Identification of idiopathic pulmonary fibrosis hub genes and exploration of the mechanisms of action of Jinshui Huanxian formula.

Idiopathic pulmonary fibrosis (IPF) is a common and heterogeneous chronic disease, and the mechanism of Jinshui Huanxian formula (JHF) on IPF remains unclear. For a total of 385 lung normal tissue samples from the Gene Expression Omnibus database, 37,777,639 gene pairs were identified through microarray and RNA-seq platforms. Using the individualized differentially expressed gene (DEG) analysis algorithm RankComp (FDR < 0.01), we identified 344 genes as DEGs in at least 95 % (n = 81) of the IPF samples. Of these genes, IGF1, IFNGR1, GLI2, HMGCR, DNM1, KIF4A, and TNFRSF11A were identified as hub genes. These genes were verified using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) in mice with pulmonary fibrosis (PF) and MRC-5 cells, and they were highly effective at classifying IPF samples in the independent dataset GSE134692 (AUC = 0.587-0.788) and mice with PF (AUC = 0.806-1.000). Moreover, JHF ameliorated the pathological changes in mice with PF and significantly reversed the changes in hub gene expression (KIF4A, IFNGR1, and HMGCR). In conclusion, a series of IPF hub genes was identified, and validated in an independent dataset, mice with PF, and MRC-5 cells. Moreover, the abnormal gene expression was normalized by JHF. These findings provide guidance for further exploration of the pathogenesis and treatment of IPF.

Bufei Yishen formula protects the airway epithelial barrier and ameliorates COPD by enhancing autophagy through the Sirt1/AMPK/Foxo3 signaling pathway.

OBJECT: Bufei Yishen formula (BYF), a traditional Chinese medicine alleviates COPD symptoms and suppresses airway epithelial inflammation. In this study, we determined whether BYF protects the airway epithelial barrier from destruction in COPD rats. METHODS: The protective effects of BYF on the airway epithelial barrier were examined in a rat COPD model. BEAS-2B epithelial cells were exposed to cigarette smoke extract (CSE) to determine the effect of BYF on epithelial barrier function. Transcriptomic and network analyses were conducted to identify the protective mechanisms. RESULTS: Oral BYF reduced the severity of COPD in rats by suppressing the decline in lung function, pathological changes, inflammation, and protected airway epithelial barrier function by upregulating apical junction proteins, including occludin (OCLN), zonula occludens (ZO)-1, and E-cadherin (E-cad). BYF treatment reduced epithelial permeability, and increased TEER as well as the apical junction proteins, OCLN, ZO-1, and E-cad in BEAS-2B cells exposed to CSE. Furthermore, 58 compounds identified in BYF were used to predict 421 potential targets. In addition, the expression of 572 differentially expressed genes (DEGs) was identified in CSE-exposed BEAS-2B cells. A network analysis of the 421 targets and 572 DEGs revealed that BYF regulates multiple pathways, of which the Sirt1, AMPK, Foxo3, and autophagy pathways may be the most important with respect to protective mechanisms. Moreover, in vitro experiments confirmed that nobiletin, one of the active compounds in BYF, increased apical junction protein levels, including OCLN, ZO-1, and E-cad. It also increased LC3B and phosphorylated AMPK levels and decreased the phosphorylation of FoxO3a. CONCLUSIONS: BYF protects the airway epithelial barrier in COPD by enhancing autophagy through regulation of the SIRT1/AMPK/FOXO3 signaling pathway.

Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis-related genes and potential therapeutic drugs.

