Novel application of metformin combined with targeted drugs on anticancer treatment.

The success of targeted drug therapies for treating cancer patients has attracted broad attention both in the academic community and social society. However, rapidly developed acquired resistance is becoming a newly recognized major challenge to the continuing efficiency of these therapies. Metformin is a well-known natural compound with low toxicity derived from the plant French lilac. Our previous work has highlighted research progress of the combination of clinically applied chemotherapies and metformin by different mechanisms. We have also launched a study to combine metformin with the small molecule targeted drug gefitinib to treat bladder cancer using intravesical administration. Thus, in this minireview, we summarize recent achievements combining metformin with various targeted therapies. This work directs the potential clinical future by selecting available cancer patients and providing precise medicine by the combination of metformin and targeted drugs to overcome resistance and enhance therapeutic efficacies.

The prognostic analysis and a machine-learning based disease-specific survival state model in pulmonary large-cell neuroendocrine carcinomas.

Background: Pulmonary large-cell neuroendocrine carcinoma (PLCNEC) is a rare and highly malignant lung cancer. Due to the paucity of data from clinical studies, its clinical characteristics and treatment remain controversial. The present study explored factors influencing the prognosis and survival outcomes of patients with PLCNEC and developed a dependable prognostic model using machine learning. Methods: The clinical data of PLCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020. A total of 2,897 PLCNEC patients were enrolled and univariate and multivariate Cox regression analyses were performed to explore independent prognostic factors for disease-specific survival (DSS). Ten machine learning algorithms were utilized to predict the 2-year survival. The clinicopathological data collected from The First Affiliated Hospital of Sun Yat-sen University between 2010 and 2022 were used to test the trained machine. Results: Sex [hazard ratio (HR) 1.168, 95% confidence interval (CI): 1.063-1.284], age (HR 1.262, 95% CI: 1.144-1.391), surgery (HR 0.481, 95% CI: 0.413-0.559), chemotherapy (HR 0.450, 95% CI: 0.404-0.501), bone metastasis (HR 1.284, 95% CI: 1.124-1.466), brain metastasis (HR 1.167, 95% CI: 1.023-1.331), liver metastasis (HR 1.223, 95% CI: 1.069-1.399), American Joint Committee on Cancer-Node (AJCC-N), and tumor stage were independent prognostic factors. The gradient boosting decision tree (GBDT) performed better than other models, with an F1-score of 0.791 and an area under the curve of 0.831. Conclusions: Male, age ≥65 years, distant metastasis to the bone, liver, and brain are associated with a worse prognosis in PLCNEC patients, while surgery and chemotherapy are associated with improved prognosis. GBDT showed promising performance in predicting 2-year survival, which can serve as a valuable reference for clinical diagnosis and treatment of PLCNEC.

Relationship between hemoglobin and lung cancer: evidence from Mendelian randomization and National Health and Nutrition Examination Survey.

BACKGROUND: Lung cancer is the primary cause of cancer-related mortality worldwide. Hemoglobin (Hb) represents the most widely utilized test parameter in clinical settings. However, few articles have examined the causal relationship between Hb concentration and lung cancer incidence. METHODS: Mendelian randomization (MR) was first conducted to investigate the potential causality between Hb and lung cancer. Sensitivity analyses were applied to validate the reliability of MR results. Then, the National Health and Nutrition Examination Survey (NHANES) database was used to verify the effect of Hb on the prognosis of lung cancer. RESULTS: The MR analysis demonstrated that Hb was casually associated with the decreased risk of lung cancer in the European population (ORIVW 0.84, 95% CI 0.75-0.95, p = 0.006; ORWeighted-median 0.78, 95% CI 0.65-0.94, p = 0.008; ORMR-Egger 0.82, 95% CI 0.64-1.04, p = 0.11). The results from the NHANES database showed that a high value of Hb was associated with better outcomes for patients with lung cancer (HR 0.45, 95% CI 0.26-0.79, p = 1.6E-03). CONCLUSIONS: Our study provides further evidence for the relationship between Hb levels and lung cancer, highlighting the potential significance of Hb as a biomarker for predicting the risk and prognosis of lung cancer.

FHL2 promotes the aggressiveness of lung adenocarcinoma by inhibiting autophagy via activation of the PI3K/AKT/mTOR pathway.

BACKGROUND: Increasing evidence indicates that four and a half LIM domains 2 (FHL2) plays a crucial role in the progression of various cancers. However, the biological functions and molecular mechanism of FHL2 in lung adenocarcinoma (LUAD) remain unclear. METHODS: We evaluated the prognostic value of FHL2 in LUAD using public datasets and further confirmed its prognostic value with our clinical data. The biological functions of FHL2 in LUAD were evaluated by in vitro and in vivo experiments. Pathway analysis and rescue experiments were subsequently performed to explore the molecular mechanism by which FHL2 promoted the progression of LUAD. RESULTS: FHL2 was upregulated in LUAD tissues compared to adjacent normal lung tissues, and FHL2 overexpression was correlated with unfavorable outcomes in patients with LUAD. FHL2 knockdown significantly suppressed the proliferation, migration and invasion of LUAD cells, while FHL2 overexpression had the opposite effect. Mechanistically, FHL2 upregulated the PI3K/AKT/mTOR pathway and subsequently inhibited autophagy in LUAD cells. The effects FHL2 on the proliferation, migration and invasion of LUAD cells are dependent on the inhibition of autophagy, as of induction autophagy attenuated the aggressive phenotype induced by FHL2 overexpression. CONCLUSIONS: FHL2 promotes the progression of LUAD by activating the PI3K/AKT/mTOR pathway and subsequently inhibiting autophagy, which can be exploited as a potential therapeutic target for LUAD.

