Prognostic impact of cytological fluid tumor markers in non-small cell lung cancer

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma

Objective

Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance.

Methods

Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed.

Results

Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed.

Conclusion

Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.

Prognostic value of blood-based protein biomarkers in non-small cell lung cancer: A critical review and 2008-2022 update

BACKGROUND

Therapeutic possibilities for non-small cell lung cancer (NSCLC) have considerably increased during recent decades.

OBJECTIVE

To summarize the prognostic relevance of serum tumor markers (STM) for early and late-stage NSCLC patients treated with classical chemotherapies, novel targeted and immune therapies.

METHODS

A PubMed database search was conducted for prognostic studies on carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase, squamous-cell carcinoma antigen, progastrin-releasing-peptide, CA125, CA 19-9 and CA 15-3 STMs in NSCLC patients published from 2008 until June 2022.

RESULTS

Out of 1069 studies, 141 were identified as meeting the inclusion criteria. A considerable heterogeneity regarding design, patient number, analytical and statistical methods was observed. High pretherapeutic CYFRA 21-1 levels and insufficient decreases indicated unfavorable prognosis in many studies on NSCLC patients treated with chemo-, targeted and immunotherapies or their combinations in early and advanced stages. Similar results were seen for CEA in chemotherapy, however, high pretherapeutic levels were sometimes favorable in targeted therapies. CA125 is a promising prognostic marker in patients treated with immunotherapies. Combinations of STMs further increased the prognostic value over single markers.

CONCLUSION

Protein STMs, especially CYFRA 21-1, have prognostic potential in early and advanced stage NSCLC. For future STM investigations, better adherence to comparable study designs, analytical methods, outcome measures and statistical evaluation standards is recommended.

Expression changes and clinical significance of serum neuron-specific enolase and squamous cell carcinoma antigen in lung cancer patients after radiotherapy

OBJECTIVE

To explore the changes and clinical significance of serum Neuron-Specific Enolase (NSE) and Squamous Cell Carcinoma antigen (SCC) in patients with lung cancer before and after radiotherapy.

METHODS

82 patients with lung cancer were treated with radiotherapy, and effective clinical intervention was given during the radiotherapy process. The patients were followed up for 1 year after radiotherapy and were divided into a recurrence and metastasis group (n = 28) and a non-recurrence and metastasis group (n = 54) according to their prognosis. Another 54 healthy volunteers examined in the present study's hospital during the same period were selected as the control group. To compare the changes of NSE and SCC levels in serum in patients with lung cancer at admission and after radiotherapy, and to explore their clinical significance.

RESULTS

After intervention, NSE and SCC levels in the serum of the two groups of patients were significantly lower than those before intervention, and the levels of CD4⁺ and CD4⁺/CD8⁺ were significantly higher than those before intervention (p < 0.05); the level of CD8⁺ was not significantly different from that before intervention (p > 0.05). And NSE and SCC levels in the intervention group were significantly lower than those in the routine group, the levels of CD4⁺, CD4⁺/CD8⁺ were significantly higher than those in the routine group (p < 0.05).

CONCLUSION

NSE and SCC in serum can preliminarily evaluate the effect of radiotherapy in patients with lung cancer and have a certain predictive effect on prognosis.

Combined detection of estrogen and tumor markers is an important reference factor in the diagnosis and prognosis of lung cancer

The correlation between lung cancer tumor markers and sex differences in lung cancer remains a clinical problem that is worthy of further study. This study investigated the significance of the combined detection of 17β-estrogen (E2) and tumor markers in the diagnosis and prognosis of lung cancer. A total of 174 patients, including 117 patients with non-small-cell lung cancer (NSCLC) and 57 patients with benign pulmonary lesions (BPL), were enrolled. An enzyme-linked immunosorbent assay was used to detect the expression of E2, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and cytokeratin 19 fragment antigen 21-1 (CYFRA21-1) in patients with NSCLC and BPL to analyze the correlation between E2 and CEA, NSE or CYFRA21-1 expression, and its correlation with clinicopathological features and prognosis. The expression of tumor markers was then examined in different lung cancer cells (A549, H1795, H460, and SK-MES-1). The expression of tumor markers was detected by a real-time reverse transcription-polymerase chain reaction (RT-PCR) and Western blot analysis. The expressions of p-p44/42 mitogen-activated protein kinase (MAPK) and phospho-AKT (p-AKT) were detected by Western blot analysis. The expression levels of E2, CEA, NSE, and CYFRA21-1 in patients with NSCLC were significantly higher than those in patients with BPL ( P < .05); E2 was positively correlated with tumor markers ( P < .01). Patients with a high expression of E2 and tumor markers showed a poor prognosis ( P < .05). RT-quantitative PCR and Western blot analysis showed that the expression levels of CEA, NSE, CYFRA21-1, p-p44/42 MAPK, and p-AKT in the E2 group were higher than those in the other groups ( P < .05). These studies indicate that the interaction of E2 and tumor markers can significantly improve the role of tumor markers in the diagnosis and prognosis of lung cancer.