Evaluation of the role of KPNA2 mutations in breast cancer prognosis using bioinformatics datasets

Abstract

Breast cancer, comprising of several sub-phenotypes, is a leading cause of female cancer-related mortality in the UK and accounts for 15% of all cancer cases. Chemoresistant sub phenotypes of breast cancer remain a particular challenge. However, the rapidly-growing availability of clinical datasets, presents the scope to underpin a data-driven precision medicine-based approach exploring new targets for diagnostic and therapeutic interventions.We report the application of a bioinformatics-based approach probing the expression and prognostic role of Karyopherin-2 alpha (KPNA2) in breast cancer prognosis. Aberrant KPNA2 overexpression is directly correlated with aggressive tumour phenotypes and poor patient survival outcomes. We examined the existing clinical data available on a range of commonly occurring mutations of KPNA2 and their correlation with patient survival.Our analysis of clinical gene expression datasets show that KPNA2 is frequently amplified in breast cancer, with differences in expression levels observed as a function of patient age and clinicopathologic parameters. We also found that aberrant KPNA2 overexpression is directly correlated with poor patient prognosis, warranting further investigation of KPNA2 as an actionable target for patient stratification or the design of novel chemotherapy agents.In the era of big data, the wealth of datasets available in the public domain can be used to underpin proof of concept studies evaluating the biomolecular pathways implicated in chemotherapy resistance in breast cancer.

Proteomic profiling improves prognostic risk stratification of the Sarculator nomogram in soft tissue sarcomas of the extremities and trunk wall

“…KPNA2 is a member of the karyopherin family of nuclear export proteins and has been shown to have prognostic value in multiple cancer types including breast and prostate cancer. 48 , 49 , 50 , 51 Furthermore, this protein regulates tumour growth and migration in preclinical models of several cancer types including liver and gallbladder cancer. 52 , 53 While there are currently no drugs that directly targets KPNA2, the deubiquinating enzyme USP1 has been shown to regulate the stabilisation of KPNA2.…”

“…KPNA2 is a member of the karyopherin family of nuclear export proteins and has been shown to have prognostic value in multiple cancer types including breast and prostate cancer. [48][49][50][51] F I G U R E 4 Calibration plots showing observed 5-year overall survival (OS) against predicted probabilities for Sarculator alone (left) and Sarculator + sarcoma proteomic module 6 (SPM6) score (right).…”

“…Of the non‐essential genes identified to have a functional effect in decreasing cell viability in >50% of the cell lines assessed, genetic knockdown of KPNA2 displayed one of the strongest effects. KPNA2 is a member of the karyopherin family of nuclear export proteins and has been shown to have prognostic value in multiple cancer types including breast and prostate cancer 48–51 . Furthermore, this protein regulates tumour growth and migration in preclinical models of several cancer types including liver and gallbladder cancer 52,53 .…”

Proteomic characterisation of Sarculator nomogram-defined risk groups in soft tissue sarcomas of the extremities and trunk wall

“…Of the non-essential genes identified to have a functional effect in decreasing cell viability in >50% of the cell lines assessed, genetic knockdown of KPNA2 displayed one of the strongest effects. KPNA2 is a member of the karyopherin family of nuclear export proteins and has been shown to have prognostic value in multiple cancer types including breast and prostate cancer [45][46][47][48] . Furthermore, this protein regulates tumour growth and migration in preclinical models of several cancer types including liver and gallbladder cancer 49,50 .…”

A gap analysis of UK biobank publications reveals SNPs associated with intrinsic subtypes of breast cancer

“…GeneMANIA serves as a valuable tool and database for predicting gene functions by leveraging a wide array of networks derived from diverse genomic and proteomic datasets [33] . We utilised cBioPortal, an open access resource for cancer genomics widely employed for investigating common gene alterations in various cancer types [34] , [35] , [36] , to examine gene alteration levels in different subyptes of breast cancer. Initially, all breast cancer studies were selected, and the 'Pam50 + Claudin-low subtype' option was chosen from the 'Charts' dropdown menu.…”