Prognostic and predictive biomarkers in non-small cell lung carcinoma

Investigating the frequencies of EGFR mutations and EGFR single nucleotide polymorphisms genotypes and their predictive role in NSCLC patients in Republic of Serbia

BACKGROUND

Genetic factors influence non-small cell lung cancer (NSCLC) development, progression and treatment response. Epidermal growth factor receptor (EGFR) variants, particularly single nucleotide polymorphisms (SNPs), were linked to clinical outcomes in NSCLC. The general objective of this study was to examine frequencies of -191 C/A and - 216G/T EGFR SNPs, EGFR mutation profiles and their associations among gender, age, and smoking status.

PATIENTS AND METHODS

A cohort of 211 NSCLC patients (131 males and 80 females) from the Republic of Serbia was analyzed. PCR-RFLP genotyping was used for EGFR SNPs, and real-time PCR for detection of EGFR mutations. Cramér's V statistic, Chi-square tests, and binary logistic regression, were employed to explore the associations between EGFR SNPs, EGFR mutation status, and demographic factors. Data were analyzed using SPSS-27 software (SPSS, Inc.) and R software (version 4.3.2).

RESULTS

Statistical significance with moderate associations was found between smoking status and EGFR mutation status. A significant correlation was also observed between smoking and the - 216GG genotype (p = 0.016). Notably, male smokers with EGFR wild-type status and female non-smokers with EGFR mutations showed the highest frequencies of the - 216GG genotype. Binary logistic regression confirmed that the - 216G/T (p = 0.049) and smoking status (p ≤ 0.001) were significantly associated with the presence of EGFR mutations in females.

CONCLUSION

The - 216G/T SNP and smoking status may serve as potential predictors for EGFR mutation status in NSCLC patients. Further studies are warranted to confirm these associations and assess their implications for personalized treatment approach.

In smokers, the axis NCAPG/hsa-let-7b-5p/TMPO-AS1 promotes lung adenocarcinoma

Background

Smoking is linked to high morbidity and mortality rates of lung cancer, emphasizing the need for a better understanding of prognosis-related mRNA/miRNA/lncRNA-ceRNA networks.

Materials and methods

The study utilized databases like OncoMX, The University of ALabama at Birmingham CANcer data (UALCAN), OncoDB, ENCORI, Kaplan-Meier (KM) Plotter, miRNet, CancerMIRNome, TISIDB, and TIMER2.0 to analyze NCAPG/miRNA and LncRNA expression in lung cancer tumors and healthy tissues.

Results

The NCAPG gene is overexpressed in lung cancer cells. High NCAPG expression is associated with adenocarcinoma patients with a log fold change of 8.7 in case of tumor vs. normal samples (t = 515, n = 59). Overexpression of NCAPG indicates poor overall survivability in lung adenocarcinoma (LUAD) patients [hazard ratio (HR) = 1.6, confidence interval (CI) = 1.34-1.9, p = 9.9e-08] and those with a smoking history (HR = 1.44, CI = 1.11-1.87, p = 0.0062), but not significantly associated with lung squamous cell carcinoma (LUSC). miRNA hsa-let-7b-5p negatively correlates (R = -0.348) with NCAPG expression, with its down expression associated with poor survivability (HR = 0.71), while lncRNA TMPO-AS1 positively correlates (R = 0.575) with the NCAPG axis, with its overexpression associated with poor survivability (HR = 2.16).

Conclusion

Elevated levels of NCAPG and TMPO-AS1 in lung adenocarcinoma patients lead to aggressive growth and poor prognosis. miRNA hsa-let-7b-5p, a key miRNA, may inhibit these factors, potentially improving patient prognosis. Further research and clinical trials are needed to validate this targeted therapy.

