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Medicina
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Insights and Advances in Cancer Biomarkers
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Insights and Advances in Cancer Biomarkers

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 6666

Special Issue Editor

Dr. Krishan Jain

E-Mail Website
Guest Editor
1. Department of Surgery, Health Sciences Division, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA
2. Burn and Shock Trauma Research Institute, Health Sciences Division, Stritch School of Medicine, Loyola University Chicago, Maywood, IL 60153, USA
Interests: cellular immunotherapy; allogeneic cell sources; stem cells; regenerative medicine; tissue engineering; cell differentiation; multiomics; machine learning; generative AI

Special Issue Information

Dear Colleagues,

Cancer biomarkers play a crucial role in modern oncology, enabling early diagnosis, prognosis assessment, and personalized treatment strategies. The concept of cancer biomarkers dates back to the 19th century, when the light chain of immunoglobulin was identified as the first-ever cancer biomarker in patients with myeloma. Since then, numerous biomarkers have been discovered, including alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). These markers provide insights into cancer biology, guide treatment decisions, and contribute to ongoing research in precision oncology. As technology advances, the search for new and better biomarkers continues, aiming to improve patient outcomes and transform cancer care.

Medicina is launching a Special Issue titled “Insights and Advances in Cancer Biomarkers”. This Special Issue aims to comprehensively explore the intricate connections between cancer initiation, progression, chemotherapy resistance, and cancer recurrence, which significantly impact clinical practice and public health. Researchers and colleagues are cordially invited to contribute original research articles addressing novel approaches in cancer research. Submissions covering basic to clinical research are encouraged, particularly those focused on identifying new biomarkers and applying novel technologies, including multiomics and generative AI, to develop noninvasive tools for rapid diagnosis and therapeutic development. Additionally, review articles summarizing recent advancements in cancer biomarker research are welcome, with the goal of developing novel diagnostic methods and therapeutics for cancer treatment. Let us work together to advance our understanding and management of these critical health challenges.

Cutting-edge research in cancer biomarkers is advancing rapidly. Researchers are exploring liquid biopsies, single-cell sequencing, immune biomarkers, epigenetic markers, and microbiome and metabolomics driven by technological innovations to gain a deeper understanding of cancer biology. Liquid biopsies are non-invasive and provide real-time information about tumor mutations, heterogeneity, and treatment response. Advances in single-cell sequencing reveal cellular diversity, clonal evolution, and drug resistance mechanisms, leading to more precise treatment strategies. Furthermore, immune checkpoint inhibitors have revolutionized cancer therapy. Researchers are identifying immune-related biomarkers to predict patient response and guide immunotherapy decisions. Studying DNA methylation patterns and histone modifications helps identify novel biomarkers for early detection and prognosis. Investigating the tumor microenvironment, gut microbiome, and metabolic pathways reveals potential biomarkers associated with cancer risk, progression, and treatment outcomes.

Dr. Krishan Jain
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. Medicina is an international peer-reviewed open access monthly 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 2200 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

  • liquid biopsies
  • cancer biomarkers
  • cellular immunotherapy
  • single-cell sequencing
  • immune biomarkers
  • epigenetic markers
  • microbiome and metabolomics
  • proteomics
  • multiomics
  • machine learning
  • generative AI

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Published Papers (6 papers)

