The role of telomerase reverse transcriptase in human malignancies : genetic polymorphisms and promoter mutations

Telomerase is a ribonucleoprotein with its catalytic subunit telomerase reverse transcriptase (TERT) as a key component, lengthening telomeres. In differentiated human cells, telomerase is silent due to the transcriptional repression of the TERT gene, but activated in oncogenesis. Telomerase activation/TERT induction is essential to unlimited proliferation of cancer cells via telomere lengthening, whereas recent evidence also suggests that TERT may be a master contributor of cancer hallmarks. It is thus important to define regulatory mechanisms underlying cancer-specific TERT expression, and to delineate oncogenic effects of TERT. This thesis is designed to address these issues with the following specific aims: (1) The association between single-nucleotide polymorphisms (SNPs) of the TERT gene and cancer susceptibility and (2) Biological/translational implications of cancer-specific TERT promoter mutations. The TERT SNP association with cancer risk has been extensively investigated, most studies being focused on rs2736100 and rs2736098. The rs2736100_CC genotype has been shown to be associated with higher risk for a number of cancer types. Consistently, we observed that male individuals carrying the rs2736100_CC exhibited greater susceptibility to myeloproliferative neoplasms (MPNs), clonal diseases with myeloid cell origin (PAPER I). Furthermore, a comparison between Swedish and Chinese populations revealed a significantly higher fraction of rs2736100_CC in Swedes, coupled with a higher MPN incidence (compared to that in China). In addition, we made the same genotyping in upper tract urothelial carcinoma (UTUC) and hepatocellular carcinoma (HCC). The rs2736100_AC genotype was associated with reduced UTUC risk compared to the rs2736100_AA and CC carriers (PAPER II), while there were no significant differences in the rs2736100 or rs2736098 genotype distribution between HCC patients and healthy individuals (PAPER III). Collectively, male/female and ethnical groups may harbor different germline TERT variants, thereby contributing to different incidences and susceptibility dependent on origins of malignancies. The recurrent TERT promoter mutations, recently identified in different human malignancies, stimulate TERT transcription and activate telomerase. To explore the biological and clinical implication of TERT promoter mutations, we sequenced the TERT promoter region in tumor specimens derived from patients with UTUC, bladder cancer (BC) and HCC (PAPERS III and IV), and mutations were observed in 65/220 (30%) UTUC, 41/70 (59%) BC and 57/190 (30%) of HCC patients, respectively. In UTUC, the presence of TERT promoter mutations was significantly correlated with metastases, whereas for HCC, there was a significant difference in rs2736098 and rs2736100 genotypes between wt and mutant TERT promoter-bearing tumors. The cancer risk genotype rs2736100_CC was significantly associated with a reduced incidence of TERT promoter mutations, while the rs2736098_CT genotype was significantly higher in HCCs with TERT promoter mutations. Thus, the germline TERT genetic background may substantially affect the incidence of TERT promoter mutations in HCCs. As TERT promoter mutations are absent in normal cells, we evaluated the mutant TERT promoter as a urinary biomarker for non-invasive detection of UTUC and BC. The mutant TERT promoter was indeed detectable in urine from the mutation-positive UTUC and BC patients using Sanger sequencing, but the sensitivity was only 60%. To improve it, we developed a Competitive Allele-Specific TaqMan PCR (castPCR), and achieved an overall sensitivity of 89% and specificity of 96%. Thus, castPCR assays of TERT promoter mutations may be useful tools for non-invasive, urine-based diagnostics of UTUC and BC. In summary, our findings gain new insights into the association of TERT SNPs with cancer risk and TERT promoter mutations. These results will hopefully contribute to the rational development of a TERT-based strategy for precision oncology

Joint transmit power allocation and splitting for swipt aided OFDM-IDMA in wireless sensor networks

In this paper, we propose to combine Orthogonal Frequency Division Multiplexing-Interleave Division Multiple Access (OFDM-IDMA) with Simultaneous Wireless Information and Power Transfer (SWIPT), resulting in SWIPT aided OFDM-IDMA scheme for power-limited sensor networks. In the proposed system, the Receive Node (RN) applies Power Splitting (PS) to coordinate the Energy Harvesting (EH) and Information Decoding (ID) process, where the harvested energy is utilized to guarantee the iterative Multi-User Detection (MUD) of IDMA to work under sufficient number of iterations. Our objective is to minimize the total transmit power of Source Node (SN), while satisfying the requirements of both minimum harvested energy and Bit Error Rate (BER) performance from individual receive nodes. We formulate such a problem as a joint power allocation and splitting one, where the iteration number of MUD is also taken into consideration as the key parameter to affect both EH and ID constraints. To solve it, a sub-optimal algorithm is proposed to determine the power profile, PS ratio and iteration number of MUD in an iterative manner. Simulation results verify that the proposed algorithm can provide significant performance improvement

Electronic properties and quantum transports in functionalized graphene Sierpinski carpet fractals

Recent progress in controllable functionalization of graphene surfaces enables the experimental realization of complex functionalized graphene nanostructures, such as Sierpinski carpet (SC) fractals. Herein, we model the SC fractals formed by hydrogen and fluorine functionalized patterns on graphene surfaces, namely, H-SC and F-SC, respectively. We then reveal their electronic properties and quantum transport features. From calculated results of the total and local density of state, we find that states in H-SC and F-SC have two characteristics: (i) low-energy states inside about |E/t|<1 (with t as the near-neighbor hopping) are localized inside free graphene regions due to the insulating properties of functionalized graphene regions, and (ii) high-energy states in F-SC have two special energy ranges including -2.3<E/t<-1.9 with localized holes only inside free graphene areas and 3<E/t<3.7 with localized electrons only inside fluorinated graphene areas. The two characteristics are further verified by the real-space distributions of normalized probability density. We analyze the fractal dimension of their quantum conductance spectra and find that conductance fluctuations in these structures follow the Hausdorff dimension. We calculate their optical conductivity and find that several additional conductivity peaks appear in high energy ranges due to the adsorbed H or F atoms

Towards Assumption-free Bias Mitigation

Despite the impressive prediction ability, machine learning models show discrimination towards certain demographics and suffer from unfair prediction behaviors. To alleviate the discrimination, extensive studies focus on eliminating the unequal distribution of sensitive attributes via multiple approaches. However, due to privacy concerns, sensitive attributes are often either unavailable or missing in real-world scenarios. Therefore, several existing works alleviate the bias without sensitive attributes. Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias. The latter requires strong assumptions about the correlation between sensitive and non-sensitive attributes. As data distribution and task goals vary, the strong assumption on non-sensitive attributes may not be valid and require domain expertise. In this work, we propose an assumption-free framework to detect the related attributes automatically by modeling feature interaction for bias mitigation. The proposed framework aims to mitigate the unfair impact of identified biased feature interactions. Experimental results on four real-world datasets demonstrate that our proposed framework can significantly alleviate unfair prediction behaviors by considering biased feature interactions