ObjectivesHuman papillomavirus (HPV) causes tumors primarily Cervical cancer. Recently, inconsistent reports came up in Breast cancer (BC) too. In India, despite treatment 70,218 BC patients die each year. So, we explored the association of HPV, if any, with BC prognosis in Indian pre-therapeutic (PT) and Neo-adjuvant chemotherapy (NACT) patients with subsequent analysis of HPV profile.MethodsHPV prevalence was checked and analysis of physical status, copy number, genome variation, promoter methylation and expression (mRNA and protein) of the prevalent subtype was done.ResultsHigh prevalence of HPV was observed in both PT (64.0%) and NACT (71.0%) cases with significant association with younger (20–45 yrs) PT patients. Interestingly, HPV infection was significantly increased from adjacent normal breast (9.5%, 2/21), fibro adenomas (30%, 3/10) to tumors (64.8%, 203/313) samples. In both PT and NACT cases, HPV16 was the most prevalent subtype (69.0%) followed by HPV18 and HPV33. Survival analysis illustrated hrHPV infected PT patients had worst prognosis. So, detailed analysis of HPV16 profile was done which showed Europian-G350 as the most frequent HPV16 variant along with high rate of integration. Moreover, low copy number and hyper-methylation of P97 early promoter were concordant with low HPV16 E6 and E7 mRNA and protein expression. Notably, four novel variations (KT020838, KT020840, KT020841 and KT020839) in the LCR region and two (KT020836 and KT020837) in the E6 region were identified for the first time along with two novel E6^E7*I (KU199314) and E6^E7*II (KU199315) fusion transcript variants.ConclusionThus, significant association of hrHPV with prognosis of Indian BC patients led to additional investigation of HPV16 profile. Outcomes indicated a plausible role of HPV in Indian BC patients.

Biological data are accumulating at a faster rate, but interpreting them still remains a problem. Classifying biological data into distinct groups is the first step in understanding them. Data classification in response to a certain treatment is an extremely important aspect for differentially expressed genes in making present/absent calls. Many feature selection algorithms have been developed including the support vector machine recursive feature elimination procedure (SVM-RFE) and its variants. Support vector machine RFEs are greedy methods that attempt to find superlative possible combinations leading to binary classification, which may not be biologically significant. To overcome this limitation of SVM-RFE, we propose a novel feature selection algorithm, termed as "sigFeature" (https://bioconductor.org/packages/sigFeature/), based on SVM and t statistic to discover the differentially significant features along with good performance in classification. The "sigFeature" R package is centered around a function called "sigFeature," which provides automatic selection of features for the binary classification. Using six publicly available microarray data sets (downloaded from Gene Expression Omnibus) with different biological attributes, we further compared the performance of "sigFeature" to three other feature selection algorithms. A small number of selected features (by "sigFeature") also show higher classification accuracy. For further downstream evaluation of its biological signature, we conducted gene set enrichment analysis with the selected features (genes) from "sigFeature" and compared it with the outputs of other algorithms. We observed that "sigFeature" is able to predict the signature of four out of six microarray data sets accurately, whereas the other algorithms predict less data set signatures. Thus, "sigFeature" is considerably better than related algorithms in discovering differentially significant features from microarray data sets.

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Cancer-associated p53 missense mutants confer (GOF) and promote tumorigenesis by regulating crucial signaling pathways. However, the role of GOF mutant p53 in regulating DNA replication, a commonly altered pathway in cancer, is less explored. Here, we show that enhanced Cdc7-dependent replication initiation enables mutant p53 to confer oncogenic phenotypes. We demonstrate that mutant p53 cooperates with the oncogenic transcription factor Myb and transactivates Cdc7 in cancer cells. Moreover, mutant p53 cells exhibit enhanced levels of Dbf4, promoting the activity of Cdc7/Dbf4 complex. Chromatin enrichment of replication initiation factors and subsequent increase in origin firing confirm increased Cdc7-dependent replication initiation in mutant p53 cells. Further, knockdown of significantly abrogates mutant p53-driven cancer phenotypes and Importantly, high expression significantly correlates with p53 mutational status and predicts poor clinical outcome in lung adenocarcinoma patients. Collectively, this study highlights a novel functional interaction between mutant p53 and the DNA replication pathway in cancer cells. We propose that increased Cdc7-dependent replication initiation is a hallmark of p53 mutations.

The aim of this study was to analyze the alterations of PTCH1 (deletion/promoter methylation/mutation/expression) during the development of cervical cancer (CACX). For this purpose, deletion/methylation of PTCH1 were analyzed in HPV16 positive exfoliated asymptomatic cervical swabs (n = 74), cervical intraepithelial neoplasia (CIN) (n = 32), CACX (n = 174) samples, and two CACX cell lines. The deletion of PTCH1 increased significantly from CIN (11.5%) to stage I/II (42%) and comparable in stage III/IV (46%). Low frequency (14-16%) of PTCH1 methylation was seen in the asymptomatic exfoliated cervical cells and in the normal epithelium adjacent to the tumor followed by a significant increase in CIN (31%) to stage I/II (57%) and comparable in stage III/IV (58%). The overall alterations (deletion/methylation) of PTCH1 significantly increased from CIN (34%) to stage I/II (70%) and comparable in stage III/IV (69%). Interestingly, in the normal epithelium, methylation of PTCH1 was high in basal/parabasal layers (83%), followed by decrease in the spinous layer (33 %), and showed significant inverse correlation with its expression. Reduced expression of PTCH1 seen in tumors showed a significant association with its alterations (deletion/methylation). The expression pattern of PTCH1 showed an inverse correlation with the nuclear expression of GLI1 in the normal epithelium as well as in the tumors. High nuclear expression of HPV16, E6, and E7 were seen in basal/parabasal layers of the normal epithelium and also in tumors. The PTCH1 alterations (deletion and/or methylation) in tumors and its methylation in adjacent normal epithelium were associated with poor prognosis of patients. Thus, our data suggests that activation of the Hedgehog pathway due to PTCH1 inactivation along with HPV infection is important in CACX development.