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Research

15 pages, 1045 KiB  
Article
Metabolomic Profiling of Erector Spinae Plane Block for Breast Cancer Surgery
by Ekin Guran, Ozan Kaplan, Serpil Savlı, Cigdem Sonmez, Lutfi Dogan and Suheyla Unver
Medicina 2025, 61(7), 1294; https://doi.org/10.3390/medicina61071294 - 18 Jul 2025
Viewed by 275
Abstract
Background and Objectives: Regional and systemic analgesic techniques, such as erector spinae plane (ESP) block and opioid administration, implemented during cancer surgery, have been shown to influence immune responses and potentially affect cancer outcomes. Surgical stress and analgesic techniques used in cancer surgery—such [...] Read more.
Background and Objectives: Regional and systemic analgesic techniques, such as erector spinae plane (ESP) block and opioid administration, implemented during cancer surgery, have been shown to influence immune responses and potentially affect cancer outcomes. Surgical stress and analgesic techniques used in cancer surgery—such as regional nerve blocks or systemic opioids—not only affect pain control but also influence immune and inflammatory pathways that may impact cancer progression. To understand the biological consequences of these interventions, metabolomic profiling has emerged as a powerful approach for capturing systemic metabolic and immunological changes, which are particularly relevant in the oncologic perioperative setting. In this study, we examined the impact of the ESP on the metabolomic profile, as well as levels of VEGF, cortisol, and CRP, in addition to its analgesic effects in breast cancer surgery. Materials and Methods: Ninety patients were placed into three different analgesia groups (morphine, ESP, and control groups). Demographic data, ASA classification, comorbidities, surgery types, and pain scores were documented. Blood samples were taken at preoperative hour 0, postoperative hour 1, and postoperative hour 24 (T0, T1, and T24). VEGF, cortisol, and CRP levels were measured, and metabolomic analysis was performed. Results: Study groups were comparable regarding demographic findings, comorbidities, and surgery types (p > 0.05). NRS scores of group ESP were lowest in the first 12 h period (p < 0.01) and ESP block reduced opioid consumption (p < 0.01). VEGF and cortisol levels of group morphine were similar to ESP at T24 (p > 0.05). Group ESP had lower VEGF and cortisol levels than the control at T24 (p = 0.025, p = 0.041, respectively.). The CRP level of group morphine was higher than both ESP and control at T24 (p = 0.022). Metabolites involved in primary bile acid, steroid hormone biosynthesis, amino acid, and glutathione metabolism were changed in group ESP. Conclusions: Metabolites in bile acid biosynthesis and steroid hormone pathways, which play a key role in immune responses, were notably lower in the ESP group. Accordingly, VEGF and cortisol peaks were more moderate in group ESP. In conclusion, we think that ESP block, which provides adequate analgesia, is an acceptable approach in terms of modulating immune responses in breast cancer surgery. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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10 pages, 653 KiB  
Article
Immune and Inflammation Markers as a Predictor of Overall Survival in Patients with Hematologic Malignancies: A Retrospective Cohort Study
by Mehmet Ali Ucar, Anıl Tombak, Aydın Akdeniz, Hüseyin Derya Dinçyürek, Meryem Şener, Mahmut Bakır Koyuncu, Eyüp Naci Tiftik and Recep Dokuyucu
Medicina 2025, 61(6), 1019; https://doi.org/10.3390/medicina61061019 - 30 May 2025
Viewed by 563
Abstract
Background and Objectives: this study aimed to evaluate the prognostic significance of systemic immune-inflammatory markers, particularly the pan-immune-inflammation value (PIV) and systemic immune-inflammation Index (SII), in predicting overall survival among patients with hematologic malignancies. Materials and Methods: This retrospective cohort study included 300 [...] Read more.
Background and Objectives: this study aimed to evaluate the prognostic significance of systemic immune-inflammatory markers, particularly the pan-immune-inflammation value (PIV) and systemic immune-inflammation Index (SII), in predicting overall survival among patients with hematologic malignancies. Materials and Methods: This retrospective cohort study included 300 patients diagnosed with various hematologic malignancies between January 2020 and January 2025 at the Department of Hematology, Faculty of Medicine, Mersin University. Baseline laboratory data, including neutrophil, lymphocyte, platelet, and monocyte counts, were collected to calculate SII, NLR, PLR, and PIV. Patients were stratified into high and low groups based on the median values of these markers. Overall survival was analyzed using Kaplan–Meier curves and Cox proportional hazards models, adjusted for age, sex, malignancy type, and disease stage. Results: High levels of PIV and SII were significantly associated with poorer overall survival. In univariate analysis, high PIV (HR: 2.35, 95% CI: 1.68–3.28, p < 0.001) and high SII (HR: 2.12, 95% CI: 1.53–2.95, p < 0.001) were strong predictors of mortality. After multivariate adjustment, PIV (adjusted HR: 2.14, 95% CI: 1.47–3.11, p < 0.001) and SII (adjusted HR: 1.88, 95% CI: 1.32–2.67, p = 0.001) remained independent prognostic factors. Subgroup analyses demonstrated that the predictive power of PIV and SII was consistent across different malignancy types, particularly in acute myeloid leukemia and multiple myeloma patients. Conclusions: Our findings indicated that systemic immune-inflammatory markers, particularly PIV and SII, are valuable prognostic tools in hematologic malignancies. These markers, derived from routine blood counts, offer a simple cost-effective means for improving risk stratification. Incorporating these indices into clinical practice could enhance individualized management strategies. Further prospective studies are warranted to validate these findings. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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20 pages, 2591 KiB  
