Microbes in lung cancer initiation, treatment, and outcome: Boon or bane?

Environmental pollutants exposure-derived extracellular vesicles: crucial players in respiratory disorders

BACKGROUND

Individual exposure to environmental pollutants, as one of the most influential drivers of respiratory disorders, has received considerable attention due to its preventability and controllability. Considering that the extracellular vesicle (EV) was an emerging intercellular communication medium, recent studies have highlighted the crucial role of environmental pollutants derived EVs (EPE-EVs) in respiratory disorders.

METHODS

PubMed and Web of Science were searched from January 2018 to December 2023 for publications with key words of environmental pollutants, respiratory disorders and EVs.

RESULTS

Environmental pollutants could disrupt airway intercellular communication by indirectly stimulating airway barrier cells to secrete endogenous EVs, or directly transmitting exogenous EVs, mainly by biological pollutants. Mechanistically, EPE-EVs transferred specific contents to modulate biological functions of recipient cells, to induce respiratory inflammation and impair tissue and immune function, which consequently contributed to the development of respiratory diseases, such as asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, pulmonary hypertension, lung cancer and infectious lung diseases. Clinically, EVs could emerged as promising biomarkers and biological agents for respiratory diseases attributed by their specificity, convenience, sensibility and stability.

CONCLUSIONS

Further studies of EPE-EVs are helpful to understand the aetiology and pathology of respiratory diseases, and facilitate the precision respiratory medicine in risk screening, early diagnosis, clinical management and biotherapy.

Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy

BACKGROUND

There is an urgent need to find a reliable and effective imaging method to evaluate the therapeutic efficacy of immunochemotherapy in advanced non-small cell lung cancer (NSCLC). This study aimed to investigate the capability of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) histogram analysis based on different region of interest (ROI) selection methods for predicting treatment response to chemoimmunotherapy in advanced NSCLC.

METHODS

Seventy-two stage III or IV NSCLC patients who received chemoimmunotherapy were enrolled in this study. IVIM and DKI were performed before treatment. The patients were classified as responders group and non-responders group according to the Response Evaluation Criteria in Solid Tumors 1.1. The histogram parameters of ADC, Dslow, Dfast, f, Dk and K were measured using whole tumor volume ROI and single slice ROI analysis methods. Variables with statistical differences would be included in stepwise logistic regression analysis to determine independent parameters, by which the combined model was also established. And the receiver operating characteristic curve (ROC) were used to evaluate the prediction performance of histogram parameters and the combined model.

RESULTS

ADC, Dslow, Dk histogram metrics were significantly lower in the responders group than in the non-responders group, while the histogram parameters of f were significantly higher in the responders group than in the non-responders group (all P < 0.05). The mean value of each parameter was better than or equivalent to other histogram metrics, where the mean value of f obtained from whole tumor and single slice both had the highest AUC (AUC = 0.886 and 0.812, respectively) compared to other single parameters. The combined model improved the diagnostic efficiency with an AUC of 0.968 (whole tumor) and 0.893 (single slice), respectively.

CONCLUSIONS

Whole tumor volume ROI demonstrated better diagnostic ability than single slice ROI analysis, which indicated whole tumor histogram analysis of IVIM and DKI hold greater potential than single slice ROI analysis to be a promising tool of predicting therapeutic response to chemoimmunotherapy in advanced NSCLC at initial state.

Construction of BaTiO3-TiO2 hollow sphere heterojunctions for enhanced microwave dynamic therapy in cancer treatment

Cancer is one of the primary health concerns among humans due to its high incidence rate and lack of effective treatment. Currently, medical techniques to achieve the precise elimination of local cancer lesions with negligible damage to normal tissues are still intensely desired. Herein, we synthesized BaTiO3-TiO2 hollow spheres (BTHSs) for use in microwave dynamic therapy (MWDT) for cancer. Under UV irradiation, BTHSs can mediate the production of multiple reactive oxygen species (ROS), mainly 1O2, which results in a rapid photocatalytic degradation rate (97%), 1.6-fold that of commercial P25. Importantly, the ROS production process can be triggered by microwaves to effectively execute MWDT for cancer. Under microwave irradiation, BTHSs exhibit a remarkable therapeutic effect and slight cytotoxicity. In terms of mechanism, the enhanced ROS production efficiency of BTHSs can be attributed to their unique hollow structure and the formation of a type-II heterojunction by the incorporation of BaTiO3. The hollow structure increases the availability of active sites and enhances light scattering, while the BaTiO3-TiO2 heterojunction enhances the photocatalytic activity of TiO2 through charge transfer and electron-hole separation. Overall, this study provides important insights into the design and optimization of sensitizers for MWDT applications.

