scholarly journals Radiomics and gene expression profile to characterize the disease and predict outcome in patients with lung cancer

2021 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Marinella Corbetta ◽  
Emanuele Voulaz ◽  
Noemi Gozzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Biology ◽  
Area Under The Curve ◽  
Standardized Uptake Value ◽  
Gene Expression Signature ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Cancer Tissue ◽  
Tissue Samples

Abstract Objectives The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC).Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F]-FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n=74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalized linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence.Results Standardized Uptake Value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule in predicting the outcome resulted in an Area Under the Curve (AUC) of 0.87.Conclusions: Radiogenomic data provided clinically relevant information in NCSCL patients, regarding the histotype, aggressiveness, and progression. Gene expression may provide additional valuable information to guide patient management. The application of ML allows to increase the efficacy of radiogenomic analysis and provide novel insights into cancer biology.

scholarly journals Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

2021 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Marinella Corbetta ◽  
Emanuele Voulaz ◽  
Noemi Gozzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Biology ◽  
Area Under The Curve ◽  
Gene Expression Signature ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Relevant Information ◽  
Cancer Tissue ◽  
Tissue Samples

Abstract Objective The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

scholarly journals RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

Biomedicines ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 114
Author(s):  
Maxim Sorokin ◽  
Kirill Ignatev ◽  
Elena Poddubskaya ◽  
Uliana Vladimirova ◽  
Nurshat Gaifullin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Correlation Study ◽  
Cancer Tissue ◽  
Transcriptional Level ◽  
Tissue Samples ◽  
Protein Levels ◽  
Formalin Fixed Paraffin Embedded

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman’s rho 0.65–0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

Signal Transducer and Activator of Transcription6 Signaling Pathway in Tumor-Associated Macrophage Aggravates Lung Cancer Progression

2021 ◽  
Vol 11 (9) ◽  
pp. 1707-1713
Author(s):  
Jun Wan ◽  
Jian Wang ◽  
Qiurong Huang ◽  
Guanggui Ding ◽  
Xiean Ling
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
T Cell ◽  
Cancer Progression ◽  
Cancer Tissue ◽  
M2 Macrophage ◽  
Normal Tissues ◽  
Tumor Associated Macrophage ◽  
M1 Macrophage ◽  
And Migration

Lung cancer is a most common cancer worldwide. Tumor-associated macrophage (TAM) is known a key effector cell in tumor microenvironment. Meanwhile, STAT6 is crucial to cancer development. We aimed to determine the interaction between STAT6 and TAMs in lung cancer. In this work, firstly, we established mouse model of lung cancer. Then, immunofluorescence was performed to determine STAT6 and CD206 level in lung cancer tissue and adjacent normal tissues as well as model mice. RT-qPCR was applied to detect differentiation of macrophage and determine related gene expression. After treatment of siRNA of STAT6 or STAT6 inhibitor (AS1517499), Transwell assay and MTT were used to determine cell proliferation and migration. STAT6 was upregulated in lung cancer tissues while arginase was more active in M2 macrophage rather than M1 macrophage. Transfection of si-STAT6 not only decreased differentiation in M2 macrophage but also inhibited proliferative, migratory and invasive ability of cancer cells while AS1517499 led to reduced tumor growth. STAT6 inhibition caused decreased expression of M2 macrophages. Similarly, intratumoral T cell markers showed that CD8+T cell gene expression and CD4-mediated T cell marker FoxP3 was increased slightly. Taken altogether, macrophage-STAT6 promotes cell migration and proliferation in lung cancer.

Association of hypoxia-induced centrosome amplification with clinical outcomes in triple-negative breast cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23170-e23170
Author(s):  
Karuna Mittal ◽  
Da Hoon Choi ◽  
Angela Ogden ◽  
Brian D Melton ◽  
Meenakshi Vij Gupta ◽  
...  
Keyword(s):  
Gene Expression ◽  
Triple Negative ◽  
Gene Expression Signature ◽  
Breast Tumors ◽  
Centrosome Amplification ◽  
Strong Positive Correlation ◽  
Breast Cancers ◽  
Tissue Samples ◽  
Hypoxic Conditions ◽  
Cells Cultured

