Salivary Biomarkers in Lung Cancer

The implication of microbiome in lungs cancer: mechanisms and strategies of cancer growth, diagnosis and therapy

Available evidence illustrates that microbiome is a promising target for the study of growth, diagnosis and therapy of various types of cancer. Lung cancer is a leading cause of cancer death worldwide. The relationship of microbiota and their products with diverse pathologic conditions has been getting large attention. The novel research suggests that the microbiome plays an important role in the growth and progression of lung cancer. The lung microbiome plays a crucial role in maintaining mucosal immunity and synchronizing the stability between tolerance and inflammation. Alteration in microbiome is identified as a critical player in the progression of lung cancer and negatively impacts the patient. Studies suggest that healthy microbiome is essential for effective therapy. Various clinical trials and research are focusing on enhancing the treatment efficacy by altering the microbiome. The regulation of microbiota will provide innovative and promising treatment strategies for the maintenance of host homeostasis and the prevention of lung cancer in lung cancer patients. In the current review article, we presented the latest progress about the involvement of microbiome in the growth and diagnosis of lung cancer. Furthermore, we also assessed the therapeutic status of the microbiome for the management and treatment of lung cancer.

ASSOCIATION OF ORAL MICROBIOTA AND PERIODONTAL DISEASE WITH LUNG CANCER: A SYSTEMATIC REVIEW AND META-ANALYSIS

OBJECTIVES

Evidence of oral microbiota perturbations has been accumulated for lung cancers. This review focused on the oral microbiota alterations in population suffering from lung cancer. In addition, we also discussed conflicting data about the association between oral microbiota dysbiosis and risk of lung cancer.

METHODS

A systematic search was conducted in Medline, Embase, PubMed, and Cochrane Library databases. The studies evaluated diversity and abundance of oral microbes in healthy and lung cancer individuals as well as association of periodontal disease and pathogens with lung cancer. Of 3559 studies, 28 included studies were performed in qualitative analysis, and 25 studies were used in meta-analyses for quantitative assessment. Heterogeneity was analyzed by using I² and chi-squared Q test statistics. Statistical analyses were performed by using the RevMan 5.4 software.

RESULTS

Compared with the control, lung cancer patients had lower alpha diversity (Shannon: SMD = -0.54; 95% CI, -0.90 to -0.19; P < .01, I2 = 71%). In nested case-control studies, individuals with decreased alpha diversity tended to have an increased risk of lung cancer (observed species: HR = 0.90; 95% CI, 0.85-0.96; P < .01, I2 = 0%; Shannon: HR = 0.89; 95% CI, 0.83-0.95; P < .01, I2 = 0%). Overall, no strong evidence of association of relative abundance with specific oral microbes with lung cancers was found because of inconsistent data. No associations were found between periodontal pathogens and lung cancer risk (red complex: HR = 1.12, 95% CI: 0.42-3.02, P = .82, I2 = 62%; orange complex: HR =1.77, 95% CI: 0.78-3.98, P = .17, I2 = 36%), expect for Fusobacterium nucleatum (HR = 2.27, 95% CI: 1.13-4.58, P = .02, I2 = 0%). The positive association of periodontal disease with lung cancer risk was found (HR = 1.58, 95% CI: 1.25-2.00, P < .001, I2= 0%) with increase of periodontal diseases severity (HR = 2.39, 95% CI: 1.57-3.66, P < .001, I2 = 0%). However, such association was not found in never-smoker participants (HR = 1.00, 95% CI: 0.76-1.31, P = .37, I2= 7%).

CONCLUSIONS

Lower alpha diversity of oral microbiome may be associated with a greater risk of lung cancer and might serve as a predictive signal of lung cancer risk. There was no strong evidence of relative abundance of oral microbial taxa and periodontal pathogens in lung cancer patients. Fusobacterium nucleatum might be a potential microbial candidate of biomarkers in lung cancer. Periodontal disease may be positively associated with lung cancer risk by confounding of smoking, but not an independent risk factor.

Importance of oral health in mental health disorders: An updated review

Background

Mental disorders are indeed an expanding threat, which requires raised awareness, education, prevention, and treatment initiatives nationally and globally. This review presents an updated review on the relationships between oral health and mental health disorders and the importance of oral health in mental health disorders.

Method

A literature search was done regarding mental disorders and oral health approaches in Google Scholar and PubMed from the year 1995 until 2023. All the English-language papers were evaluated based on the inclusion criteria. Publications included original research papers, review articles and book chapters.

