Transkingdom network reveals bacterial players associated with cervical cancer gene expression program

Cervical cancer is the fourth most common cancer in women worldwide with human papillomavirus (HPV) being the main cause the disease. Chromosomal amplifications have been identified as a source of upregulation for cervical cancer driver genes but cannot fully explain increased expression of immune genes in invasive carcinoma. Insight into additional factors that may tip the balance from immune tolerance of HPV to the elimination of the virus may lead to better diagnosis markers. We investigated whether microbiota affect molecular pathways in cervical carcinogenesis by performing microbiome analysis via sequencing 16S rRNA in tumor biopsies from 121 patients. While we detected a large number of intra-tumor taxa (289 operational taxonomic units (OTUs)), we focused on the 38 most abundantly represented microbes. To search for microbes and host genes potentially involved in the interaction, we reconstructed a transkingdom network by integrating a previously discovered cervical cancer gene expression network with our bacterial co-abundance network and employed bipartite betweenness centrality. The top ranked microbes were represented by the families Bacillaceae, Halobacteriaceae, and Prevotellaceae. While we could not define the first two families to the species level, Prevotellaceae was assigned to Prevotella bivia. By co-culturing a cervical cancer cell line with P. bivia, we confirmed that three out of the ten top predicted genes in the transkingdom network (lysosomal associated membrane protein 3 (LAMP3), STAT1, TAP1), all regulators of immunological pathways, were upregulated by this microorganism. Therefore, we propose that intra-tumor microbiota may contribute to cervical carcinogenesis through the induction of immune response drivers, including the well-known cancer gene LAMP3

Transkingdom network reveals bacterial players associated with cervical cancer gene expression program

Cervical cancer is the fourth most common cancer in women worldwide with human papillomavirus (HPV) being the main cause the disease. Chromosomal amplifications have been identified as a source of upregulation for cervical cancer driver genes but cannot fully explain increased expression of immune genes in invasive carcinoma. Insight into additional factors that may tip the balance from immune tolerance of HPV to the elimination of the virus may lead to better diagnosis markers. We investigated whether microbiota affect molecular pathways in cervical carcinogenesis by performing microbiome analysis via sequencing 16S rRNA in tumor biopsies from 121 patients. While we detected a large number of intra-tumor taxa (289 operational taxonomic units (OTUs)), we focused on the 38 most abundantly represented microbes. To search for microbes and host genes potentially involved in the interaction, we reconstructed a transkingdom network by integrating a previously discovered cervical cancer gene expression network with our bacterial co-abundance network and employed bipartite betweenness centrality. The top ranked microbes were represented by the families Bacillaceae, Halobacteriaceae, and Prevotellaceae. While we could not define the first two families to the species level, Prevotellaceae was assigned to Prevotella bivia. By co-culturing a cervical cancer cell line with P. bivia, we confirmed that three out of the ten top predicted genes in the transkingdom network (lysosomal associated membrane protein 3 (LAMP3), STAT1, TAP1), all regulators of immunological pathways, were upregulated by this microorganism. Therefore, we propose that intra-tumor microbiota may contribute to cervical carcinogenesis through the induction of immune response drivers, including the well-known cancer gene LAMP3

Reference MicroRNAs for RT-qPCR Assays in Cervical Cancer Patients and Their Application to Studies of HPV16 and Hypoxia Biomarkers

MicroRNA (miRNA) expressions in tumor biopsies have shown potential as biomarkers in cervical cancer, but suitable reference RNAs for normalization of reverse transcription quantitative polymerase chain reaction (RT-qPCR) assays in patient cohorts with different clinicopathological characteristics are not available. We aimed to identify the optimal reference miRNAs and apply these to investigate the potential of miR-9-5p as human papilloma virus (HPV) 16 biomarker and miR-210-3p as hypoxia biomarker in cervical cancer. Candidate reference miRNAs were preselected in sequencing data of 90 patients and ranked in a stability analysis by RefFinder. A selection of the most stable miRNAs was evaluated by geNorm and NormFinder analyses of RT-qPCR data of 29 patients. U6 small nuclear RNA (RNU6) was also included in the evaluation. MiR-9-5p and miR-210-3p expression was assessed by RT-qPCR in 45 and 65 patients, respectively. Nine candidates were preselected in the sequencing data after excluding those associated with clinical markers, HPV type, hypoxia status, suboptimal expression levels, and low stability. In RT-qPCR assays, the combination of miR-151-5p, miR-152-3p, and miR-423-3p was identified as the most stable normalization factor across clinical markers, HPV type, and hypoxia status. RNU6 showed poor stability. By applying the optimal reference miRNAs, higher miR-9-5p expression in HPV16- than HPV18-positive tumors and higher miR-210-3p expression in more hypoxic than less hypoxic tumors were found in accordance with the sequencing data. MiR-210-3p was associated with poor outcome by both sequencing and RT-qPCR assays. In conclusion, miR-151-5p, miR-152-3p, and miR-423-3p are suitable reference miRNAs in cervical cancer. MiR-9-5p and miR-210-3p are promising HPV16 and hypoxia biomarkers, respectively