Promising dawn in the management of pulmonary hypertension: The mystery veil of gut microbiota

The gut microbiota is a complex community of microorganisms inhabiting the intestinal tract, which plays a vital role in human health. It is intricately involved in the metabolism, and it also affects diverse physiological processes. The gut-lung axis is a bidirectional pathway between the gastrointestinal tract and the lungs. Recent research has shown that the gut microbiome plays a crucial role in immune response regulation in the lungs and the development of lung diseases. In this review, we present the interrelated factors concerning gut microbiota and the associated metabolites in pulmonary hypertension (PH), a lethal disease characterized by elevated pulmonary vascular pressure and resistance. Our research team explored the role of gut-microbiota-derived metabolites in cardiovascular diseases and established the correlation between metabolites such as putrescine, succinate, trimethylamine N-oxide (TMAO), and N, N, N-trimethyl-5-aminovaleric acid with the diseases. Furthermore, we found that specific metabolites, such as TMAO and betaine, have significant clinical value in PH, suggesting their potential as biomarkers in disease management. In detailing the interplay between the gut microbiota, their metabolites, and PH, we underscored the potential therapeutic approaches modulating this microbiota. Ultimately, we endeavor to alleviate the substantial socioeconomic burden associated with this disease. This review presents a unique exploratory analysis of the link between gut microbiota and PH, intending to propel further investigations in the gut-lung axis.

CEBPA facilitates LOXL2 and LOXL3 transcription to promote BCL-2 stability and thus enhances the growth and metastasis of lung carcinoma cells in vitro

Lung carcinoma (LC) is a complicated and highly heterogeneous disease with high morbidity and mortality. Both lysyl oxidase-like (LOXL) 2 and 3 act in cancer progression. This work endeavors to illustrate the influence of LOXL2/LOXL3 on LC progression and the underlying mechanisms. LOXL family genes and CCAAT enhancer binding protein A (CEBPA) were analyzed in the TCGA database for their expression patterns in LC patients and their correlations with the patient's prognosis. CEBPA, LOXL2, and LOXL3 expression levels were determined in LC cells. Gain- and loss-of-function assays were conducted, followed by assays for cell proliferation, epithelial-mesenchymal transition (EMT), apoptosis, invasion, and migration. The binding of CEBPA or B cell lymphoma protein (BCL)-2 to LOXL2/LOXL3 was verified. The ubiquitination level of BCL-2 and histone acetylation level of LOXL2/LOXL3 in LC cells were analyzed. Database analyses revealed that LC patients had high CEBPA, LOXL2, and LOXL3 expression, which were related to poor prognosis. LC cells also exhibited high CEBPA, LOXL2, and LOXL3 levels. LOXL2/LOXL3 knockdown subdued EMT, proliferation, migration, and invasion while enhancing the apoptosis of LC cells. LOXL2/LOXL3 could bind to CEBPA and BCL-2. LOXL2/LOXL3 knockdown upregulated BCL-2 ubiquitination level and diminished BCL-2 expression in LC cells. CEBPA recruited Tip60 to enhance histone acetylation and transcription of LOXL2/LOXL3 in LC cells. BCL-2 overexpression abolished the impacts of LOXL2/LOXL3 knockdown on LC cells. In conclusion, CEBPA boosts LOXL2 and LOXL3 transcription to facilitate BCL-2 stability by recruiting Tip60 and thus contributes to LC cell growth and metastasis.