Results

Common mental disorders include depression, anxiety, bipolar disorder, Schizophrenia, dementia, and alcohol and drug use disorders. The interplay of oral health and mental disorders involves dysregulated microbiome, translocated bacteria, and systemic inflammation, among others.

Conclusion

There is a complex relationship between mental disorders and oral diseases. Various oral health problems are associated with mental health problems. The interplay of oral health and mental disorders involves dysregulated microbiome, translocated bacteria, and systemic inflammation, among others. Mental health nurses including physicians and dental professionals should be involved in the oral health care of mental health disorder patients. Therefore, multidisciplinary should be involved in the care of mental health disorders, and they should consider oral health care as an essential part of their care for patients with mental health disorders. Future investigations should strive to elucidate the exact biological relationships, to develop new directions for treatment.

Enhanced Peroxidase-Mimic Catalytic Activity via Cerium Doping of Strontium-Based Metal-Organic Frameworks with Design of a Smartphone-Based Sensor for On-Site Salivary Total Antioxidant Capacity Detection in Lung Cancer Patients

The development of artificial nanozymes with superior catalytic performance and excellent stability has been a long-standing objective for chemists. The total antioxidant capacity (TAC) is one of the most important bioanalytical measures of oxidative stress in the body. The present work aims to develop a smartphone-assisted visual detection sensor using cerium-doped strontium-based metal-organic frameworks (Ce-SrMOFs) as peroxidase-like nanozymes for the rapid, low-cost, on-site detection of TAC. The pristine SrMOF functioned as a peroxidase nanozyme, and its enzymatic activity was enhanced after doping it with Ce(IV) ions because of the multivalent nature and synergistic impact of the heteroatoms. The Ce-SrMOFs were sensitive to the single electron transfer and hydrogen atom transfer processes, which implies that the Ce-SrMOFs can serve as an ideal nanozyme candidate for TAC analysis. The investigated mechanism revealed that •OH is the most active oxygen species for the peroxidase-like activity. The Ce-SrMOFs exhibited a strong affinity for 3,3',5,5'-tetramethylbenzidine (TMB) and H₂O₂, with K_m values of 0.082 and 0.427 mM, which are 5.29- and 8.67-fold lower than those of horseradish peroxidase (HRP), respectively. The Ce-SrMOFs were used for the detection of ascorbic acid, cysteine, and glutathione, with limits of detection of 44, 53, and 512 nM, respectively. The proposed method proved effective in measuring the TAC in saliva samples from lung cancer patients, thereby yielding results with satisfactory precision and accuracy.

LC-MS/MS Based Volatile Organic Compound Biomarkers Analysis for Early Detection of Lung Cancer

(1) Background: lung cancer is the world's deadliest cancer, but early diagnosis helps to improve the cure rate and thus reduce the mortality rate. Annual low-dose computed tomography (LD-CT) screening is an efficient lung cancer-screening program for a high-risk population. However, LD-CT has often been characterized by a higher degree of false-positive results. To meet these challenges, a volatolomic approach, in particular, the breath volatile organic compounds (VOCs) fingerprint analysis, has recently received increased attention for its application in early lung cancer screening thanks to its convenience, non-invasiveness, and being well tolerated by patients. (2) Methods: a LC-MS/MS-based volatolomics analysis was carried out according to P/N 5046800 standard based breath analysis of VOC as novel cancer biomarkers for distinguishing early-stage lung cancer from the healthy control group. The discriminatory accuracy of identified VOCs was assessed using subject work characterization and a random forest risk prediction model. (3) Results: the proposed technique has good performance compared with existing approaches, the differences between the exhaled VOCs of the early lung cancer patients before operation, three to seven days after the operation, as well as four to six weeks after operation under fasting and 1 h after the meal were compared with the healthy controls. The results showed that only 1 h after a meal, the concentration of seven VOCs, including 3-hydroxy-2-butanone (TG-4), glycolaldehyde (TG-7), 2-pentanone (TG-8), acrolein (TG-11), nonaldehyde (TG-19), decanal (TG-20), and crotonaldehyde (TG-22), differ significantly between lung cancer patients and control, with the invasive adenocarcinoma of the lung (IAC) having the most significant difference. (4) Conclusions: this novel, non-invasive approach can improve the detection rate of early lung cancer, and LC-MS/MS-based breath analysis could be a promising method for clinical application.

Non-Invasive Biomarkers for Early Lung Cancer Detection

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.

Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study

Background

Saliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non‐invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL).

Materials and Methods

Unstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.

Results

The profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516–0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back‐selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598–0.861, p = 0.003).

Conclusion

The four salivary metabolites can serve as potential non‐invasive biomarkers for distinguishing LC from BLL.