OBJECTIVE: The pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs). METHODS: Using IPF gene-expression data, Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks, hub genes, and competitive endogenous RNA (ceRNA) networks were constructed or identified by Cytoscape. Quantitative polymerase chain reaction (qPCR) experiments in TGF-ß1-induced human fetal lung (HFL) fibroblast cells and a pulmonary fibrosis mouse model verified gene reliability. The SymMap database predicted potential TCMs targeting IPF. The reliability of TCMs was verified in TGF-ß1-induced MRC-5 cells. MATERIALS: Multiple gene-expression profile data of normal lung and IPF tissues were downloaded from the Gene Expression Omnibus database. HFL fibroblast cells and MRC-5 cells were purchased from Wuhan Procell Life Science and Technology Co., Ltd. (Wuhan, China). C57BL/12 mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). RESULTS: In datasets GSE134692 and GSE15197, DEGs were identified using Wilcoxon rank-sum tests (both p < 0.05). Among them, 1885 DEGs were commonly identified, and 87% (1640 genes) had identical dysregulation directions (binomial test, p < 1.00E-16). A PPI network with 1623 nodes and 8159 edges was constructed, and 18 hub genes were identified using the Analyze Network plugin in Cytoscape. Of 18 genes, CAV1, PECAM1, BMP4, VEGFA, FYN, SPP1, and COL1A1 were further validated in the GeneCards database and independent dataset GSE24206. ceRNA networks of VEGFA, SPP1, and COL1A1 were constructed. The genes were verified by qPCR in samples of TGF-ß1-induced HFL fibroblast cells and pulmonary fibrosis mice. Finally, Sea Buckthorn and Gnaphalium Affine were predicted as potential TCMs for IPF. The TCMs were verified by qPCR in TGF-ß1-induced MRC-5 cells. CONCLUSION: This analysis strategy may be useful for elucidating novel mechanisms underlying IPF at the transcriptome level. The identified hub genes may play key roles in IPF pathogenesis and therapy.

Maintenance of airway epithelial barrier integrity via the inhibition of AHR/EGFR activation ameliorates chronic obstructive pulmonary disease using effective-component combination.

BACKGROUND: Airway epithelial barrier dysfunction is highly related to the pathogenesis of chronic obstructive pulmonary disease (COPD). Effective-component combination (ECC) derived from Bufei Yishen formula (BYF) is an effective treatment regimen for patients with COPD and has previously been found to attenuate COPD and airway epithelial inflammation in rats. PURPOSE: To determine the mechanism underlying the protective effects of ECC-BYF against the disruption of the airway epithelial barrier in COPD. METHODS: The protective effects of ECC-BYF on the airway epithelial barrier were investigated in a rat COPD model. BEAS-2B epithelial cells were stimulated with cigarette smoke extract (CSE) to determine the direct effects of ECC-BYF on epithelial barrier function and aryl hydrocarbon receptor (AHR)/ epidermal growth factor receptor (EGFR) signaling. RESULTS: The results revealed that ECC-BYF attenuated COPD in rats and maintained the airway epithelial barrier by upregulating the expression of apical junction proteins, including occludin (OCC), zonula occludens (ZO)-1, and E-cadherin (E-cad). In BEAS-2B cells, ECC-BYF decreased permeability, increased transepithelial electrical resistance, and prevented the decrease in OCC, ZO-1, and E-cad expression induced by CSE exposure. In addition, transcriptomics and network analysis revealed that the protective effects of ECC-BYF may be related to multiple signaling pathways, including ErbB, AHR, and PI3K-Akt-mTOR pathways. ECC-BYF treatment suppressed the protein levels of p-EGFR and p-ERK1/2 and mRNA levels of CYP1A1 in CSE-exposed BEAS-2B cells as well as the protein levels of p-EGFR, p-ERK1/2, and CYP1A1 in the lungs of rats with COPD. In BEAS-2B cells, the AHR agonist FICZ weakened the protective effect of ECC-BYF on the epithelial barrier by suppressing the increase in ZO-1 and OCC expression induced by ECC-BYF and preventing the inhibitory effects of ECC-BYF on EGFR phosphorylation. CONCLUSIONS: This is the first study to demonstrate the protective effect of ECC-BYF on airway epithelial barrier function. The underlying mechanism may be associated with the suppression of the AHR/EGFR pathway to promote apical junction protein adhesion.

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes.

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10-5). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman's rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

TGF-ß-induced CCR8 promoted macrophage transdifferentiation into myofibroblast-like cells.