Association of COVID-19 and Lung Cancer: Short-Term and Long-Term Interactions

Background: COVID-19 has been ravaging the globe for more than three years. Due to systemic immunosuppression of anti-tumor therapy, application of chemotherapy and adverse effects of surgery, the short- and long-term prognosis of cancer patients to COVID-19 are of significant concern. Method: This research included three parts of data. The first part of the data came from the public database that covered Veneto residents. The second part of the data included participants in Guangzhou. The third part of the data was used for MR analysis. We assessed the associations by logistic, linear or Cox regression when appropriate. Result: Lung cancer patients with COVID-19 had shorter progression-free survival (PFS) after COVID-19 (Model II: HR: 3.28, 95% CI: 1.6~6.72; Model III: HR: 3.39, 95% CI: 1.45~7.95), compared with lung cancer patients without COVID-19. Targeted therapy patients recovered from SARS-CoV-2 infection more quickly (Model I: β: -0.58, 95% CI: -0.75~-0.41; Model II: β: -0.59, 95% CI: -0.76~-0.41; Model III: β: -0.57; 95% CI: -0.75~-0.40). Conclusions: PFS in lung cancer patients is shortened by COVID-19. The outcome of COVID-19 in lung cancer patients was not significantly different from that of the healthy population. In lung cancer patients, targeted therapy patients had a better outcome of COVID-19, while chemotherapy patients had the worst.

An anoikis-related lncRNA signature is a useful tool for predicting the prognosis of patients with lung adenocarcinoma

Background

Anoikis-related long non-coding RNAs (ARLs) play a critical role in tumor metastasis and progression, suggesting that they may serve as risk markers for cancer. This study aimed to investigate the prognostic value of ARLs in patients with lung adenocarcinoma (LUAD).

Methods

Clinical data, RNA sequencing (RNA-seq) data, and mutation data from the LUAD project were obtained from The Cancer Genome Atlas (TCGA) database. The Molecular Signatures Database (MSigDB) and the GeneCard database were used to collect an anoikis-related gene (ARG) set. Pearson correlation analysis was performed to identify ARLs. LASSO and Cox regression were then used to establish a prognostic risk signature for ARLs. The median risk score served as the basis for categorizing patients into high and low-risk groups. Kaplan-Meier analysis was utilized to compare the prognosis between these two groups. The study also examined the associations between risk scores and prognosis, clinicopathological characteristics, immune status, tumor mutation burden (TMB), and chemotherapeutic agents. LncRNA expression was assessed using quantitative real-time PCR (qRT-PCR).

Results

A total of 480 RNA expression profiles, 501 ARGs, and 2698 ARLs were obtained from the database. A prognostic ARL signature for LUAD was established, consisting of 9 lncRNAs. Patients in the low-risk group exhibited significantly better prognosis compared to those in the high-risk group (P < 0.001). The 9 lncRNAs from the ARL signature were identified as independent prognostic factors (P < 0.001). The signature demonstrated high accuracy in predicting LUAD prognosis, with area under the curve values exceeding 0.7. The risk scores for ARLs showed strong negative correlations with stroma score (P = 5.9E-07, R = -0.23), immune score (P = 9.7E-09, R = -0.26), and microenvironment score (P = 8E-11, R = -0.29). Additionally, the low-risk group exhibited significantly higher TMB compared to the high-risk group (P = 4.6E-05). High-risk status was significantly associated with lower half-maximal inhibitory concentrations for most chemotherapeutic drugs.

Conclusion

This newly constructed signature based on nine ARLs is a useful instrument for the risk stratification of LUAD patients. The signature has potential clinical significance for predicting the prognosis of LUAD patients and guiding personalized immunotherapy.

MicroRNA-139-5p suppresses non-small cell lung cancer progression by targeting ATAD2

MiR-139-5p is a suppressor in multiple types of cancer. However, whether miR-139-5p affects NSCLC is unknown. In this study, miR-139-5p expression in clinical samples was examined by real-time PCR and in situ hybridization (ISH). MiR-139-5p mimic was transfected to monitor NSCLC cell behaviors. Potential target was predicated using bioinformatics database. Next, whether miR-139-5p impacted cell behaviors via regulation of its predicted target gene were further evaluated. The result revealed that miR-139-5p was lower in NSCLC samples/cells. MiR-139-5p restrained A549 cell proliferation, accelerated apoptosis, and inhibited the β-catenin signaling. ATAD2 was a predicted target of miR-139-5p, and it was highly expressed in NSCLC tissues. ATAD2 overexpression abolished the miR-139-5p's anti-tumor effect on cell proliferation and apoptosis. TWS119 (a β-catenin signaling activator) partially reversed miR-139-5p overexpression-induced suppression of cell proliferation and promotion of cell apoptosis. In tumor xenografts, miR-139-5p restrained tumor growth. MiR-139-5p was a tumor suppressor in NSCLC by regulating the oncogene ATAD2 and β-catenin signaling. Our study provides a promising target for cancer treatment.

