Molecular landscape of lung epithelium contributes to high severity and comorbidities for COVID-19 and lung cancer

2021 ◽  
Vol 17 ◽  
Author(s):  
Nilesh Kumar Sharma ◽  
Sachin C Sarode ◽  
Gargi S Sarode ◽  
Shankargouda Patil
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Molecular Genetic ◽  
Complex Nature ◽  
Lung Epithelium ◽  
Lung Cancer Patients ◽  
Tissue Microenvironment ◽  
High Severity ◽  
Cancer Types ◽  
Molecular Landscape

: The heterogeneous and complex nature of cancer is extensively revealed at molecular, genetic and tissue microenvironment levels. Currently, co-occurrence of coronavirus disease 2019 (COVID-19) to lung cancer patients and severity of infections by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is being understood at preclinical and clinical levels. However, molecular and cellular insights are not discussed in those papers that support the increased COVID-19 severity and comorbidities in several cancer types, including lung cancer patients. Therefore, this perspective highlights the basis of high severity and comorbidities among lung cancer patients infected by COVID-19, emphasizing translational aspects.

scholarly journals Development and validation of a prognostic nomogram in lung cancer with obstructive sleep apnea syndrome

2021 ◽  
Author(s):  
ke hu ◽  
Wei Liu ◽  
xiaofeng wu ◽  
ling zhou
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Sleep Apnea Syndrome ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Lung Cancer Patients ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
Cancer Types

Abstract Background To analyze the prognostic factors and survival rate of lung cancer patients with OSA by nomogram. Methods We developed and validated a nomogram to help improve prediction of overall survival in Lung cancer patients with OSA. The nomogram was established by a development cohort (n = 90), and the validation cohort included 38 patients. Factors in the nomogram were identified by Cox hazard analysis. We tested the accuracy of the nomograms by discrimination and calibration, and plotted decision curves to assess the benefits of nomogram-assisted decisions. At last we evaluated the risk in the two cohort. Results There were significant difference in sex, AHI, TNM stage, coronary heart disease, LSpO2, T90%, TS90% and ODI4 between lung cancer subgroup and lung cancer with OSA subgroup (P < 0.05). Lung cancer patients with OSA age(P = 0.021), AHI(P = 0.006), TNM stage(P = 0.005), cancer types(P = 0.038), BMI(P = 0.007) and were independent prognostic factor. Based on these six factors, a nomogram model was established. The c-index of internal verification was 0.802 (95% CI 0.767–0.885)。The ROC curve analysis for the nomogram show 1-year survival (AUC = 0.827), 3-year survival (AUC = 0.867), 5-year survival (AUC = 0.801) in the development cohort and 3-year survival(AUC = 0.863) in the validation cohort is with good accuracy. The calibration curve shows that the survival probability of this prediction model is in good agreement with the actual situation at 1, 3 and 5 years with good consistency. decision curve analysis(DCA)suggests that the net benefit of decision-making with this nomogram is higher, especially in the probability interval of < 20% threshold. Conclusions Age, AHI,TNM stage, cancer types, BMI and ODI4 are independent risk factors for prognosis of lung cancer patients with OSAS༌the nomogram can predict the prognosis of patients and guide individualized treatment.

scholarly journals BSCI-12. COMPREHENSIVE GENOMIC ANALYSIS OF BRAIN METASTASES FROM MULTIPLE CANCER TYPES

2019 ◽  
Vol 1 (Supplement_1) ◽  
pp. i3-i3
Author(s):  
Kazutaka Fukumura ◽  
Xizeng Mao ◽  
Xingzhi Song ◽  
Grant Fischer ◽  
Jie Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Brain Metastasis ◽  
Brain Metastases ◽  
Cancer Patients ◽  
Genomic Analysis ◽  
Primary Tumors ◽  
Lung Cancer Patients ◽  
Cancer Types ◽  
Methylation Profiling

Abstract PURPOSE: Brain metastases occur in approximately 8–10% of patients with cancer, and the incidence has increased over the past decades. The most common primary tumors responsible for brain metastases are lung cancer, melanoma, renal cell carcinoma (RCC), breast cancer and colorectal cancer. The precise mechanisms by which genomic and transcriptional abnormalities drive the formation of brain metastases remain unclear. Here, we conducted comprehensive genomic and transcriptional analysis with paired primary tumor tissue (or extracranial metastasis tissue) and brain metastasis tissue using whole-exome sequencing (WES), mRNA-Seq and global methylation profiling. METHODS: Frozen, paired brain metastasis tissue and primary tumor tissue (or extracranial metastasis tissue) and white blood cells were acquired from RCC (n=12), breast cancer (n=17), lung cancer (n=15) and melanoma (n=14) patients, followed by extraction of DNA and RNA. WES and mRNA-Seq were performed on the Illumina HiSeq4000 platform. For methylation profiling, DNA was analyzed using Illumina Infinium MethylationEPIC Beadchip arrays. RESULTS: Somatic mutations or methylation of VHL gene were identified in 81.8% of RCC patients. Gene Set Enrichment Analysis revealed significant enrichment for hypoxia pathway transcripts in RCC brain metastases relative to primary tumors. The most common alterations in breast and lung cancer patients were TP53 mutations with frequencies of 50.0% and 73.3%, followed by ERBB2 alterations (43.8%) in breast cancer patients and mutually exclusive alterations of EGFR (33.3%) and KRAS (26.7%) in lung cancer patients. Mutually exclusive alterations of NRAS (42.9%) and BRAF (42.9%) were also observed in melanoma patients. Gene expression and epigenetic analysis revealed characteristics of brain metastases depending on primary cancer types. CONCLUSIONS: Comprehensive genomic analysis of brain metastases from four different cancer types revealed that brain metastasis tissues have unique genomic, transcriptional and epigenetic profiles according to histopathology groups. Therefore, the therapeutic strategies should be designed based at least in part on tumor histiogenesis.

scholarly journals Is hyperprogressive disease a specific phenomenom of immunotherapy?

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Marta Brambilla ◽  
Giuseppe Lo Russo ◽  
Roberto Ferrara ◽  
Sara Manglaviti ◽  
Marina Chiara Garassino ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cell Lung Cancer ◽  
Retrospective Studies ◽  
Nonsmall Cell Lung Cancer ◽  
Small Cell Lung ◽  
Cancer Treatments ◽  
Lung Cancer Patients ◽  
Cancer Types ◽  
Hyperprogressive Disease

Hyperprogressive disease (HPD) is a novel pattern of response during immunotherapy treatment. Several retrospective studies have evaluated its prevalence among various cancer types and, in particular, in nonsmall cell lung cancer patients, based on different definition criteria. If HPD is a just a typical phenomenon of immunotherapy is still an unsolved concern. This paper summarized the available data about HPD in other cancer treatments. Hyperprogressive disease (HPD) is a novel pattern of response during immunotherapy treatment. Several retrospective studies have evaluated its prevalence among various cancer types and, in particular, in non-small cell lung cancer patients, based on different definition criteria. If HPD is a just a typical phenomenon of immunotherapy is still an unsolved concern. This paper summarized the available data about HPD in other cancer treatments.

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