Article
Prognostic Immune and Nutritional Index as a Predictor of Survival in Patients Undergoing Curative-Intent Resection for Gastric Cancer
by Soomin An, Wankyu Eo and Sookyung Lee
Medicina 2025, 61(6), 1015; https://doi.org/10.3390/medicina61061015 - 29 May 2025
Viewed by 552
Abstract
Background and Objectives: The prognostic immune and nutritional index (PINI) was reported to be clinically relevant for colorectal cancer prognosis. Herein, the utility of PINI as a prognostic factor for the survival of patients with gastric cancer (GC) was investigated. Materials and [...] Read more.
Background and Objectives: The prognostic immune and nutritional index (PINI) was reported to be clinically relevant for colorectal cancer prognosis. Herein, the utility of PINI as a prognostic factor for the survival of patients with gastric cancer (GC) was investigated. Materials and Methods: We retrospectively analyzed 492 patients with stage I–III GC, predominantly of Asian descent, who underwent curative-intent gastrectomy. Multivariate Cox regression analysis identified independent predictors of overall survival (OS). Model performance was evaluated using the concordance index (C-index), integrated area under the curve (iAUC), time-dependent AUC, integrated discrimination improvement (IDI), and continuous net reclassification improvement (cNRI). Results: The PINI score—calculated as [albumin (g/dL) × 0.9] − [absolute monocyte count (/μL) × 0.0007]—was found to be independently associated with OS (p < 0.001). Additional independent prognostic factors included age, body mass index, 5-factor modified frailty index, tumor–node–metastasis (TMN) stage, gastrectomy type, and anemia. The full model that included all significant covariates outperformed the baseline TNM model, showing significantly higher C-index and iAUC values (both p < 0.001). Compared with an intermediate model, which excluded PINI, the full model demonstrated a superior C-index and iAUC (both p = 0.004). Although the observed improvements in AUC, IDI, and cNRI at 3 years were not statistically significant, the full model achieved significant gains in all three metrics at 5 years, underscoring the added long-term prognostic value of the PINI. Conclusions: PINI is a significant independent predictor of survival in patients with GC who underwent curative-intent surgery. Its inclusion in prognostic models enhances the long-term predictive accuracy for survival, supporting its potential role in guiding personalized postoperative management. External validation in larger multi-ethnic prospective cohorts is essential to confirm its generalizability and to establish its role in routine clinical practice. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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18 pages, 2075 KiB  
Article
Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detection
by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem and Luca Paolo Ardigò
Medicina 2025, 61(4), 581; https://doi.org/10.3390/medicina61040581 - 25 Mar 2025
Cited by 2 | Viewed by 1182
Abstract
Aim: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatments are available for BC, most cancer-related deaths are due to incurable metastases. Therefore, the early [...] Read more.
Aim: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatments are available for BC, most cancer-related deaths are due to incurable metastases. Therefore, the early diagnosis and treatment of BC are crucial before metastasis. Mammography and ultrasonography are primarily used in the clinic for the initial identification and staging of BC; these methods are useful for general screening but have limitations in terms of sensitivity and specificity. Omics-based biomarkers, like metabolomics, can make early diagnosis much more accurate, make tracking the disease’s progression more accurate, and help make personalized treatment plans that are tailored to each tumor’s specific molecular profile. Metabolomics technology is a feasible and comprehensive method for early disease detection and biomarker identification at the molecular level. This research aimed to establish an interpretable predictive artificial intelligence (AI) model using plasma-based metabolomics panel data to identify potential biomarkers that distinguish BC individuals from healthy controls. Methods: A cohort of 138 BC patients and 76 healthy controls were studied. Plasma metabolites were examined using LC-TOFMS and GC-TOFMS techniques. Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were evaluated using performance metrics such as Receiver Operating Characteristic-Area Under the Curve (ROC AUC), accuracy, sensitivity, specificity, and F1 score. ROC and Precision-Recall (PR) curves were generated for comparative analysis. The SHapley Additive Descriptions (SHAP) analysis evaluated the optimal prediction model for interpretability. Results: The RF algorithm showed improved accuracy (0.963 ± 0.043) and sensitivity (0.977 ± 0.051); however, LightGBM achieved the highest ROC AUC (0.983 ± 0.028). RF also achieved the best Precision-Recall Area under the Curve (PR AUC) at 0.989. SHAP search found glycerophosphocholine and pentosidine as the most significant discriminatory metabolites. Uracil, glutamine, and butyrylcarnitine were also among the significant metabolites. Conclusions: Metabolomics biomarkers and an explainable AI (XAI)-based prediction model showed significant diagnostic accuracy and sensitivity in the detection of BC. The proposed XAI system using interpretable metabolite data can serve as a clinical decision support tool to improve early diagnosis processes. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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15 pages, 1180 KiB  