Identification and Validation of Reference Genes for RT-qPCR Studies of Hypoxia in Squamous Cervical Cancer Patients

<div><p>Hypoxia is an adverse factor in cervical cancer, and hypoxia-related gene expression could be a powerful biomarker for identifying the aggressive hypoxic tumors. Reverse transcription quantitative PCR (RT-qPCR) is a valuable method for gene expression studies, but suitable reference genes for data normalization that are independent of hypoxia status and clinical parameters of cervical tumors are lacking. In the present work, we aimed to identify reference genes for RT-qPCR studies of hypoxia in squamous cervical cancer. From 422 candidate reference genes selected from the literature, we used Illumina array-based expression profiles to identify 182 genes not affected by hypoxia in cervical cancer, i.e. genes regulated by hypoxia in eight cervical cancer cell lines or correlating with the hypoxia-associated dynamic contrast-enhanced magnetic resonance imaging parameter A_Brix in 42 patients, were excluded. Among the 182 genes, nine candidates (<i>CHCHD1</i>, <i>GNB2L1</i>, <i>IPO8</i>, <i>LASP1</i>, <i>RPL27A</i>, <i>RPS12</i>, <i>SOD1</i>, <i>SRSF9</i>, <i>TMBIM6</i>) that were not associated with tumor volume, stage, lymph node involvement or disease progression in array data of 150 patients, were selected for further testing by RT-qPCR. geNorm and NormFinder analyses of RT-qPCR data of 74 patients identified <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM6</i> as the optimal set of reference genes, with stable expression both overall and across patient subgroups with different hypoxia status (A_Brix) and clinical parameters. The suitability of the three reference genes were validated in studies of the hypoxia-induced genes <i>DDIT3</i>, <i>ERO1A</i>, and <i>STC2</i>. After normalization, the RT-qPCR data of these genes showed a significant correlation with Illumina expression (P<0.001, n = 74) and A_Brix (P<0.05, n = 32), and the <i>STC2</i> data were associated with clinical outcome, in accordance with the Illumina data. Thus, <i>CHCHD1</i>, <i>SRSF9</i> and <i>TMBIM6</i> seem to be suitable reference genes for studying hypoxia-related gene expression in squamous cervical cancer samples by RT-qPCR. Moreover, <i>STC2</i> is a promising prognostic hypoxia biomarker in cervical cancer.</p></div

Association between gene expression and tumor hypoxia status (A_Brix).

<p>Association between gene expression and tumor hypoxia status (A_Brix).</p

Pre-evaluation of 9 candidate reference genes by RT-qPCR in 10 patients.

<p>(<b>A</b>) Gel electrophoresis of the PCR products for nine candidate reference genes in one patient. Lower (25 bp) and upper (1500 bp) markers are shown in each lane. Gene symbols are indicated. The figure is a composite image where <i>CHCHD1</i> is from a separate image and the ladder from each image is shown. Vertical lines indicate cropping of the image or different images. (<b>B</b>) Box plots of the arithmetic means of duplicate C_q-values for eight candidate reference genes in 10 patients. Boxes indicate the interquartile range (IQR) with median as the black center bar. Extended vertical bars represents 1.5 x IQR below the first quartile and 1.5 x IQR above the third quartile, and circles mark suspected outliers. (<b>C</b>) geNorm analysis of eight candidate reference genes. Average expression stability (M) of the remaining candidates after stepwise removal of the least stable gene is shown. The least stable gene in each step is indicated below. (<b>D</b>) Stability value of each of the eight candidate reference genes from the NormFinder analysis, where a low value indicates more stable expression.</p

Nine candidate reference genes and three hypoxia-induced genes evaluated in this study.

<p>Nine candidate reference genes and three hypoxia-induced genes evaluated in this study.</p

Evaluation of stability across subgroups for 5 candidate reference genes by RT-qPCR in 74 patients.

<p>NormFinder analyses of the stability of five candidate reference genes across patient subgroups. The subgroups assessed were: low (n = 49) and high (n = 25) tumor stage (FIGO 1B-2B vs. 3A-4A), with (n = 32) and without (n = 42) lymph node (LN) involvement at diagnosis, below (n = 36) and above (n = 36) a median tumor volume of 44.6 cm³, with (n = 32) or without (n = 42) treatment recurrence at five years, and different hypoxia status represented by below (n = 16) and above (n = 16) a median A_Brix.</p