Immunologic prognostic factors in lung cancer have not been fully clarified. We report the results of a prospective study undertaken to clarify the correlation between various cellular immunologic parameters and the survival of lung cancer patients. A total of 287 lung cancer patients were enrolled in this study. Representative in vitro cellular immune activities including lymphoblastogenesis and natural killer cell activities, in addition to the percentage of main lymphocyte subsets (CD3, CD4, CD8, HLA-DR, and Fc gamma R III on T cells) in the peripheral blood were evaluated before the initiation of therapy. The immune factors that influence the prognosis were analyzed by the log rank test and a multivariate analysis using the Cox proportional hazards model. Univariate analysis of the survival curves revealed a significant difference with regard to disease stage (P<0.0001), age (P=0.007), gender (P=0.0037), and HLA-DR (%) (P=0.048), when all the non-small cell lung cancer (NSCLC) patients (n=257) were analyzed together. This analysis, based on the histologic type, revealed that HLA-DR (%) was a significant predictor of survival in squamous cell carcinoma (P=0.0013) and small cell carcinoma (P=0.0025). A decreased CD4/CD8 ratio in small cell carcinoma (P=0.0062) and male gender in adenocarcinoma (P=0.0086) were factors associated with a worse prognosis. Multivariate analysis identified a significant correlation between survival and disease stage (P<0.0001) and gender (P=0.0243) in adenocarcinoma, disease stage (P<0.0001), age (P=0.0436) and HLA-DR (%) (P=0.0142) in squamous cell carcinoma, and HLA-DR (%) (P=0.0212) and CD4/CD8 (P=0.0112) in small cell carcinoma, suggesting independent prognostic significance. A variety of immunologic indices have prognostic significance for the different types of lung cancer. Among these, the HLA-DR (%) in the peripheral blood is the most reliable factor for squamous cell carcinoma and small cell carcinoma.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s0169-5002(02)00100-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prediction of pulmonary function decline in fibrous interstitial lung abnormalities based on quantitative chest CT parameters.
BMC Med Imaging
January 2025
Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
Background: Interstitial lung abnormalities (ILA) are a proposed imaging concept. Fibrous ILA have a higher risk of progression and death. Clinically, computed tomography (CT) examination is a frequently used and convenient method compared with pulmonary function tests.
View Article and Find Full Text PDFNiche-derived Semaphorin 4A safeguards functional identity of myeloid-biased hematopoietic stem cells.
Nat Aging
January 2025
Translational Science and Therapeutics Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Somatic stem cell pools comprise diverse, highly specialized subsets whose individual contribution is critical for the overall regenerative function. In the bone marrow, myeloid-biased hematopoietic stem cells (myHSCs) are indispensable for replenishment of myeloid cells and platelets during inflammatory response but, at the same time, become irreversibly damaged during inflammation and aging. Here we identify an extrinsic factor, semaphorin 4A (Sema4A), which non-cell-autonomously confers myHSC resilience to inflammatory stress.
View Article and Find Full Text PDFComparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis.
Sci Rep
January 2025
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 11461, Riyadh, Saudi Arabia.
Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates.
View Article and Find Full Text PDFIntricacies of human-AI interaction in dynamic decision-making for precision oncology.
Nat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
View Article and Find Full Text PDFRole of PGC-1α in the proliferation and metastasis of malignant tumors.
J Mol Histol
January 2025
Department of Thoracic Surgery, Lung Cancer Diagnosis and Treatment Center of Dalian, The First Affiliated Hospital of Dalian Medical University, Dalian, 116011, China.
Malignant tumors are among the major diseases threatening human survival in the world, and advancements in medical technology have led to a steady increase in their detection rates worldwide. Despite unique clinical presentations across the spectrum of malignancies, treatment modalities generally adhere to common strategies, encompassing primarily surgical intervention, radiation therapy, chemotherapy, and targeted treatments. Uncovering the genetic elements contributing to cancer cell proliferation, metastasis, and drug resistance remains a pivotal pursuit in the development of novel targeted therapeutics.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!