mantilla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • transcriptomics
  • single-cell RNA sequencing
  • machine learning
  • sarcoma

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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13 pages, 1293 KiB  
Article
Integration of an OS-Based Machine Learning Score (AS Score) and Immunoscore as Ancillary Tools for Predicting Immunotherapy Response in Sarcomas
by Isidro Machado, Raquel López-Reig, Eduardo Giner, Antonio Fernández-Serra, Celia Requena, Beatriz Llombart, Francisco Giner, Julia Cruz, Victor Traves, Javier Lavernia, Antonio Llombart-Bosch and José Antonio López Guerrero
Cancers 2025, 17(15), 2551; https://doi.org/10.3390/cancers17152551 - 1 Aug 2025
Viewed by 158
Abstract
Background: Angiosarcomas (ASs) represent a heterogeneous and highly aggressive subset of tumors that respond poorly to systemic treatments and are associated with short progression-free survival (PFS) and overall survival (OS). The aim of this study was to develop and validate an immune-related [...] Read more.
Background: Angiosarcomas (ASs) represent a heterogeneous and highly aggressive subset of tumors that respond poorly to systemic treatments and are associated with short progression-free survival (PFS) and overall survival (OS). The aim of this study was to develop and validate an immune-related prognostic model—termed the AS score—using data from two independent sarcoma cohorts. Methods: A prognostic model was developed using a previously characterized cohort of 25 angiosarcoma samples. Candidate genes were identified via the Maxstat algorithm (Maxstat v0.7-25 for R), combined with log-rank testing. The AS score was then computed by weighing normalized gene expression levels according to Cox regression coefficients. For external validation, transcriptomic data from TCGA Sarcoma cohort (n = 253) were analyzed. The Immunoscore—which reflects the tumor immune microenvironment—was inferred using the ESTIMATE package (v1.0.13) in R. All statistical analyses were performed in RStudio (v 4.0.3). Results: Four genes—IGF1R, MAP2K1, SERPINE1, and TCF12—were ultimately selected to construct the prognostic model. The resulting AS score enabled the classification of angiosarcoma cases into two prognostically distinct groups (p = 0.00012). Cases with high AS score values, which included both cutaneous and non-cutaneous forms, exhibited significantly poorer outcomes, whereas cases with low AS scores were predominantly cutaneous. A significant association was observed between the AS score and the Immunoscore (p = 0.025), with higher Immunoscore values found in high-AS score tumors. Validation using TCGA sarcoma cohort confirmed the prognostic value of both the AS score (p = 0.0066) and the Immunoscore (p = 0.0029), with a strong correlation between their continuous values (p = 2.9 × 10−8). Further survival analysis, integrating categorized scores into four groups, demonstrated robust prognostic significance (p = 0.00021). Notably, in tumors with a low Immunoscore, AS score stratification was not prognostic. In contrast, among cases with a high Immunoscore, the AS score effectively distinguished outcomes (p < 0.0001), identifying a subgroup with poor prognosis but potential sensitivity to immunotherapy. Conclusions: This combined classification using the AS score and Immunoscore has prognostic relevance in sarcoma, suggesting that angiosarcomas with an immunologically active microenvironment (high Immunoscore) and poor prognosis (high AS score) may be prime candidates for immunotherapy and this approach warrants prospective validation. Full article
(This article belongs to the Special Issue Genomics and Transcriptomics in Sarcoma)
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Review

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15 pages, 236 KiB  
Review
Novel Molecular Methods in Soft Tissue Sarcomas: From Diagnostics to Theragnostics
by Nicholas Frazzette and George Jour
Cancers 2025, 17(7), 1215; https://doi.org/10.3390/cancers17071215 - 3 Apr 2025
Cited by 1 | Viewed by 678
Abstract
Soft tissue sarcomas (STSs) are a diverse group of malignant tumors derived from mesenchymal tissues [...] Full article
(This article belongs to the Special Issue Genomics and Transcriptomics in Sarcoma)
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