Early stage lung cancer detection is the first step toward successful clinical therapy and increased patient survival. Clinicians monitor cancer progression by profiling tumor cell proteins in the blood plasma of afflicted patients. Blood plasma, however, is a difficult cancer protein assessment medium because it is rich in albumins and heterogeneous protein species. We report herein a method to detect the proteins released into the circulatory system by tumor cells. Initially we analyzed the protein components in the conditioned medium (CM) of lung cancer primary cell or organ cultures and in the adjacent normal bronchus using one-dimensional PAGE and nano-ESI-MS/MS. We identified 299 proteins involved in key cellular process such as cell growth, organogenesis, and signal transduction. We selected 13 interesting proteins from this list and analyzed them in 628 blood plasma samples using ELISA. We detected 11 of these 13 proteins in the plasma of lung cancer patients and non-patient controls. Our results showed that plasma matrix metalloproteinase 1 levels were elevated significantly in late stage lung cancer patients and that the plasma levels of 14-3-3 σ, β, and η in the lung cancer patients were significantly lower than those in the control subjects. To our knowledge, this is the first time that fascin, ezrin, CD98, annexin A4, 14-3-3 σ, 14-3-3 β, and 14-3-3 η proteins have been detected in human plasma by ELISA. The preliminary results showed that a combination of CD98, fascin, polymeric immunoglobulin receptor/secretory component and 14-3-3 η had a higher sensitivity and specificity than any single marker. In conclusion, we report a method to detect proteins released into blood by lung cancer. This pilot approach may lead to the identification of novel protein markers in blood and provide a new method of identifying tumor biomarker profiles for guiding both early detection and therapy of human cancer.

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

Structural maintenance of chromosome 4 (SMC4) is a core subunit of condensin complexes that mainly contributes to chromosome condensation and segregation. Our previous study demonstrated that the gene expression profile during lung development is of great values for the study of lung cancer. In this study, we identified SMC4 through co-expression network analysis and clique percolation clustering using genes that constant changes during four stages of lung development. Gene ontology and KEGG pathway enrichment analysis demonstrated that SMC4 is closely related to cell cycle, cell adhesion and RNA processing in lung development and carcinogenesis. Moreover, SMC4 is overexpressed in lung adenocarcinoma tissues and acts as an independent prognostic factor. SMC4 knockdown significantly inhibits the proliferation and invasion of A549 cells. Furthermore, we found that SMC4 interacts with DDX46 (DEAD-box helicase 46). In conclusion, the pivotal role of SMC4 in lung development and carcinogenesis suggests that genes with a similar expression pattern to SMC4 in lung development may also contribute to lung cancer progression. The identification of genes that are essentially involved in development through a comparative study between development and cancer may be a practical strategy for discovering potential biomarkers and illuminating the mechanisms of carcinogenesis.

Genome-wide expression profiling during development provides useful information to uncover the potential molecular mechanisms of development in mammals. Recent studies have revealed that a subset of lncRNAs can regulate major biological processes during development. Here, we sequenced four tissues, including brain, colon, liver and lung, using RNA-seq across three developmental stages (early, middle and late stage), and then constructed genome-wide expression profiles during macaque development. In each tissue, we identified developmental time-specific lncRNA and mRNA clusters that displayed diverse expression alteration patterns, including a gradual increase, a gradual decrease, or a reversal of expression. These lncRNAs showed more specific functional associations with their corresponding tissues relative to the developmental time-specific mRNAs. Furthermore, we identified 20 spatiotemporal-specific co-modules including 101 lncRNAs and 609 mRNAs distributed at different developmental stages in different tissues. Our findings suggested that lncRNAs could play critical roles in the development of macaques through close cooperation with mRNAs. Finally, we predicted the functions of the spatiotemporal-specific lncRNAs by their spatiotemporal cooperation with mRNAs and further validated our findings using gene knockdown data of mouse. Our study reveals the spatiotemporal characteristics of lncRNAs and provides a functional map of the spatiotemporal-specific lncRNAs during the development of macaques.

Long non-coding RNAs (lncRNAs) constitute an important layer of chromatin regulation that contributes to various biological processes and diseases. By interacting with chromatin, many lncRNAs can regulate that state of chromatin by recruiting chromatin-modifying complexes and thus control large-scale gene expression programs. However, the available information on interactions between lncRNAs and chromatin is hidden in a large amount of dispersed literature and has not been extensively collected. We established the LnChrom database, a manually curated resource of experimentally validated lncRNA–chromatin interactions. The current release of LnChrom includes 382 743 interactions in human and mouse. We also manually collected detailed metadata for each interaction pair, including those of chromatin modifying factors, epigenetic marks and disease associations. LnChrom provides a user-friendly interface to facilitate browsing, searching and retrieving of lncRNA–chromatin interaction data. Additionally, a large amount of multi-omics data was integrated into LnChrom to aid in characterizing the effects of lncRNA–chromatin interactions on epigenetic modifications and transcriptional expression. We believe that LnChrom is a timely and valuable resource that can greatly motivate mechanistic research into lncRNAs. Database URL: http://biocc.hrbmu.edu.cn/LnChrom/