Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma (original) (raw)
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American Journal of Pathology, 1999
Many genes and signaling pathways are involved in renal cell carcinoma (RCC) development. However, genetic tumor markers have not gained use in RCC diagnostics and prognosis prediction. Identification and evaluation of new molecular parameters are of utmost importance in cancer research and cancer treatment. Here we present a novel approach to rapidly identify clinically relevant molecular changes in cancer. To identify genes with relevance to RCC, a cDNA array analysis was first performed on 5184 cDNA clones on a filter to screen for genes with differential expression between the renal cancer cell line CRL-1933 and normal kidney tissue. There were 89 differentially expressed genes in the cancer cell line , one of them coding for vimentin , a cytoplasmic intermediate filament. In a second step , a renal cancer tissue microarray containing 532 RCC specimen was used to determine vimentin expression by immunohistochemistry. Vimentin expression was seen frequently in clear cell (51%) and papillary RCC (61%), but rarely in chromophobe RCC (4%) and oncocytomas (12%). Furthermore , vimentin expression was significantly associated with poor patient prognosis (P < 0.007) independent of grade and stage. These results obtained from minute arrayed tumor samples match well with previous findings on vimentin expression in renal tumors. It is concluded that the combination of tumor arrays and cDNA arrays is a powerful approach to rapidly identify and further evaluate genes that play a role in tumor biology.
Primer on Medical Genomics Part III: Microarray Experiments and Data Analysis
Mayo Clinic Proceedings, 2002
Genomics has been defined as the comprehensive study of whole sets of genes, gene products, and their interactions as opposed to the study of single genes or proteins. Microarray technology is one of many novel tools that are allowing global and high-throughput analysis of genes and gene products. In addition to an introduction on underlying principles, the current review focuses on the use of both complementary DNA and oligodeoxynucleotide microarrays in gene expression analysis. Genome-wide experiments generate a massive amount of data points that require systematic methods of analysis to extract biologically useful information. Accordingly, the current educational communication discusses different methods of data analysis, including supervised and unsupervised clustering algorithms. Illustrative clinical examples show clinical applications, including (1) identification of candidate genes or pathological pathways (ie, elucidation of pathogenesis); (2) identification of "new" molecular classes of diseases ALL = acute lymphocytic leukemia; AML = acute myeloid leukemia; bp = base pair; cDNA = complementary DNA; CLL = chronic lymphocytic leukemia; cRNA = complementary RNA; DLBCL = diffuse large B-cell lymphoma; EST = expressed sequence tag; FL = follicular lymphoma; mRNA = messenger RNA; PCR = polymerase chain reaction; SOM = self-organizing map that may be relevant in disease reclassification, prognostication, and treatment selection (ie, class discovery); and (3) use of expression profiles of known disease classes to predict diagnosis and classification of unknown samples (ie, class prediction). The current review should serve as an introduction to the subject for clinician investigators, physicians and medical scientists in training, practicing clinicians, and other students of medicine.
Abstracts from USCAP 2019: Medical Renal Pathology (including transplantation) (1581-1620)
Modern Pathology
Background: Immune checkpoint inhibitor (ICI) therapy has shown survival benefit in a growing number of malignancies. However, due to their immunostimulatory mechanism of action, ICIs have been associated with numerous autoimmune and alloimmune adverse events, including acute interstitial nephritis (AIN) in native kidneys and acute T-cell mediated rejection (TCMR) in transplant kidneys. The goal of this study was to further characterize these adverse events through comparative molecular analysis. Design: NanoString was used to measure the expression of 800 genes in 25 archival kidney biopsies. The genes included a 770-gene PanCancer Immune Profiling Panel plus 30 additional TCMR-related genes. The samples included native kidney biopsies with ICI-related AIN (ICI-AIN, n=2), ICI-related crescentic glomerulonephritis (ICI-CGN, n=1) and non-ICI-related AIN (AIN, n=4); transplant kidney biopsies with ICI-related TCMR (ICI-TCMR, n=2) and non-ICI-related TCMR (TCMR, n=8); and normal implantation biopsies (Normal, n=8). Exploratory analysis was performed using principal component analysis (PCA) and heat maps with hierarchical clustering. Gene sets associated with TCMR and AIN were identified using volcano plot analysis with a false discovery rate (FDR) threshold of 0.05. Mann-Whitney U-test was used to compare gene set expression between groups. Results: Unsupervised PCA and heat map analysis demonstrated distinct clustering of Normal, AIN and TCMR biopsies based on gene expression (Figure 1). ICI-AIN overlapped with AIN while, interestingly, ICI-TCMR clustered more closely with AIN than TCMR. 341 genes were significantly upregulated in TCMR vs. Normal and AIN, and 111 genes were upregulated in AIN vs. Normal and TCMR (FDR<0.05); the top 100 of each were combined into aggregate TCMR and AIN gene sets, respectively. TCMR gene set expression was higher in TCMR than AIN (p=0.048) but there was no significant difference between ICI-TCMR and ICI-AIN, and no difference between the ICI groups and TCMR or AIN (Figure 2). AIN gene set expression was higher than TCMR in AIN, ICI-AIN and ICI-TCMR (p<0.044) but there was no significant difference between these three groups.