Establishment of a risk classifier to predict the in-hospital death risk of nosocomial fungal infections in cancer patients

BACKGROUND

Patients with malignancy are at a higher risk of developing nosocomial infections. However, limited studies investigated the clinical features and prognostic factors of nosocomial infections due to fungi in cancer patients. Herein, this study aims to investigate the clinical characteristics of in-hospital fungal infections and develop a nomogram to predict the risk of in-hospital death during fungal infection of hospitalized cancer patients.

METHODS

This retrospective observational study enrolled cancer patients who experienced in-hospital fungal infections between September 2013 and September 2021. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of in-hospital mortality. Variables demonstrating significant statistical differences in the multivariate analysis were utilized to construct a nomogram for personalized prediction of in-hospital death risk associated with nosocomial fungal infections. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis.

RESULTS

A total of 216 participants were included in the study, of which 57 experienced in-hospital death. C.albicans was identified as the most prevalent fungal species (68.0%). Respiratory infection accounted for the highest proportion of fungal infections (59.0%), followed by intra-abdominal infection (8.8%). The multivariate regression analysis revealed that Eastern Cooperative Oncology Group Performance Status (ECOG-PS) 3-4 (odds ratio [OR] = 6.08, 95% confidence interval [CI]: 2.04-18.12), pulmonary metastases (OR = 2.76, 95%CI: 1.11-6.85), thrombocytopenia (OR = 2.58, 95%CI: 1.21-5.47), hypoalbuminemia (OR = 2.44, 95%CI: 1.22-4.90), and mechanical ventilation (OR = 2.64, 95%CI: 1.03-6.73) were independent risk factors of in-hospital death. A nomogram based on the identified risk factors was developed to predict the individual probability of in-hospital mortality. The nomogram demonstrated satisfactory performance in terms of classification ability (area under the curve [AUC]: 0.759), calibration ability, and net clinical benefit.

CONCLUSIONS

Fungi-related nosocomial infections are prevalent among cancer patients and are associated with poor prognosis. The constructed nomogram provides an invaluable tool for oncologists, enabling them to make timely and informed clinical decisions that offer substantial net clinical benefit to patients.

Carbon Nanotube Field-Effect Transistor Biosensor with an Enlarged Gate Area for Ultra-Sensitive Detection of a Lung Cancer Biomarker

Carcinoembryonic antigen (CEA) is a recognized biomarker for lung cancer and can be used for early detection. However, the clinical value of CEA is not fully realized due to the rigorous requirement for high-sensitivity and wide-range detection methods. Field-effect transistor (FET) biosensors, as one of the potentially powerful platforms, may detect CEA with a significantly higher sensitivity than conventional clinical testing equipment, while their sensitivity and detection range for CEA are far below the requirement for early detection. Here, we construct a floating gate FET biosensor to detect CEA based on a semiconducting carbon nanotube (CNT) film combined with an undulating yttrium oxide (Y₂O₃) dielectric layer as the biosensing interface. Utilizing an undulating biosensing interface, the proposed device showed a wider detection range and optimized sensitivity and detection limit, which benefited from an increase of probe-binding sites on the sensing interface and an increase of electric double-layer capacitance, respectively. The outcomes of analytical studies confirm that the undulating Y₂O₃ provided the desired biosensing surface for probe immobilization and performance optimization of a CNT-FET biosensor toward CEA including a wide detection range from 1 fg/mL to 1 ng/mL, good linearity, and high sensitivity of 72 ag/mL. More crucially, the sensing platform can function normally in the complicated environment of fetal bovine serum, indicating its great promise for early lung cancer screening.

Exploration of lung mycobiome in the patients with non-small-cell lung cancer

As the Human Microbiome Project (HMP) progresses, the relationship between microbes and human health has been receiving increasing attention. A growing number of reports support the correlation between cancer and microbes. However, most studies have focused on bacteria, rather than fungal communities. In this study, we studied the alteration in lung mycobiome in patients with non-small-cell lung cancer (NSCLC) using metagenomic sequencing and qPCR. The higher fungal diversity and more complex network were observed in the patients with NSCLC. In addition, Alternaria arborescens was found as the most relevant fungus to NSCLC, and the enrichment of it in cancerous tissue was also detected. This study proposes that the changes in fungal communities may be closely related to lung cancer, and provides insights into further exploration the relationship between lung cancer and fungi.

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

Lung Cancer Stage Prediction Using Multi-Omics Data

Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.