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BACKGROUND: The incidence of multiple primary cancers (MPC), especially involving primary lung cancer (PLC) and primary hematologic malignancies (PHM), is rising. This study aims to analyze clinicopathological features, gene abnormalities, and prognostic outcomes in individuals diagnosed with PLC-PHM MPC. METHODS: A retrospective analysis included 89 patients diagnosed with PLC-PHM MPC at the Respiratory or Hematology Departments of Ruijin Hospital from 2003 to 2022 (a total of 842,047 people). Next-generation sequencing (NGS) assessed lung cancer specimens, while Polymerase Chain Reaction (PCR) and NGS were used for hematologic malignancy specimens. Statistical analysis involved survival analysis and Cox regression. RESULTS: PLC-PHM MPC incidence surged from 1.67 per year (2011-2013) to 16.3 per year (2020-2022). The primary demographic for PLC-PHM MPC consists predominantly of elderly (average age 66 years) males (59.6%), with a high prevalence of metachronous MPC (89.9%). The prevailing histological types were lung adenocarcinoma (70.8%) in lung cancer (LC) and mature B-cell lymphomas (50.6%) in hematologic malignancies (HM). Notably, in a molecular testing cohort of 38 LC patients, 84.2% of lung cancer cases exhibited driver mutations, in which EGFR mutations frequence prevalent was 74.2%. In total group of 85 cases achieved a median overall survival (mOS) of 46.2 months, with a 5-year survival rate of 37.9% and advanced LC patients with LC gene mutations achieved a mOS was 52.6 months, with a 5-year OS rate of 30.6%. The median progression-free survival (PFS) following first-line treatment of 11 advanced patients with lung cancer-associated driver gene mutations is 26.6 months. Multivariate Cox regression revealed a favorable OS associated with surgery for LC, favorable PS score, adenocarcinoma pathology of LC, and the presence of genetic abnormalities associated with HM. CONCLUSION: PLC-PHM MPC incidence is rising, characterized by a significant proportion of lung adenocarcinoma and a high prevalence of positive driver genes, especially in EGFR. Despite suffering from two primary tumors, the PLC-PHM MPC patients had superior data of both PFS and OS, suggesting an inherently intricate background of genetic abnormalities between the two kinds of tumors.
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PURPOSE: Small cell lung cancer (SCLC) is an aggressive and rapidly progressive malignant tumor characterized by a poor prognosis. Chemotherapy remains the primary treatment in clinical practice; however, reliable biomarkers for predicting chemotherapy outcomes are scarce. METHODS: In this study, 78 SCLC patients were stratified into "good" or "poor" prognosis cohorts based on their overall survival (OS) following surgery and chemotherapeutic treatment. Next-generation sequencing was employed to analyze the mutation status of 315 tumorigenesis-associated genes in tumor tissues obtained from the patients. The random forest (RF) method, validated by the support vector machine (SVM), was utilized to identify single nucleotide mutations (SNVs) with predictive power. To verify the prognosis effect of SNVs, samples from the cbioportal database were utilized. RESULTS: The SVM and RF methods confirmed that 20 genes positively contributed to prognosis prediction, displaying an area under the validation curve with a value of 0.89. In the corresponding OS analysis, all patients with SDH, STAT3 and PDCD1LG2 mutations were in the poor prognosis cohort (15/15, 100%). Analysis of public databases further confirms that SDH mutations are significantly associated with worse OS. CONCLUSION: Our results provide a potential stratification of chemotherapy prognosis in SCLC patients, and have certain guiding significance for subsequent precise targeted therapy.
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The knowledge on the mechanisms of DNA interfacing with nanoparticles holds great potential for the design, assembly and usage of DNA in biological applications. A wave of understanding and exploitation of the mechanisms in DNA-nanoparticles interfacial phenomenon has raised. Although some previous reviews have been reported, systematic and detailed reviews are rare. To achieve a better understanding of the mechanisms in the interaction between DNA and nanoparticles, here, we summarized the recent progresses on the fundamental principles regarding the DNA-nanoparticle interactions and their applications in biosensing. Special focus was put on inorganic nanoparticles such as metal nanoparticles, carbon-based materials, metal oxides and quantum dots. For each material, the surface properties, the interfacing mechanisms, and the kinetics and spatial control of DNA adsorption were summarized and discussed. We also highlighted some of the recent technologies based on DNA-NPs interactions for biomolecules detection. Finally, the challenges and future directions were discussed and proposed. This review provides a systematic understanding of the mechanisms in the interaction of DNA-nanoparticles, which, in turn, can inspire new insights for designing biosensors with improved properties.
