Molecular profiling and utility of cell-free DNA in nonsmall carcinoma of the lung: Study in a tertiary care hospital

Ghosh, Moupali; Mukhopadhyay, Madhumita; Das, Chhanda; Chatterjee, Sayan; Naskar, Bidisha Ghosh

doi:10.4103/jcrt.jcrt_99_20

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…Nevertheless, the ability of tumor markers to detect early-stage lung cancer is not optimal, as indicated by the sensitivity rates reported in studies [11,12]. Additionally, no individual serum tumor marker is exclusively reliable for lung cancer detection [13,14]. Enhancing the early diagnosis rates of lung cancer has become a focal point of research, with investigations focusing on combining traditional biological markers present in the peripheral blood, including tumor markers, can be used as indicators.…”

Section: Introductionmentioning

confidence: 99%

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Zhou,

Ge,

Yang

et al. 2024

AJCEM

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The aim of this research was to develop a lung cancer diagnostic and predictive model that integrates traditional laboratory indicators with tumor markers. This model is intended to facilitate early screening and assist in the process of identifying or detecting lung cancer through a cost-effective, rapid, and convenient approach, ultimately enhancing the early detection rate of lung cancer. A retrospective study was conducted on 66 patients diagnosed with lung cancer and 159 patients with benign pulmonary conditions. Data including general clinical information, conventional laboratory test results, and tumor marker levels were collected. Data analysis was conducted using SPSS 26.0 (Statistical Product and Service Solutions 26.0). The lung cancer diagnosis and prediction model is created using a composite index established through binary logistic regression. The combined diagnostic prediction models, incorporating both traditional indicators and tumor markers, demonstrated a greater area under the curve (AUC) when compared to the diagnostic prediction model based solely on tumor markers and their combination testing. The values of cut-off point, AUC, accuracy, sensitivity, specificity, positive and negative detection rate and accuracy rate are 0.1805, 0.959, 86.67%, 0.955, 0.830, 95.45%, 83.02% and 89.33 respectively and it is shown that the combined diagnostic model display notable efficacy and clinical relevance in aiding the early diagnosis of lung cancer.

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Section: Introductionmentioning

confidence: 99%

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Zhou,

Ge,

Yang

et al. 2024

AJCEM

View full text Add to dashboard Cite

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Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients

Lin,

Wang,

2024

Journal of Cancer Research and Therapeutics

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Background: Urinary cell-free deoxyribonucleic acid (DNA) (ucfDNA) holds promise as a biomarker; however, its potential remains largely unexplored. We examined the fragmentation pattern of ucfDNA and identified somatic mutations within urine samples from metastatic breast cancer (MBC) patients. Methods: Urine and blood specimens were collected before treatment from 45 MBC patients and posttreatment urine samples from 16 of the 45 patients at the China National Cancer Center. Somatic mutations and tumor mutational burden (TMB) in the urine and plasma of 10 patients were analyzed by next-generation sequencing (NGS). Fragmentation patterns of cfDNA were displayed using electropherograms. Differences in the extracted amount of cfDNA, length of cfDNA fragments, and TMB between urine and plasma were compared using a Wilcoxon test. Results: The fragmentation patterns of ucfDNA were categorized as follows: (1) profile A (n = 26) containing a short peak (100–200 bp) and a long peak (>1500 bp); (2) profile B (n = 8) containing only a long peak; and (3) profile C (n = 11) containing flat pattern. For profile A patients, the short-peaked ucfDNA circulating in the bloodstream was much shorter compared with plasma cfDNA (149 vs. 171 bp, Wilcoxon test, P = 0.023). The fragmentation patterns in lung metastasis patients exhibited a higher propensity toward profile C (P = 0.002). After treatment, 87.5% of the patients exhibited consistent fragmentation patterns. The concordance rate for somatic mutations in the plasma and urine was 30%, and the median TMB of urine and plasma was not significantly different. Conclusions: This study established a fragmentation pattern for ucfDNA and detected somatic mutations in the urine of MBC patients. These results suggest the potential application of ucfDNA as a biomarker for MBC.

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Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

Li,

Liu

et al. 2023

Journal of Cancer Research and Therapeutics

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Background: Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making. Materials and Methods: We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively). Results: The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC. Conclusions: The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

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Molecular profiling and utility of cell-free DNA in nonsmall carcinoma of the lung: Study in a tertiary care hospital

Cited by 4 publications

References 40 publications

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Regression–Based Diagnostic Models for Early Lung Cancer Integrating Conventional Indicators with Tumor Markers

Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers

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