A Risk-Scoring Model for Severe Checkpoint Inhibitor-Related Pneumonitis: A Case-Control Study.

BACKGROUND AND OBJECTIVE: Checkpoint inhibitor-related pneumonitis (CIP) is one of the most common serious and fatal adverse events associated with immune checkpoint inhibitors (ICIs). The study sought to identify risk factors of all-grade and severe CIP and to construct a risk-scoring model specifically for severe CIP. METHODS: This observational, retrospective case-control study involved 666 lung cancer patients who received ICIs between April 2018 and March 2021. The study analyzed patient demographic, preexisting lung diseases, and the characteristics and treatment of lung cancer to determine the risk factors for all-grade and severe CIP. A risk score for severe CIP was developed and validated in a separate patient cohort of 187 patients. RESULTS: Among 666 patients, 95 patients were afflicted with CIP, of which 37 were severe cases. Multivariate analysis revealed age ≥ 65 years, current smoking, chronic obstructive pulmonary disease, squamous cell carcinoma, prior thoracic radiotherapy, and extra-thoracic radiotherapy during ICI were independently associated with CIP events. Five factors, emphysema (odds ratio [OR] 2.87), interstitial lung disease (OR 4.76), pleural effusion (OR 3.00), history of radiotherapy during ICI (OR 4.30), and single-agent immunotherapy (OR 2.44) were independently associated with severe CIP and were incorporated into a risk-score model (score ranging 0-17). The area under the model receiver operating characteristic curve for the model was 0.769 in the development cohort and 0.749 in the validation cohort. CONCLUSIONS: The simple risk-scoring model may predict severe CIP in lung cancer patients receiving ICIs. For patients with high scores, clinicians should use ICIs with caution or strengthen the monitoring of these patients.

Overexpression of BIT33_RS14560 Enhances the Biofilm Formation and Virulence of Acinetobacter baumannii.

Acinetobacter baumannii, a strictly aerobic, non-lactose fermented Gram-negative bacteria, is one of the important pathogens of nosocomial infection. Major facilitator superfamily (MFS) transporter membrane proteins are a class of proteins that widely exists in microbial genomes and have been revealed to be related to biofilm formation in a variety of microorganisms. However, as one of the MFS transporter membrane proteins, little is known about the role of BIT33_RS14560 in A. baumannii. To explore the effects of BIT33_RS14560 on biofilm formation of A. baumannii, the biofilm formation abilities of 62 isolates were firstly investigated and compared with their transcript levels of BIT33_RS14560. Then, this specific gene was over-expressed in a standard A. baumannii strain (ATCC 19606) and two isolates of extensively drug-resistant A. baumannii (XDR-Ab). Bacterial virulence was observed using a Galleria mellonella infection model. High-throughput transcriptome sequencing (RNA seq) was performed on ATCC 19606 over-expressed strain and its corresponding empty plasmid control strain. Spearman's correlation analysis indicated a significant negative correlation (R = -0.569, p = 0.000) between the △CT levels of BIT33_RS1456 and biofilm grading of A. baumannii isolates. The amount of A. baumannii biofilm was relatively high within 12-48 h. Regardless of standard or clinical strains; the biofilm biomass in the BIT33_RS14560 overexpression group was significantly higher than that in the control group ( p < 0.0001). Kaplan-Meier survival curve analysis showed that the mortality of G. mellonella was significantly higher when infected with the BIT33_RS14560 overexpression strain (χ2 = 8.462, p = 0.004). RNA-Seq showed that the mRNA expression levels of three genes annotated as OprD family outer membrane porin, glycosyltransferase family 39 protein, and glycosyltransferase family 2 protein, which were related to bacterial adhesion, biofilm formation, and virulence, were significantly upregulated when BIT33_RS14560 was over-expressed. Our findings provided new insights in identifying potential drug targets for the inhibition of biofilm formation. We also developed a practical method to construct an over-expressed vector that can stably replicate in XDR-Ab isolates.

Different Roles of the Insulin-like Growth Factor (IGF) Axis in Non-small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) remains one of the deadliest malignant diseases, with high incidence and mortality worldwide. The insulin-like growth factor (IGF) axis, consisting of IGF-1, IGF-2, related receptors (IGF-1R, -2R), and high-affinity binding proteins (IGFBP 1-6), is associated with promoting fetal development, tissue growth, and metabolism. Emerging studies have also identified the role of the IGF axis in NSCLC, including cancer growth, invasion, and metastasis. Upregulation of IGE-1 and IGF-2, overexpression of IGF-1R, and dysregulation of downstream signaling molecules involved in the PI-3K/Akt and MAPK pathways jointly increase the risk of cancer growth and migration in NSCLC. At the genetic level, some noncoding RNAs could influence the proliferation and differentiation of tumor cells through the IGF signaling pathway. The resistance to some promising drugs might be partially attributed to the IGF axis. Therapeutic strategies targeting the IGF axis have been evaluated, and some have shown promising efficacy. In this review, we summarize the biological roles of the IGF axis in NSCLC, including the expression and prognostic significance of the related components, noncoding RNA regulation, involvement in drug resistance, and therapeutic application. This review offers a comprehensive understanding of NSCLC and provides insightful ideas for future research.