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Background Lung cancer remains the leading cause of cancer-related death worldwide. The human PINK1 gene (PTEN induced kinase 1, Park6), an important gene for Parkinson's disease, was found to be associated with tumor development although the molecular mechanisms underlying this relationship remain largely unknown. Objective To analyze the clinical value and molecular mechanism of PINK1 in non-small cell lung cancer (NSCLC). Materials and methods Western blot, qRT-PCR and Immunohistochemistry were employed to determine the levels of PINK1 in 87 paired NSCLC tissues, Oncomine and TCGA databases were also used for the evaluation of expression and prognosis of PINK1. The mitophagy, proliferation, migration, invasion, and apoptosis abilities of A549 and H1975 cells were detected, and the autophagy-related proteins in the cells were also determined. Results Immunohistochemical staining revealed higher PINK1 expression in tumor tissues, which was strongly linked to the tumor-node-metastasis classification. Survival analysis of 1085 NSCLC patients also revealed that low PINK1 expression levels were associated with significantly longer overall survival. Univariate and multivariate analyses indicated that PINK1 expression was an independent predictor of overall survival among patients with NSCLC. We also evaluated the influence of PINK1 deficiency in NSCLC cell lines (A549 and H1975), which revealed significant suppression of migration capability and cell viability, as well as a significantly elevated apoptosis ratio. In cells with stable interference of PINK1 expression, dysfunctional mitochondria accumulated while autophagy was inhibited, which indicated that cell activity suppression was mediated by the accumulation of dysfunctional mitochondria. The suppression of migration and autophagy was reversed in cells that overexpressed PINK1. Conclusion Our results suggest that PINK1 may be a potential therapeutic target and prognostic biomarker in NSCLC.

Background: Almost all patients with lung adenocarcinoma (LUAD) develop resistance to EGFR-TKIs, which limit the long-term clinical application of these agents. Accumulating evidence shows one of the main reasons for resistance to EGFR-TKIs is induction of autophagy in tumor cells. Our previous study found that circumsporozoite protein (CSP) in Plasmodium can suppress autophagy in host hepatocytes. However, it is unknown whether CSP-mediated inhibition of autophagy could improve the anti-tumor effect of EGFR-TKIs. Methods: We constructed A549 and H1975 cell lines with stable overexpression of CSP (OE-CSP cells). CCK-8, LDH, flow cytometry, and colony analysis were performed to observe the effect of CSP overexpression on cell viability, the apoptosis rate, and stemness. The sensitizing effect of CSP on gefitinib was evaluated in vivo using a subcutaneous tumor model in nude mice and immunohistochemical assay. The role of CSP in regulation of autophagy was investigated by laser confocal microscopy assay and Western blotting. A transcriptome sequencing assay and real-time polymerase chain reaction were used to determine the levels of mRNA for autophagy-related proteins. Cyclohexane, MG132, TAK-243, and immunoprecipitation assays were used to detect and confirm proteasomal degradation of LC3B. Results: OE-CSP A549 and H1975 cells were more sensitive to gefitinib, demonstrating significant amounts of apoptosis and decreased viability. In the OE-CSP group, autophagy was significantly inhibited, and there was a decrease in LC3B protein after exposure to gefitinib. Cell viability and stemness were recovered when OE-CSP cells were exposed to rapamycin. In nude mice with xenografts of LUAD cells, inhibition of autophagy by CSP resulted in suppression of cell growth and more marked apoptosis during exposure to gefitinib. CSP promoted ubiquitin-proteasome degradation of LC3B, leading to inhibition of autophagy in LUAD cells after treatment with gefitinib . When LUAD cells were treated with the ubiquitin-specific inhibitor TAK-243, cell viability, apoptosis, and growth were comparable between the OE-CSP group and a control group both in vivo and in vitro . Conclusions: CSP can inhibit gefitinib-induced autophagy via proteasomal degradation of LC3B, which suggests that CSP could be used as an autophagy inhibitor to sensitize EGFR-TKIs.

