Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD)

Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma

PURPOSE

Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients.

METHODS

In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA.

RESULTS

Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001).

CONCLUSION

In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.

Plasma tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 as novel diagnostic biomarkers for lung adenocarcinoma

Objective

TRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs) are recognized as novel and potential types of non-coding RNAs (ncRNAs), and several tRF/tiRNA signatures are closely associated with tumor diagnosis. This study aimed to analyze the expression profiles of plasma tRFs/tiRNAs and to clarify their diagnostic value in lung adenocarcinoma (LUAD).

Methods

The differential expression profiles of plasma tRFs/tiRNAs in patients with four patients with early LUAD, four patients with advanced LUAD, and four healthy controls were analyzed using high-throughput sequencing technology. Then, plasma tRFs/tiRNAs were validated by quantitative real-time polymerase chain reaction (qRT-PCR), and their diagnostic efficiency was appraised by receiver operating characteristic curve analysis. The correlation of candidate plasma tRFs/tiRNAs with clinicopathological features was also analyzed. Finally, bioinformatics analysis was performed to explore and identify the potential biological pathways induced by tRFs/tiRNAs.

Results

The sequencing results revealed that tRFs/tiRNAs from plasma samples in patients with LUAD were differently expressed, supporting the necessity of exploring their potential as biomarkers. The validation results of qRT-PCR demonstrated that the expression level of tRF-1:29-Pro-AGG-1-M6 was downregulated in LUAD, while that of tRF-55:76-Tyr-GTA-1-M2 was upregulated, which was consistent with the sequencing data. The areas under the receiver operating characteristic curve of tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 were 0.882 and 0.896, respectively, which have significant values in the diagnosis of LUAD. The expressions of tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 in LUAD were obviously correlated with various clinicopathological features such as tumor–node–metastasis stage, node stage, and the expression levels of carcinoembryonic antigen. In addition, their expression was significantly altered from before to after tumor resection in LUAD patients. The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses further indicated that tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 are widely distributed and apparently enriched in several tumor-related signaling pathways.

Conclusions

Plasma tRF-1:29-Pro-AGG-1-M6 and tRF-55:76-Tyr-GTA-1-M2 may be promising components in the development of highly sensitive and non-invasive biomarkers for LUAD diagnosis.

Corrected Allele Frequency of BRCA1/2 Mutations Is an Independent Prognostic Factor for Treatment Response to PARP-Inhibitors in Ovarian Cancer Patients

PARP inhibitors (PARPi) have increased treatment options in ovarian cancer, particularly in patients with BRCA1/2 mutations, although there are still marked differences in the duration of patients’ response to this targeted therapy. BRCA testing is routinely performed in tumor tissue of ovarian cancer patients. The resulting molecular pathological findings include the genetic nomenclature of the mutation, the frequency of the mutated allele (variant allele frequency, VAF), and the tumor cell content. VAF measures the percentage of mutated alleles from the total alleles in the cells of the examined tissue. The aim of this study was to investigate the significance of VAF on the therapeutic response to PARPis in ovarian cancer patients. Epithelial ovarian cancer patients harboring BRCA1/2 tumor mutations, who underwent germline testing and received PARPi therapy at the Medical University of Vienna (n = 41) were included in the study. Corrected VAF (cVAF) was calculated based on VAF, tumor cell content, and germline mutation. Patients were divided into two groups based on their cVAF. Median PFS under PARPi in patients with low cVAF was 13.0 months (IQR [10.3-not reached]) and was not reached in the high cVAF group. High cVAF was significantly associated with longer PFS in the multivariate analysis (HR = 0.07; 95% CI [0.01–0.63]; p = 0.017). In conclusion, high cVAF was associated with a significantly better response to PARPi in this study population.