e23170 Background: Centrosome amplification (CA) which refers to presence of supernumerary or abnormally large centrosomes is believed to drive tumor progression by promoting chromosomal instability and the generation of aggressive tumor clones that are more capable of rapid metastasis. Not much is known about factors that drive CA within solid tumors. We have previously shown the existence of rampant CA in triple-negative breast cancers (TNBCs).We report here thatintratumoral hypoxia, which is one of the major contributors to tumor heterogeneity, induces CA in TNBCs via HIF-1α. Methods: We immunohistochemically labeled 24 TNBC and adjacent normal tissue samples for HIF-1α and derived weighted indices (WIs) for nuclear HIF-1α. Adjacent serial sections from the same tumors were immunofluorescently labeled for the centrosomal marker γ-tubulin and CA was determined. Using public microarray datasets (Kao dataset, n = 327), we investigated whether centrosomal gene expression is enriched in breast tumors characterized by a hypoxia gene expression signature. Finally, to test the role of hypoxia in CA induction we exposed cultured TNBC cells (MDA-MB-231 and MDA-MB-468) to hypoxia and overexpressed (OE) or knocked out (KO) HIF-1α and quantitated CA. Results: A strong positive correlation was found between nuclear HIF-1α WI and CA in TNBC samples (Spearman’s rho p = 0.722, p < 0.001), and higher nuclear HIF-1α was associated with worse overall survival (p = 0.041; HR = 1.03). Furthermore, breast tumors with high expression of hypoxia-associated genes exhibited higher expression of centrosomal genes than breast tumors with low expression of hypoxia-associated genes. TNBC cells cultured in hypoxic conditions exhibited ~1.5 fold higher (p < 0.05) CA compared to cells cultured in normoxic conditions. Interestingly, level of CA decreased when HIF-1α KO TNBC cells were exposed to hypoxia; conversely, CA increased when HIF-1α OE TNBC cells were cultured in normoxic conditions. Conclusions: Thus,intratumoral hypoxia drives CA in TNBC via HIF-1α and contributes to poor outcomes. Determination of CA may help identify TNBC patients who could benefit from centrosome declustering drugs and HIF-1α inhibitors.

scholarly journals Non-small cell lung cancer microbiota characterization: Prevalence of enteric and potentially pathogenic bacteria in cancer tissues

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249832
Author(s):  
Nathan Dumont-Leblond ◽  
Marc Veillette ◽  
Christine Racine ◽  
Philippe Joubert ◽  
Caroline Duchaine
Keyword(s):  
Lung Cancer ◽  
Pathogenic Bacteria ◽  
Amplicon Sequencing ◽  
Healthy Tissue ◽  
Cancer Tissue ◽  
Rrna Gene ◽  
Variable Degree ◽  
Tissue Samples ◽  
Small Cancer ◽  
Cancer Tissues

Following recent findings linking the human gut microbiota to gastrointestinal cancer and its treatment, the plausible relationship between lung microbiota and pulmonary cancer is explored. This study aims at characterizing the intratumoral and adjacent healthy tissue microbiota by applying a 16S rRNA gene amplicon sequencing protocol to tissue samples of 29 non-small cancer patients. Emphasis was put on contaminant management and a comprehensive comparison of bacterial composition between cancerous and healthy adjacent tissues of lung adenocarcinoma and squamous cell carcinoma is provided. A variable degree of similarity between the two tissues of a same patient was observed. Each patient seems to possess its own bacterial signature. The two types of cancer tissue do not have a distinct bacterial profile that is shared by every patient. In addition, enteric, potentially pathogenic and pro-inflammatory bacteria were more frequently found in cancer than healthy tissue. This work brings insights into the dynamic of bacterial communities in lung cancer and provides prospective data for more targeted studies.

scholarly journals Evaluation of GNMT Gene Expression in Prostate Cancer Tissues using Real-Time PCR

2021 ◽  
Author(s):  
Niloofar Dehghani ◽  
Masoud Salehipour ◽  
Babak Javanmard
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Benign Prostatic Hyperplasia ◽  
Real Time ◽  
Real Time Pcr ◽  
Prostatic Hyperplasia ◽  
Expression Level ◽  
P Value ◽  
Cancer Tissue ◽  
Tissue Samples

Introduction: Prostate cancer is the second most common cancer and the leading cause of cancer-related deaths worldwide. In the present study, the expression level of glycine N-methyl transferase gene (GNMT) was investigated in prostate cancer tissue. The GNMT enzyme is encoded by the GNMT gene. Increased GNMT gene expression increases the conversion of glycine to sarcosine and results in the elevated levels of sarcosine in blood and urine. Methods: The expression level of GNMT gene in tissue samples of patients with prostate cancer was compared with those with benign prostatic hyperplasia using Real-Time PCR technique. Results: The GNMT gene expression level increased significantly in prostate cancer patients compared with those with benign prostatic hyperplasia (p-value <0.001). In addition, the expression level of GNMT gene was stage-dependent and  significant increases were observed in all stages of prostate cancer compared with those with benign prostatic hyperplasia (p-value <0.001). Conclusion: The concentration of sarcosine is controlled by GNMT and it seems that increasing the expression level of GNMT gene increases the level of sarcosine concentration. Thus, it appears that increased levels of GNMT expression occur in the early stages of prostate cancer. Therefore, periodic measurement of GNMT expression levels can detect prostate cancer before it forms a cancer cell and invades other tissues.

scholarly journals Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chen Xue ◽  
Yalei Zhao ◽  
Ganglei Li ◽  
Lanjuan Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Specific Expression ◽  
Tissue Samples ◽  
Expression Levels ◽  
Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

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