Background: Idiopathic pulmonary fibrosis (IPF) is an interstitial disease of unknown origin, characterized by tissue fibrosis, for which currently there is no effective treatment. Macrophages, the main immune cells in lung tissue, are involved in the whole process of pulmonary fibrosis. In recent years, intercellular transformation has led to wide spread concern among pulmonary fibrosis researchers. Macrophages with flexible heterogeneity and plasticity participate in different physiological processes in the body. Cell chemokine receptor 8 (CCR8) is expressed in a variety of cells and plays a significant chemotactic role in the induction of cell activation and migration. It can also promote the differentiation of macrophages under certain environmental conditions. The current study is intended to explore the role of CCR8 in macrophage to myofibroblast transdifferentiation (MMT) in IPF. Methods: We conducted experiments using CCR8-specific small interfering RNA (siRNA), an autophagy inhibitor (3-methyladenine, 3-MA), and an agonist (rapamycin) to explore the underlying mechanisms of macrophage transdifferentiation into myofibroblast cells in transforming growth factor-beta (TGF-ß)-induced pulmonary fibrosis. Results: TGF-ß treatment increased the CCR8 protein level in a time- and dose-dependent manner in mouse alveolar macrophages, as well as macrophage transdifferentiation-related markers, including vimentin, collagen 1, and a-SMA, and cell migration. In addition, the levels of autophagy were enhanced in macrophages treated with TGF-ß. We found that 3-MA, an autophagy inhibitor, decreased the expression levels of macrophage transdifferentiation-related markers and attenuated cell migration. Furthermore, the inhibition of CCR8 via CCR8-specific siRNA reduced the levels of autophagy and macrophage transdifferentiation-related markers, and inhibited the cell migration. Enhancing autophagy with rapamycin attenuated the inhibition effect of CCR8-specific siRNA on macrophage migration and the increase in myofibroblast marker proteins. Conclusions: Our findings showed that the macrophages exposed to TGF-ß had the potential to transdifferentiate into myofibroblasts and CCR8 was involved in the process. The effect of CCR8 on TGF-ß-induced macrophage transdifferentiation occurs mainly through autophagy. Targeting CCR8 may be a novel therapeutic strategy for the treatment of IPF.

Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis.

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high morbidity and mortality. The etiology of COPD is complex, and the pathogenesis mechanisms remain unclear. In this study, we used rat and human COPD gene expression data from our laboratory and the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between individuals with COPD and healthy individuals. Then, protein-protein interaction (PPI) networks were constructed, and hub genes were identified. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. A total of 198 DEGs were identified, and a PPI network with 144 nodes and 355 edges was constructed. Twelve hub genes were identified by the cytoHubba plugin in Cytoscape. Of these genes, CCR3, CCL2, COL4A2, VWF, IL1RN, IL2RA, and CCL13 were related to inflammation or immunity, or tissue-specific expression in lung tissue, and their messenger RNA (mRNA) levels were validated by qRT-PCR. COL4A2, VWF, and IL1RN were further verified by the GEO dataset GSE76925, and the ceRNA network was constructed with Cytoscape. These three genes were consistent with COPD rat model data compared with control data, and their dysregulation direction was reversed when the COPD rat model was treated with effective-component compatibility of Bufei Yishen formula III. This bioinformatics analysis strategy may be useful for elucidating novel mechanisms underlying COPD. We pinpointed three key genes that may play a role in COPD pathogenesis and therapy, which deserved to be further studied.

The Anti-Inflammatory Effect of a Combination of Five Compounds From Five Chinese Herbal Medicines Used in the Treatment of COPD.

