The Impact of Age and Sex in DLBCL: Systems Biology Analyses Identify Distinct Molecular Changes and Signaling Networks

Potential molecular alterations based on age and sex are not well defined in diffuse large B-cell lymphoma (DLBCL). We examined global transcriptome DLBCL data from The Cancer Genome Atlas (TCGA) via a systems biology approach to determine the molecular differences associated with age and sex. Collectively, sex and age revealed striking transcriptional differences with older age associated with decreased metabolism and telomere functions and female sex was associated with decreased interferon signaling, transcription, cell cycle, and PD-1 signaling. We discovered that the key genes for most groups strongly regulated immune function activity. Furthermore, older females were predicted to have less DLBCL progression versus older males and young females. Finally, analyses in systems biology revealed that JUN and CYCS signaling were the most critical factors associated with tumor progression in older and male patients. We identified important molecular perturbations in DLBCL that were strongly associated with age and sex and were predicted to strongly influence tumor progression.

[1]  Philip Hahnfeldt,et al.  Proton irradiation impacts age-driven modulations of cancer progression influenced by immune system transcriptome modifications from splenic tissue , 2015, Journal of radiation research.

[2]  Philip Hahnfeldt,et al.  Host age is a systemic regulator of gene expression impacting cancer progression. , 2015, Cancer research.

[3]  M. J. You,et al.  Jun-regulated genes promote interaction of diffuse large B-cell lymphoma with the microenvironment. , 2015, Blood.

[4]  Pierre Koch,et al.  Inhibitors of c-Jun N-terminal kinases: an update. , 2015, Journal of medicinal chemistry.

[5]  Yurii B. Shvetsov,et al.  Identification of six new susceptibility loci for invasive epithelial ovarian cancer , 2015, Nature Genetics.

[6]  Kconfab Investigators,et al.  Identification of six new susceptibility loci for invasive epithelial ovarian cancer , 2015 .

[7]  Michael Hallek,et al.  Optimization of rituximab for the treatment of diffuse large B-cell lymphoma (II): extended rituximab exposure time in the SMARTE-R-CHOP-14 trial of the german high-grade non-Hodgkin lymphoma study group. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  M. Jerkeman,et al.  Improvement in survival of diffuse large B-cell lymphoma in relation to age, gender, International Prognostic Index and extranodal presentation: a population based Swedish Lymphoma Registry study , 2014, Leukemia & lymphoma.

[9]  Leng Han,et al.  Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types , 2014, Nature Communications.

[10]  N. Schmitz,et al.  Suboptimal dosing of rituximab in male and female patients with DLBCL. , 2014, Blood.

[11]  B. Coiffier,et al.  Impact of BMI and Gender on Outcomes in DLBCL Patients Treated with R-CHOP: A Pooled Study from the LYSA , 2014 .

[12]  Manuel Serrano,et al.  The Hallmarks of Aging , 2013, Cell.

[13]  B. Coiffier,et al.  Diffuse Large B-cell Lymphoma in the Elderly: A Review of Potential Difficulties , 2013, Clinical Cancer Research.

[14]  R. Spang,et al.  Patient age at diagnosis is associated with the molecular characteristics of diffuse large B-cell lymphoma. , 2012, Blood.

[15]  Scott E. Smith,et al.  Analysis of very elderly (≥80 years) non‐hodgkin lymphoma: impact of functional status and co‐morbidities on outcome , 2012, British journal of haematology.

[16]  T. Lightfoot,et al.  Non‐Hodgkin lymphoma and autoimmunity: Does gender matter? , 2011, International journal of cancer.

[17]  Gary D Bader,et al.  Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation , 2010, PloS one.

[18]  M. Briehl,et al.  Increased cytochrome c correlates with poor survival in aggressive lymphoma. , 2010, Oncology letters.

[19]  J. Nevins,et al.  Gene expression profiles of tumor biology provide a novel approach to prognosis and may guide the selection of therapeutic targets in multiple myeloma. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  Tao Jiang,et al.  A Systems Biology-Based Gene Expression Classifier of Glioblastoma Predicts Survival with Solid Tumors , 2009, PloS one.

[21]  Tak W. Mak,et al.  Cytochrome c: functions beyond respiration , 2008, Nature Reviews Molecular Cell Biology.

[22]  P. Lograsso,et al.  Inhibitors of c-jun-N-terminal kinase (JNK). , 2008, Mini reviews in medicinal chemistry.

[23]  Peter G Arthur,et al.  Inhibitors of c-Jun N-terminal kinases—JuNK no more? , 2007, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics.

[24]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[25]  H. Nilsson‐Ehle,et al.  The impact of gender, age and patient selection on prognosis and outcome in diffuse large B-cell lymphoma—a population-based study , 2007, Leukemia & lymphoma.

[26]  E. Feuer,et al.  SEER Cancer Statistics Review, 1975-2003 , 2006 .

[27]  W. Liang,et al.  TM4 microarray software suite. , 2006, Methods in enzymology.

[28]  W. Liang,et al.  9) TM4 Microarray Software Suite , 2006 .

[29]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[30]  T. Miller,et al.  Effect of age on therapeutic outcome in advanced diffuse histiocytic lymphoma: the Southwest Oncology Group experience. , 1986, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.