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Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition
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Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3339

Special Issue Editor

Prof. Dr. Tibor Krenacs

E-Mail Website
Guest Editor
Department of Pathology and Experimental Cancer Research, Semmelweis University, H-1085 Budapest, Hungary
Interests: biomarkers of tumor development; progression and prognosis; targeted cancer therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Though cancer development and progression involve the aberrations of complex cellular pathways at the genetic and epigenetic levels, detection of a set of selected biomarkers may have prognostic relevance and/or may offer targets for tumor therapy. These biomarkers can be either directly related to cancer evolution, such as tumor driver/oncogenes and their protein products, or can reflect e.g., protective responses of cancer to hypoxia or the body’s defense mechanisms against carcinogenesis, or reveal impaired cell-death mechanisms. Biomarkers can be discovered at different levels including DNA, the transcribed mRNA, and its epigenetic regulators, miRNAs, as well as the translated proteins which may deregulate essential cell functions and lead to cancer and its aggressive progression. Besides functional testing, advanced molecular methods can be used and combined for identifying cancer biomarkers including next generation sequencing (NGS), in situ hybridization, immunological methods, and proteomics. Research data can be supported by the in silico analysis of biomarker databases and machine-learning- and artificial-intelligence-based automated image analysis of in situ-detected molecules.

This Special Issue “Biomarkers of Tumor Progression, Prognosis and Therapy 2.0” of the International Journal of Molecular Sciences aims to introduce novel biomarkers and review established ones which may have important impacts on tumor fate by using and combining up-to-date in silico, in vitro, and in situ molecular methods in a range of cancer types.

Prof. Dr. Tibor Krenacs
Guest Editor

Manuscript Submission Information

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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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • cancer biomarkers
  • genetic aberrations
  • carcinogenesis
  • cancer progression
  • molecular methods
  • in silico data analysis
  • image analysis
  • proteomics

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Related Special Issue

Published Papers (4 papers)