Epidemiological investigations have shown that patients with Parkinson’s disease (PD) have a lower probability of developing lung cancer. Subsequent research revealed that PD and lung cancer share specific genetic alterations. Therefore, the utilisation of PD biomarkers and therapeutic targets may improve lung adenocarcinoma (LUAD) diagnosis and treatment. We aimed to identify a gene-based signature from 25 Parkinson family genes for LUAD prognosis and treatment choice. We analysed Parkinson family gene expression and protein levels in LUAD, utilising multiple databases. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a prognostic model based on the TCGA-LUAD cohort. We validated the model in external GEO cohorts. Immune cell infiltration was compared between risk groups, and GEO data was used to explore the model’s predictive ability for LUAD treatment response. Nearly all Parkinson family genes exhibited significant differential expression between LUAD and normal tissues. LASSO regression confirmed that our seven Parkinson family gene-based signature had excellent prognostic performance for LUAD, as validated in three GEO cohorts. The high-risk group was clearly associated with low tumour immune cell infiltration, suggesting that immunotherapy may not be an optimal treatment choice. This is the first Parkinson family gene-based model for the prediction of LUAD prognosis and treatment outcome. The association of these genes with poor prognosis and low immune infiltration requires further investigation.

Purpose Clinical evidence of metastasis with ground-glass nodules (GGNs) has been reported, including pulmonary metastasis and distant metastasis. However, the clonal relationships of multiple GGNs at the genetic level remain unclear. Experimental design Sixty tissue specimens were obtained from 19 patients with multiple GGN lung cancer who underwent surgery in 2019. Whole exome sequencing (WES) was performed on tissue samples, and genomic profiling and clone evolution analysis were conducted to investigate the genetic characteristics and clonality of multiple GGNs. Results A total of 15,435 nonsynonymous mutations were identified by WES, and GGNs with shared nonsynonymous mutations were observed in seven patients. Copy number variant (CNV) analysis showed that GGNs in ten patients had at least one shared arm-level CNV. Mutational spectrum analysis showed that GGNs in three patients had similar six substitution profiles and GGNs in fou patients had similar 96 substitution profiles. According to the clone evolution analysis, we found that GGNs in five patients had shared clonal driver gene mutations. Taken together, we identified that 5 patients may have multiple primary GGNs without any similar genetic features, 2 patients may have intrapulmonary metastatic GGNs with ≥ 3 similar genetic features, and the other 12 patients cannot be determined due to insufficient evidences in our cohort. Conclusions Our findings suggest that the intrapulmonary metastasis exist in multiple GGNs, but the number of GGNs was not associated with the probability of metastasis. Application of genomic profiling may prove to be important to precise management of patients with multiple GGNs.

Addressing the high false‐positive rate of conventional low‐dose computed tomography (LDCT) for lung cancer diagnosis, the efficacy of incorporating blood‐based noninvasive testing for assisting practicing clinician's decision making in diagnosis of pulmonary nodules (PNs) is investigated. In this prospective observative study, next generation sequencing‐ (NGS‐) based cell‐free DNA (cfDNA) mutation profiling, NGS‐based cfDNA methylation profiling, and blood‐based protein cancer biomarker testing are performed for patients with PNs, who are diagnosed as high‐risk patients through LDCT and subsequently undergo surgical resections, with tissue sections pathologically examined and classified. Using pathological classification as the gold standard, statistical and machine learning methods are used to select molecular markers associated with tissue's malignant classification based on a 98‐patient discovery cohort (28 benign and 70 malignant), and to construct an integrative multianalytical model for tissue malignancy prediction. Predictive models based on individual testing platforms have shown varying levels of performance, while their final integrative model produces an area under the receiver operating characteristic curve (AUC) of 0.85. The model's performance is further confirmed on a 29‐patient independent validation cohort (14 benign and 15 malignant, with power > 0.90), reproducing AUC of 0.86, which translates to an overall sensitivity of 80% and specificity of 85.7%., An integrative multianalytical machine learning model based on patient clinical features, cfDNA mutation, cfDNA methylation, and protein cancer biomarkers for noninvasive cancer diagnosis of lung pulmonary nodules has achieved an area under the receiver operating characteristic curve of 0.85 on a 98‐patient discovery cohort, and 0.86 on a 29‐patient independent validation cohort, translating to 80% sensitivity and 85.7% specificity.