Article
Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence
by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem and Luca Paolo Ardigò
Medicina 2025, 61(3), 405; https://doi.org/10.3390/medicina61030405 - 26 Feb 2025
Cited by 1 | Viewed by 1538
Abstract
Background and Objectives: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) [...] Read more.
Background and Objectives: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. Materials and Methods: This study included 219 patients diagnosed with liver cancer and 219 healthy controls. Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), t-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model’s predictive decisions. Results: Notable alterations in lipid profiles were observed: decreased sphingomyelins (SM d39:2, SM d41:2) and increased fatty acids (FA 14:1, FA 22:2) and phosphatidylcholines (PC 34:1, PC 32:1). AdaBoost exhibited a superior classification performance, achieving an AUC of 0.875. SHAP identified PC 40:4 as the most efficacious lipid for model predictions. The SM d41:2 and SM d36:3 lipids were specifically associated with an increased risk of low-onset cancer and elevated levels of the PC 40:4 lipid. Conclusions: This study demonstrates that untargeted lipidomics, in conjunction with explainable artificial intelligence (XAI) and machine learning, may effectively identify biomarkers for the early detection of liver cancer. The results suggest that alterations in lipid metabolism are crucial to the progression of liver cancer and provide valuable insights for incorporating lipidomics into precision oncology. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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18 pages, 1641 KiB  
Article
Large Unstained Cells: A Predictive Biomarker for Recurrence and Survival in Resected Gastric Cancer
by Furkan Ceylan, Ateş Kutay Tenekeci, Burak Bilgin, Mehmet Ali Nahit Şendur, Mutlu Hızal, Fahriye Tuba Köş and Didem Şener Dede
Medicina 2025, 61(2), 208; https://doi.org/10.3390/medicina61020208 - 24 Jan 2025
Viewed by 1263
Abstract
Background and Objectives: Despite advances in surgery and perioperative chemotherapy, locally advanced gastric cancer continues to pose significant challenges, creating a pressing need for biomarkers capable of predicting therapeutic efficacy and survival outcomes. This study evaluates the prognostic and predictive significance of large [...] Read more.
Background and Objectives: Despite advances in surgery and perioperative chemotherapy, locally advanced gastric cancer continues to pose significant challenges, creating a pressing need for biomarkers capable of predicting therapeutic efficacy and survival outcomes. This study evaluates the prognostic and predictive significance of large unstained cells (LUCs), a morphologically distinct subset of white blood cells identified in peripheral blood that remain unstained by standard hematological dyes, as potential indicators of immune competence and treatment response. Materials and Methods: This retrospective analysis included patients diagnosed with locally advanced gastric cancer (cT2-4, N0-3) at Ankara Bilkent City Hospital between January 2018 and November 2024. Primary endpoints were overall survival (OS) and disease-free survival (DFS), stratified by LUC levels. The secondary endpoint was the association between LUC levels and pathological tumor response. Results: A total of 180 patients were analyzed, with a median age of 59 years; a total of 76% were male. The median follow-up period was 16.5 months, during which OS and DFS rates were 82% and 66%, respectively. Most patients were presented with advanced-stage disease, including T3–T4 tumors (91%) and nodal positivity (81%). Stratification by LUC levels revealed significantly shorter DFS (HR: 2.12; 95% CI: 1.12–4.01; p = 0.020) and OS (HR: 3.37; 95% CI: 1.26–9.03; p = 0.015) in the low-LUC group compared to the high-LUC group. Furthermore, the high-LUC group exhibited a significantly higher tumor shrinkage rate (ypN0: 60% vs. 44%; p = 0.020), although tumor regression scores were similar across groups. Advanced tumor stage and lack of pathological response were strongly associated with reduced DFS and OS, while poorly cohesive carcinoma histology emerged as a predictor of inferior OS. Conclusions: This study demonstrates that elevated LUC levels are significantly associated with improved DFS and OS, as well as enhanced tumor shrinkage, in patients with locally advanced gastric cancer. These findings show the potential of LUCs as a promising biomarker for prognostication and therapeutic stratification in this population, offering a novel avenue for refining clinical decision-making. Further validation through prospective investigations is warranted. Full article
(This article belongs to the Special Issue Insights and Advances in Cancer Biomarkers)
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