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Research

12 pages, 2857 KiB  
Article
Combining rVAR2 and Anti-EpCAM to Increase the Capture Efficiency of Non-Small-Cell Lung Cancer Cell Lines in the Flow Enrichment Target Capture Halbach (FETCH) Magnetic Separation System
by Sitian He, Peng Liu, Yongjun Wu, Mette Ø. Agerbæk, Ali Salanti, Leon W. M. M. Terstappen, Pascal Jonkheijm and Michiel Stevens
Int. J. Mol. Sci. 2024, 25(18), 9816; https://doi.org/10.3390/ijms25189816 - 11 Sep 2024
Viewed by 436
Abstract
Circulating tumor cells (CTCs) are detected in approximately 30% of metastatic non-small-cell lung cancer (NSCLC) cases using the CellSearch system, which relies on EpCAM immunomagnetic enrichment and Cytokeratin detection. This study evaluated the effectiveness of immunomagnetic enrichment targeting oncofetal chondroitin sulfate (ofCS) using [...] Read more.
Circulating tumor cells (CTCs) are detected in approximately 30% of metastatic non-small-cell lung cancer (NSCLC) cases using the CellSearch system, which relies on EpCAM immunomagnetic enrichment and Cytokeratin detection. This study evaluated the effectiveness of immunomagnetic enrichment targeting oncofetal chondroitin sulfate (ofCS) using recombinant VAR2CSA proteins (rVAR2) to improve the recovery of different NSCLC cell lines spiked into lysed blood samples. Four NSCLC cell lines—NCI-H1563, A549, NCI-H1792, and NCI-H661—were used to assess capture efficiency. The results demonstrated that the combined use of anti-EpCAM antibody and rVAR2 significantly enhanced the capture efficiency to an average of 88.2% compared with 40.6% when using only anti-EpCAM and 56.6% when using only rVAR2. These findings suggest that a dual-marker approach using anti-EpCAM and rVAR2 can provide a more robust and sensitive method for CTC enrichment in NSCLC, potentially leading to better diagnostic and prognostic outcomes. Full article
(This article belongs to the Special Issue Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition)
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24 pages, 22049 KiB  
Article
Deciphering Dormant Cells of Lung Adenocarcinoma: Prognostic Insights from O-glycosylation-Related Tumor Dormancy Genes Using Machine Learning
by Chenfei Dong, Yang Liu, Suli Chong, Jiayue Zeng, Ziming Bian, Xiaoming Chen and Sairong Fan
Int. J. Mol. Sci. 2024, 25(17), 9502; https://doi.org/10.3390/ijms25179502 - 31 Aug 2024
Viewed by 943
Abstract
Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing [...] Read more.
Lung adenocarcinoma (LUAD) poses significant challenges due to its complex biological characteristics and high recurrence rate. The high recurrence rate of LUAD is closely associated with cellular dormancy, which enhances resistance to chemotherapy and evasion of immune cell destruction. Using single-cell RNA sequencing (scRNA-seq) data from LUAD patients, we categorized the cells into two subclusters: dormant and active cells. Utilizing high-density Weighted Gene Co-expression Network Analysis (hdWGCNA) and pseudo-time cell trajectory, aberrant expression of genes involved in protein O-glycosylation was detected in dormant cells, suggesting a crucial role for O-glycosylation in maintaining the dormant state. Intercellular communication analysis highlighted the interaction between fibroblasts and dormant cells, where the Insulin-like Growth Factor (IGF) signaling pathway regulated by O-glycosylation was crucial. By employing Gene Set Variation Analysis (GSVA) and machine learning, a risk score model was developed using hub genes, which showed high accuracy in determining LUAD prognosis. The model also demonstrated robust performance on the training dataset and excellent predictive capability, providing a reliable basis for predicting patient clinical outcomes. The group with a higher risk score exhibited a propensity for adverse outcomes in the tumor microenvironment (TME) and tumor mutational burden (TMB). Additionally, the 50% inhibitory concentration (IC50) values for chemotherapy exhibited significant variations among the different risk groups. In vitro experiments demonstrated that EFNB2, PTTG1IP, and TNFRSF11A were upregulated in dormant tumor cells, which also contributed greatly to the diagnosis of LUAD. In conclusion, this study highlighted the crucial role of O-glycosylation in the dormancy state of LUAD tumors and developed a predictive model for the prognosis of LUAD patients. Full article
(This article belongs to the Special Issue Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition)
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22 pages, 5368 KiB  
Article
Digital Whole Slide Image Analysis of Elevated Stromal Content and Extracellular Matrix Protein Expression Predicts Adverse Prognosis in Triple-Negative Breast Cancer
by Zsófia Karancsi, Barbara Gregus, Tibor Krenács, Gábor Cserni, Ágnes Nagy, Klementina Fruzsina Szőcs-Trinfa, Janina Kulka and Anna Mária Tőkés
Int. J. Mol. Sci. 2024, 25(17), 9445; https://doi.org/10.3390/ijms25179445 - 30 Aug 2024
Viewed by 546
Abstract
Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and limited treatment options. This study evaluates the prognostic value of stromal markers in TNBC, focusing on the tumor–stroma ratio (TSR) and overall stroma ratio (OSR) in whole slide [...] Read more.
Triple-negative breast cancer (TNBC) is a subtype of breast cancer with a poor prognosis and limited treatment options. This study evaluates the prognostic value of stromal markers in TNBC, focusing on the tumor–stroma ratio (TSR) and overall stroma ratio (OSR) in whole slide images (WSI), as well as the expression of type-I collagen, type-III collagen, and fibrillin-1 on tissue microarrays (TMAs), using both visual assessment and digital image analysis (DIA). A total of 101 female TNBC patients, primarily treated with surgery between 2005 and 2016, were included. We found that high visual OSR correlates with worse overall survival (OS), advanced pN categories, lower stromal tumor-infiltrating lymphocyte count (sTIL), lower mitotic index, and patient age (p < 0.05). TSR showed significant connections to the pN category and mitotic index (p < 0.01). High expression levels of type-I collagen (>45%), type-III collagen (>30%), and fibrillin-1 (>20%) were linked to significantly worse OS (p = 0.004, p = 0.013, and p = 0.005, respectively) and progression-free survival (PFS) (p = 0.028, p = 0.025, and p = 0.002, respectively), validated at the mRNA level. Our results highlight the importance of stromal characteristics in promoting tumor progression and metastasis and that targeting extracellular matrix (ECM) components may offer novel therapeutic strategies. Furthermore, DIA can be more accurate and objective in evaluating TSR, OSR, and immunodetected stromal markers than traditional visual examination. Full article
(This article belongs to the Special Issue Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition)
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21 pages, 12450 KiB  
Article
Comprehensive Analysis of a Six-Gene Signature Predicting Survival and Immune Infiltration of Liposarcoma Patients and Deciphering Its Therapeutic Significance
by Jiayang Han, Binbin Zhao, Xu Han, Tiantian Sun, Man Yue, Mengwen Hou, Jialin Wu, Mengjie Tu and Yang An
Int. J. Mol. Sci. 2024, 25(14), 7792; https://doi.org/10.3390/ijms25147792 - 16 Jul 2024
Viewed by 741
Abstract
Background: As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is [...] Read more.
Background: As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is essential to evaluate the prognosis of LPS patients. Methods: By comprehensive analysis of data derived from GEO datasets, differentially expressed genes (DEGs) were obtained. Univariate and Lasso Cox regressions were subsequently employed to reveal distant recurrence-free survival (DRFS)-associated DEGs and develop a prognostic gene signature, which was assessed by Kaplan–Meier survival and ROC curve. GSEA and immune infiltration analyses were conducted to illuminate molecular mechanisms and immune correlations of this model in LPS progression. Furthermore, a correlation analysis was involved to decipher the therapeutic significance of this model for LPS. Results: A six-gene signature was developed to predict DRFS of LPS patients and showed higher precision performance in more aggressive LPS subtypes. Then, a nomogram was further established for clinical application based on this risk model. Via GSEA, the high-risk group was significantly enriched in cell cycle-related pathways. In the LPS microenvironment, neutrophils, memory B cells and resting mast cells exhibited significant differences in cell abundance between high-risk and low-risk patients. Moreover, this model was significantly correlated with therapeutic targets. Conclusion: A prognostic six-gene signature was developed and significantly associated with cell cycle pathways and therapeutic target genes, which could provide new insights into risk assessment of LPS progression and therapeutic strategies for LPS patients to improve their prognosis. Full article
(This article belongs to the Special Issue Biomarkers of Tumor Progression, Prognosis and Therapy: 2nd Edition)
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