Serum Folate Levels and Lung Cancer Risk: A Meta- Epidemiological Study of Population-based Case-Control Studies

Development and validation of a nomogram model for lung cancer based on radiomics artificial intelligence score and clinical blood test data

Background

Artificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.

Methods

Between 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.

Findings

A total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.

Conclusion

The nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Identification of Candidate lncRNA and Pseudogene Biomarkers Associated with Carbon-Nanotube-Induced Malignant Transformation of Lung Cells and Prediction of Potential Preventive Drugs

Mounting evidence has linked carbon nanotube (CNT) exposure with malignant transformation of lungs. Long non-coding RNAs (lncRNAs) and pseudogenes are important regulators to mediate the pathogenesis of diseases, representing potential biomarkers for surveillance of lung carcinogenesis in workers exposed to CNTs and possible targets to develop preventive strategies. The aim of this study was to screen crucial lncRNAs and pseudogenes and predict preventive drugs. GSE41178 (small airway epithelial cells exposed to single- or multi-walled CNTs or dispersant control) and GSE56104 (lung epithelial cells exposed to single-walled CNTs or dispersant control) datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis was performed for these two datasets, and the turquoise module was preserved and associated with CNT-induced malignant phenotypes. In total, 24 lncRNAs and 112 pseudogenes in this module were identified as differentially expressed in CNT-exposed cells compared with controls. Four lncRNAs (MEG3, ARHGAP5-AS1, LINC00174 and PVT1) and five pseudogenes (MT1JP, MT1L, RPL23AP64, ZNF826P and TMEM198B) were predicted to function by competing endogenous RNA (MEG3/RPL23AP64-hsa-miR-942-5p-CPEB2/PHF21A/BAMBI; ZNF826P-hsa-miR-23a-3p-SYNGAP1, TMEM198B-hsa-miR-15b-5p-SYNGAP1/CLU; PVT1-hsa-miR-423-5p-PSME3) or co-expression (MEG3/MT1L/ZNF826P/MT1JP-ATM; ARHGAP5-AS1-TMED10, LINC00174-NEDD4L, ARHGAP5-AS1/PVT1-NIP7; MT1L/MT1JP-SYNGAP1; MT1L/MT1JP-CLU) mechanisms. The expression levels and prognosis of all genes in the above interaction pairs were validated using lung cancer patient samples. The receiver operating characteristic curve analysis showed the combination of four lncRNAs, five pseudogenes or lncRNAs + pseudogenes were all effective for predicting lung cancer (accuracy >0.8). The comparative toxicogenomics database suggested schizandrin A, folic acid, zinc or gamma-linolenic acid may be preventive drugs by reversing the expression levels of lncRNAs or pseudogenes. In conclusion, this study highlights lncRNAs and pseudogenes as candidate diagnostic biomarkers and drug targets for CNT-induced lung cancer.

Nutritional Interventions for Patients with Melanoma: From Prevention to Therapy-An Update

Melanoma is an aggressive skin cancer, whose incidence rates have increased over the past few decades. Risk factors for melanoma are both intrinsic (genetic and familiar predisposition) and extrinsic (environment, including sun exposure, and lifestyle). The recent advent of targeted and immune-based therapies has revolutionized the treatment of melanoma, and research is focusing on strategies to optimize them. Obesity is an established risk factor for several cancer types, but its possible role in the etiology of melanoma is controversial. Body mass index, body surface area, and height have been related to the risk for cutaneous melanoma, although an 'obesity paradox' has been described too. Increasing evidence suggests the role of nutritional factors in the prevention and management of melanoma. Several studies have demonstrated the impact of dietary attitudes, specific foods, and nutrients both on the risk for melanoma and on the progression of the disease, via the effects on the oncological treatments. The aim of this narrative review was to summarize the main literature results regarding the preventive and therapeutic role of nutritional schemes, specific foods, and nutrients on melanoma incidence and progression.

Vitamins as Possible Cancer Biomarkers: Significance and Limitations

The Western-style diet, which is common in developed countries and spreading into developing countries, is unbalanced in many respects. For instance, micronutrients (vitamins A, B complex, C, D, E, and K plus iron, zinc, selenium, and iodine) are generally depleted in Western food (causing what is known as ‘hidden hunger’), whereas some others (such as phosphorus) are added beyond the daily allowance. This imbalance in micronutrients can induce cellular damage that can increase the risk of cancer. Interestingly, there is a large body of evidence suggesting a strong correlation between vitamin intake as well as vitamin blood concentrations with the occurrence of certain types of cancer. The direction of association between the concentration of a given vitamin and cancer risk is tumor specific. The present review summarized the literature regarding vitamins and cancer risk to assess whether these could be used as diagnostic or prognostic markers, thus confirming their potential as biomarkers. Despite many studies that highlight the importance of monitoring vitamin blood or tissue concentrations in cancer patients and demonstrate the link between vitamin intake and cancer risk, there is still an urgent need for more data to assess the effectiveness of vitamins as biomarkers in the context of cancer. Therefore, this review aims to provide a solid basis to support further studies on this promising topic.