Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.8
4.4
Journals
Cancers
Special Issues
Lung Cancer—Advances in Therapy and Prognostic Prediction
cancers-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Cancers Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Lung Cancer—Advances in Therapy and Prognostic Prediction

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 938

Special Issue Editors

Dr. Chinmay T. Jani

E-Mail Website
Guest Editor
Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
Interests: lung cancer; immunotherapy resistance; novel therapeutics; biomarkers
Dr. Estelamari Rodríguez

E-Mail Website
Guest Editor
Division of Medical Oncology, Department of Medicine, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL 33136, USA
Interests: lung cancer; cancer disparities research; early phase clinical trials

Special Issue Information

Dear Colleagues,

We are pleased to announce an upcoming Special Issue dedicated to the rapidly evolving landscape of lung cancer, with a focus on therapeutic advancements and prognostic prediction. This Issue will highlight innovative, biomarker-driven treatment strategies—including targeted therapies, immunotherapy combinations, and precision medicine approaches that integrate molecular profiling to guide individualized care.

Emphasis will be placed on studies that advance our understanding of how genomic, transcriptomic, and liquid biopsy-based biomarkers can inform treatment selection, monitor disease progression, and refine prognostic models. As lung cancer care becomes increasingly personalized, this Special Issue aims to feature multidisciplinary research that reflects the shift toward precision oncology.

We invite researchers, clinicians, and thought leaders to contribute original research articles, reviews, and commentaries that will define the next chapter in therapeutic advancements in lung cancer.

Dr. Chinmay T. Jani
Dr. Estelamari Rodríguez
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 250 words) can be sent to the Editorial Office for assessment.

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

  • lung cancer
  • biomarkers
  • targeted therapies
  • immunotherapy
  • precision medicine

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1383 KB  
Article
Exploratory Immunohistochemical Profiling of FOXP3, PD-1 and CD32B in Resectable Lung Adenocarcinoma
by Long-Wei Lin, Hong-Jing Chuang, Kuan-Hsun Lian, Yu-Ting Tseng and Chung-Yu Chen
Cancers 2025, 17(23), 3886; https://doi.org/10.3390/cancers17233886 - 4 Dec 2025
Viewed by 178
Abstract
Background: Regulatory T cells (FOXP3+), checkpoint signaling (PD-1), and inhibitory B-cell signaling (CD32B/FCGR2B) may shape recurrence risk after resection of lung adenocarcinoma, but small, stage-heterogeneous cohorts complicate inference. Methods: We profiled 21 resected lung adenocarcinomas by immunohistochemistry (IHC) for CD3, CD8, [...] Read more.
Background: Regulatory T cells (FOXP3+), checkpoint signaling (PD-1), and inhibitory B-cell signaling (CD32B/FCGR2B) may shape recurrence risk after resection of lung adenocarcinoma, but small, stage-heterogeneous cohorts complicate inference. Methods: We profiled 21 resected lung adenocarcinomas by immunohistochemistry (IHC) for CD3, CD8, FOXP3, PD-1, CD19, and CD32B. Five systematically sampled 200× fields per stain were quantified in ImageJ to derive continuous percentages and prespecified ratios: FOXP3/CD8 and CD32B/CD19 (primary), and PD-1/CD8 (exploratory). Analyses emphasized effect sizes with exact non-parametric tests for clinicopathologic associations and Cox time-to-event models for disease-free survival (DFS). Kaplan–Meier plots used median splits for visualization only. Results: Higher immunosuppressive balance associated with adverse features and shorter DFS. Patients with higher FOXP3/CD8 and CD32B/CD19 had markedly shorter DFS on K-M displays (FOXP3/CD8: 18.9 vs. 45.6 months; CD32B/CD19: 25.0 vs. 72.8 months). In Cox models, each ratio was associated with increased hazard of recurrence (FOXP3+PD-1/CD8, HR 2.03, 95% CI 1.26–3.29; CD32B/CD19, HR 1.98, 95% CI 1.16–3.37). Conclusions: In this hypothesis-generating pilot, an immunosuppressive tumor microenvironment, indexed by higher FOXP3 (relative to CD8) and higher CD32B (relative to CD19), portends earlier recurrence after surgery. These results support external validation in larger, stage-balanced cohorts and motivate incorporation of quantitative IHC ratios into postoperative risk stratification. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

23 pages, 1835 KB  
Article
TILDA-X: Transcriptome-Informed Lung Cancer Disparities via Explainable AI
by Masrur Sobhan, Md Mezbahul Islam, Mary Jo Trepka, Gregory E. Holt, Charles J. Dimitroff and Ananda M. Mondal
Cancers 2025, 17(21), 3454; https://doi.org/10.3390/cancers17213454 - 28 Oct 2025
Viewed by 553
Abstract
Background: Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Identifying both patient-specific and cohort-specific disparity biomarkers is critical for developing targeted treatments. The lung cancer dataset is highly imbalanced [...] Read more.
Background: Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Identifying both patient-specific and cohort-specific disparity biomarkers is critical for developing targeted treatments. The lung cancer dataset is highly imbalanced across races, leading to biased results in disparity information if classification is based on race. Method: This study developed an explainable artificial intelligence-based framework, TILDA-X, which designs classification models based on disease conditions instead of races to mitigate racial imbalance in the dataset and applies explainable AI to delineate patient-specific disparity information. A lung cancer transcriptome dataset with three disease conditions—lung adenocarcinoma, lung squamous cell carcinoma, and healthy samples—was used to develop classification models. Applying a bottom-up approach from patient-specific disparity information, the cohort-specific disparity information is discovered for different racial and sex groups, African American males, European American males, African American females, and European American females. Results: Classification based on disease conditions achieved accuracy between 88% and 100% for minority groups (African American males and females), whereas it was only between 0% and 16% for race-based classification, which underscores the significance of the proposed approach. Functional analysis of sub-cohort-specific biomarker genes revealed unique pathways associated with lung cancers in different races and sexes. Among the significant pathways identified, over ~63% overlapped with previously reported lung cancer-related studies, supporting the biological validity of our findings. Overall, combining disease conditions-based classification with explainable AI, this study provides a robust, interpretable framework for characterizing race- and sex-specific disparities in lung cancer, offering a foundation for precision oncology and equitable therapeutic development based on transcriptome profile only. Full article
(This article belongs to the Special Issue Lung Cancer—Advances in Therapy and Prognostic Prediction)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop