Cancer-Risk Module Identification and Module-Based Disease Risk Evaluation: A Case Study on Lung Cancer

<div><p>Gene expression profiles have drawn broad attention in deciphering the pathogenesis of human cancers. Cancer-related gene modules could be identified in co-expression networks and be applied to facilitate cancer research and clinical diagnosis. In this paper, a new method was proposed to identify lung cancer-risk modules and evaluate the module-based disease risks of samples. The results showed that thirty one cancer-risk modules were closely related to the lung cancer genes at the functional level and interactional level, indicating that these modules and genes might synergistically lead to the occurrence of lung cancer. Our method was proved to have good robustness by evaluating the disease risk of samples in eight cancer expression profiles (four for lung cancer and four for other cancers), and had better performance than the WGCNA method. This method could provide assistance to the diagnosis and treatment of cancers and a new clue for explaining cancer mechanisms.</p></div

The lung cancer risk of each sample in GSE7670 by the WGCNA method.

<p>The lung cancer risk of each sample in GSE7670 by the WGCNA method.</p

The robustness of our method and comparison with the WGCNA method.

<p><b>a</b>) X-axis is samples. Y-axis is the lung cancer risk score of individual samples using our method, and it is ranked from the smallest to the largest. Blue represents GSE10072; green represents GSE21933; red represents GSE27262; and brown represents GSE4079. Full lines represent lung cancer samples; and dashed lines represent normal samples. The different experiment data sets have different numbers of the normal samples and the disease samples. In order to show the disease risk of every sample in four expression profiles intuitively, all samples of each expression profiles are distributed uniformly throughout x-axis. <b>b</b>) The figure is plotted the same way as a). The lung cancer risk of each sample is evaluated by the WGCNA method. <b>c</b>) Receiver operator characteristic curve using our method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7a</a>). The areas under curve provided at lower right of each diagram. <b>d</b>) Receiver operator characteristic curve using the WGCNA method for the four lung cancer expression profiles (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092395#pone-0092395-g007" target="_blank">Figure 7b</a>).</p

The number of the tumor samples and the normal samples in the expression profiles.

<p>The number of the tumor samples and the normal samples in the expression profiles.</p

Tuning Hsf1 levels drives distinct fungal morphogenetic programs with depletion impairing Hsp90 function and overexpression expanding the target space

<div><p>The capacity to respond to temperature fluctuations is critical for microorganisms to survive within mammalian hosts, and temperature modulates virulence traits of diverse pathogens. One key temperature-dependent virulence trait of the fungal pathogen <i>Candida albicans</i> is its ability to transition from yeast to filamentous growth, which is induced by environmental cues at host physiological temperature. A key regulator of temperature-dependent morphogenesis is the molecular chaperone Hsp90, which has complex functional relationships with the transcription factor Hsf1. Although Hsf1 controls global transcriptional remodeling in response to heat shock, its impact on morphogenesis remains unknown. Here, we establish an intriguing paradigm whereby overexpression or depletion of <i>C</i>. <i>albicans HSF1</i> induces morphogenesis in the absence of external cues. <i>HSF1</i> depletion compromises Hsp90 function, thereby driving filamentation. <i>HSF1</i> overexpression does not impact Hsp90 function, but rather induces a dose-dependent expansion of Hsf1 direct targets that drives overexpression of positive regulators of filamentation, including Brg1 and Ume6, thereby bypassing the requirement for elevated temperature during morphogenesis. This work provides new insight into Hsf1-mediated environmentally contingent transcriptional control, implicates Hsf1 in regulation of a key virulence trait, and highlights fascinating biology whereby either overexpression or depletion of a single cellular regulator induces a profound developmental transition.</p></div

Lung cancer-risk modules.

<p>Risk is modules category, ID indicate the identifier of cancer-risk modules, size is the module scale, namely the number of genes in the module, genes is the genes in the modules and the genes which were marked * were DE-genes, M_risk is the cancer risk of modules, p-value is significance p value of random randomized test.</p

Co-expression Level and GO Semantic Similarity.

<p>Purple point means observations, red line indicates the curve fitting, the dotted curve represents the first order tangent.</p

Receiver operator characteristic curve for expression profiles of liver cancer (GSE14520), colon cancer (GSE15781), breast cancer (GSE20437), and prostate cancer (GSE26126).

<p>Receiver operator characteristic curve for expression profiles of liver cancer (GSE14520), colon cancer (GSE15781), breast cancer (GSE20437), and prostate cancer (GSE26126).</p

Z-test.

<p>Where <i>μ</i> means the average expression value of all genes in module1 for the tumor sample s1; e11 is the expression value of g1 in module1 for s1, so do others; means the average expression value of all genes for all normal samples; σ is the standard deviation of all normal samples.</p

The relationship network of cancer-risk modules and lung cancer genes.

<p>The circles indicate cancer-risk modules, and the proportion of orange parts indicates cancer risk (<i>M_risk</i>). The disease-causing genes is represented by red triangles. Edges' colors indicate the relationships, purple represents for the protein-protein interaction, green for function sharing, and red for both functional and interaction relationship.</p