Dual-functional probe for sensitive detection of MCF-7 cells and mendelian randomization analysis of MUC1 association with multiple cancers

The present study successfully developed a method based on a dual-functional probe for detecting breast cancer cells MCF-7 by recognizing the MUC1 protein on the cell surface. This method integrated inductively coupled plasma mass spectrometry (ICP-MS) and fluorescence imaging technology, enhancing the sensitivity, specificity, and accuracy of breast cancer cell detection. Additionally, through two-sample Mendelian randomization (MR) analysis, we verified a potential correlation between breast cancer and MUC1 (PIVW<0.05), while also proving no potential correlation between liver cancer and MUC1 (PIVW>0.05). Furthermore, this study explored the relationship between other cancers and MUC1, indicating a potential correlation between ovarian cancer and colorectal cancer with MUC1 (PIVW<0.05). In summary, this study provides new strategies for the early diagnosis and treatment of breast cancer and offers new insights into the potential of MUC1 as a biomarker for the detection of other cancers.

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologists. Promising AI applications used for accomplishing diagnostic, prognostic and predictive tasks are being developed at a high pace. This is notably true in thoracic oncology, given the significant and rapid therapeutic progress made recently for lung cancer patients. Advances have been based on drugs targeting molecular alterations, immunotherapies, and, more recently antibody-drug conjugates which are soon to be introduced. For over a decade, many proof-of-concept studies have explored the use of AI algorithms in thoracic oncology to improve lung cancer patient care. However, despite the enthusiasm in this domain, the set-up and use of AI algorithms in daily practice of thoracic pathologists has not been operative until now, due to several constraints. The purpose of this review is to describe the potential but also the current barriers of AI applications in routine thoracic pathology for non-small cell lung cancer patient care and to suggest practical solutions for rapid future implementation.

MYST2 histone acetyltransferase promotes lung adenocarcinoma progression by regulating the p38 MAPK signaling pathway

BACKGROUND

Lung cancer, particularly lung adenocarcinoma, poses a significant health challenge due to its high incidence and mortality rates. Despite advancements in targeted therapies, treatment outcomes for lung adenocarcinoma remain unsatisfactory. This study explores the role of the histone acetyltransferase MYST2 in lung adenocarcinoma and its potential as a therapeutic target.

METHODS

An analysis using the TIMER 2.0 and TCGA databases was performed to compare the expression levels of MYST2 between lung adenocarcinoma tissues and normal tissues. Functional assays, including cell proliferation, migration, and invasion, were conducted to evaluate the effects of MYST2 overexpression and knockout in lung cancer cells. Co-immunoprecipitation and GST pull-down assays were utilized to identify interactions involving the MYST domain of MYST2 and p38, while also assessing the impact of MYST2 on the binding between MEK6 and p38.

RESULTS

The analysis revealed that MYST2 was significantly up-regulated in lung adenocarcinoma tissues compared to normal tissues and was associated with poor prognosis. Functional assays demonstrated that MYST2 overexpression promoted, whereas MYST2 knockout inhibited, lung cancer cell proliferation, migration, and invasion. Mechanistically, MYST2 enhanced the phosphorylation of p38 and ERK. Co-immunoprecipitation and GST pull-down assays identified the MYST domain of MYST2 as crucial for its interaction with p38. Additionally, MYST2 overexpression facilitated the binding of MEK6 to p38, indirectly influencing p38 activity.

CONCLUSION

These findings suggest that MYST2 acts as an oncogene in lung cancer by modulating p38 phosphorylation through the MYST domain, underscoring its potential as a prognostic marker and therapeutic target.

Antibody-Drug Conjugates for the Treatment of Non-Small Cell Lung Cancer with Central Nervous System Metastases

Antibody-drug conjugates (ADCs) represent an emerging class of targeted anticancer agents that have demonstrated impressive efficacy in numerous cancer types. In non-small cell lung cancer (NSCLC), ADCs have become a component of the treatment armamentarium for a subset of patients with metastatic disease. Emerging data suggest that some ADCs exhibit impressive activity even in central nervous system (CNS) metastases, a disease site that is difficult to treat and associated with poor prognosis. Herein, we describe and summarize the existing evidence surrounding ADCs in NSCLC with a focus on CNS activity.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.