DNA expression analysis: serial analysis of gene expression, microarrays and kidney disease
Current Opinion in Nephrology and Hypertension, 2003
Purpose of review Expression profiling using serial analysis of gene expression and microarray technologies allows global description of expressed genes present in biological systems. Although relatively new technologies, each having been developed in the mid-1990s, both have become established and widely used tools for identification of gene networks and gene function. Recent findings This review highlights DNA expression analyses published in 2002, emphasizing primarily serial analysis of gene expression and microarray technologies. We focus on the applicability of DNA expression analysis to renal disease, especially as some investigators have developed custom serial analysis of gene expression kidney libraries and kidney disease-specific 'designer chip' microarrays. Data analysis techniques and statistics are also discussed, since the challenge is generation of accurate messenger RNA profiles and interpretation of data in a manner that is both coherent and reproducible. Summary Because kidney disease pathophysiology is complex, expression analysis can identify candidate nephropathy pathogenesis genes and gene networks, which eventually could become targets for therapeutic intervention.
Biological and Clinical Sciences Research Journal
2024
Type 2 diabetes mellitus (T2DM) and lung cancer are two prevalent health issues worldwide, each carrying significant morbidity and mortality burdens. Objective: The main objective of the study is to find the effects of type 2 diabetes Type 2 diabetes and its impact on the risk of developing ARDS in patients with lung cancer postoperatively and its prognosis. Methods: This retrospective study was conducted at CMH Lahore from 2021 to 2022. Data were collected from 320 patients diagnosed with lung cancer. Patients diagnosed with lung cancer who underwent surgery were included in the study. Patients with a history of any other malignancies and suffering from COPD were excluded from the study. Electronic medical records were reviewed to collect demographic information, including age, sex, body mass index (BMI), smoking history, comorbidities (including T2DM), tumour characteristics, surgical procedures performed, and postoperative outcomes. Results: Data were collected from 320 diagnosed patients with lung cancer according to inclusion and exclusion criteria. The mean age of the patients was 58.98±5.67 years. There were 60% male and 40% female patients. The prevalence of T2DM was 25%, and 50% followed the lobectomy surgical procedure. 40% of the patients were from stage I, 30% from stage II, 20% from stage III and 10% at stage IV. Forced Expiratory Volume in 1 Second (FEV1) had a mean value of 2.5 litres with a standard deviation of 0.8 litres. Forced Vital Capacity (FVC) showed a mean of 3.0 litres with a standard deviation of 1.0. The FEV1/FVC ratio was approximately 0.83, with a standard deviation of 0.05. Total Lung Capacity (TLC) exhibited a mean value of 5.0 litres with a standard deviation of 1.2 litres. Residual Volume (RV) had a mean of 1.2 litres with a standard deviation of 0.4. Conclusion: It is concluded that type 2 diabetes mellitus (T2DM) is associated with a higher incidence of acute respiratory distress syndrome (ARDS) in lung cancer surgery. T2DM independently predicts lesser prognosis and survival outcomes in patients who develop ARDS postoperatively.