Effective compound combination (ECC; i.e, 20-S-ginsenoside Rh1, astragaloside, icariin, nobiletin, and paeonol), derived from Chinese herbal medicine, significantly ameliorates chronic obstructive pulmonary disease (COPD) in rats; however, the underlying mechanisms of ECC remain largely unclear. In this study, network pharmacology analysis integrated with experimental validation was used to explore the therapeutic mechanisms of ECC against COPD. ECC targets and COPD genes and targets were identified from multiple databases, and then used for an analysis of protein-protein interaction (PPI) networks, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and biological functioning. BisoGenet was used to comprehensively analyze the hub-network. We validated the therapeutic effect and mechanisms of ECC both in vivo and in vitro. We identified 45 ECC targets, which were mainly related to inflammatory processes, such as the NOD-like and NF-kappa B signaling pathways, hematopoietic cell lineage, Th17 cell differentiation, cellular response to lipopolysaccharide, and interleukin-8 secretion. In addition, 1180 COPD genes and 70 COPD targets were identified as being involved in the biological functions associated with COPD development, such as cytokine-cytokine receptor interaction, the TNF signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway, regulation of lymphocyte proliferation, and positive regulation of leukocyte migration. Integrative analysis of COPD genes and targets and ECC target networks revealed that 54 genes were mainly involved in the inflammatory process, such as IL-17 signaling, NF-kappa B signaling, innate immune response-activating signal transduction, and macrophage cell differentiation. Six targets (AR, ESR1, HNRNPA1, PAPR1, TP53, and VCAM1) contained in the hub-network and their four related compounds were obtained and recognized as the key molecules associated with the effects of ECC. Molecular docking validation demonstrated that four compounds could bind to six targets that interact with COPD genes. Finally, in vivo and in vitro experiments verified that ECC treatment ameliorated the symptoms of COPD in rats by improving their lung function, reducing pathological changes, and suppressing oxidative responses and pro-inflammatory cytokine secretion, while inhibiting inflammation in LPS-induced macrophages, which may be associated with NF-kappa B and MAPK signaling regulation. This study demonstrates the therapeutic mechanisms and effects of ECC on COPD via regulation of the underlying inflammatory process.

Identification of Genes Universally Differentially Expressed in Gastric Cancer.

Owing to the remarkable heterogeneity of gastric cancer (GC), population-level differentially expressed genes (DEGs) identified using case-control comparison cannot indicate the dysregulated frequency of each DEG in GC. In this work, first, the individual-level DEGs were identified for 1,090 GC tissues without paired normal tissues using the RankComp method. Second, we directly compared the gene expression in a cancer tissue to that in paired normal tissue to identify individual-level DEGs among 448 paired cancer-normal gastric tissues. We found 25 DEGs to be dysregulated in more than 90% of 1,090 GC tissues and also in more than 90% of 448 GC tissues with paired normal tissues. The 25 genes were defined as universal DEGs for GC. Then, we measured 24 paired cancer-normal gastric tissues by RNA-seq to validate them further. Among the universal DEGs, 4 upregulated genes (BGN, E2F3, PLAU, and SPP1) and 1 downregulated gene (UBL3) were found to be cancer genes already documented in the COSMIC or F-Census databases. By analyzing protein-protein interaction networks, we found 12 universally upregulated genes, and we found that their 284 direct neighbor genes were significantly enriched with cancer genes and key biological pathways related to cancer, such as the MAPK signaling pathway, cell cycle, and focal adhesion. The 13 universally downregulated genes and 16 direct neighbor genes were also significantly enriched with cancer genes and pathways related to gastric acid secretion. These universal DEGs may be of special importance to GC diagnosis and treatment targets, and they may make it easier to study the molecular mechanisms underlying GC.

Qualitative diagnostic signature for pancreatic ductal adenocarcinoma based on the within-sample relative expression orderings.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of pancreatic cancer, which is one of the most aggressive malignant neoplasms with a 9.3% five-year survival rate. The pathological biopsy is the current golden standard for confirming suspicious lesions of PDAC, but it is not entirely reliable because of the insufficient sampling amount and inaccurate sampling location. Therefore, developing a robust signature to aid the accurate diagnosis of PDAC is critical. METHODS: Based on the within-sample relative expression orderings of gene pairs, we identified a qualitative signature to discriminate both PDAC and adjacent samples from both chronic pancreatitis and normal samples in the training datasets and validated it in other independent datasets produced by different laboratories with different measuring platforms. RESULTS: A six-gene-pair signature was identified in the training data and validated in eight independent datasets. For surgical samples, 96.63% of 356 PDAC tissues, 100% of 11 pancreatitis tissues of non-cancer patients, and 23 of 24 normal pancreatic tissues were correctly classified. Especially, 59 of 60 cancer-adjacent normal tissues of PDAC patients were correctly identified as PDAC. For biopsy samples, all of 11 PDAC biopsy tissues were correctly classified as PDAC. CONCLUSION: The signature can distinguish both PDAC and PDAC-adjacent normal tissues from both chronic pancreatitis and normal tissues of non-cancer patients even when the sampling locations are inaccurate, which can aid the diagnosis of PDAC.

Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature.

Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.

The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome.

The within-sample relative expression orderings (REOs) of genes, which are stable qualitative transcriptional characteristics, can provide abundant information for a disease. Methods based on REO comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. Here, we evaluated the effects of common potential confounding factors, including age, cigarette smoking, sex, and race, on the REOs of gene pairs within normal lung tissues transcriptome. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in nonsmokers' lung tissues are reversed in smokers' lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR <0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism, and other pathways (hypergeometric test, FDR <0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR <0.05) which introduced a few reversal REOs between the two races. In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex, and race.

Identification of population-level differentially expressed genes in one-phenotype data.

MOTIVATION: For some specific tissues, such as the heart and brain, normal controls are difficult to obtain. Thus, studies with only a particular type of disease samples (one phenotype) cannot be analyzed using common methods, such as significance analysis of microarrays, edgeR and limma. The RankComp algorithm, which was mainly developed to identify individual-level differentially expressed genes (DEGs), can be applied to identify population-level DEGs for the one-phenotype data but cannot identify the dysregulation directions of DEGs. RESULTS: Here, we optimized the RankComp algorithm, termed PhenoComp. Compared with RankComp, PhenoComp provided the dysregulation directions of DEGs and had more robust detection power in both simulated and real one-phenotype data. Moreover, using the DEGs detected by common methods as the 'gold standard', the results showed that the DEGs detected by PhenoComp using only one-phenotype data were comparable to those identified by common methods using case-control samples, independent of the measurement platform. PhenoComp also exhibited good performance for weakly differential expression signal data. AVAILABILITY AND IMPLEMENTATION: The PhenoComp algorithm is available on the web at https://github.com/XJJ-student/PhenoComp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy.

For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91-7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26-3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.

A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions.

It is meaningful to assess the risk of cancer incidence among patients with precancerous colorectal lesions. Comparing the within-sample relative expression orderings (REOs) of colorectal cancer patients measured by multiple platforms with that of normal colorectal tissues, a qualitative transcriptional signature consisting of 1,840 gene pairs was identified in the training data. Within an evaluation dataset of 16 active and 18 inactive (remissive) ulcerative colitis subjects, the median incidence risk score of colorectal carcinoma was 0.6402 in active ulcerative colitis subjects, significantly higher than that in remissive subjects (0.3114). Evaluation of two other independent datasets yielded similar results. Moreover, we found that the score significantly positively correlated with the degree of dysplasia in the case of colorectal adenomas. In the merged dataset, the median incidence risk score was 0.9027 among high-grade adenoma samples, significantly higher than that among low-grade adenomas (0.8565). In summary, the developed incidence risk score could well predict the incidence risk of precancerous colorectal lesions and has value in clinical application.

Biased Influences of Low Tumor Purity on Mutation Detection in Cancer.

The non-cancerous components in tumor tissues, e.g., infiltrating stromal cells and immune cells, dilute tumor purity and might confound genomic mutation profile analyses and the identification of pathological biomarkers. It is necessary to systematically evaluate the influence of tumor purity. Here, using public gastric cancer samples from The Cancer Genome Atlas (TCGA), we firstly showed that numbers of mutation, separately called by four algorithms, were significant positively correlated with tumor purities (all p < 0.05, Spearman rank correlation). Similar results were also observed in other nine cancers from TCGA. Notably, the result was further confirmed by six in-house samples from two gastric cancer patients and five in-house samples from two colorectal cancer patients with different tumor purities. Furthermore, the metastasis mechanism of gastric cancer may be incorrectly characterized as numbers of mutation and tumor purities of 248 lymph node metastatic (N + M0) samples were both significantly lower than those of 121 non-metastatic (N0M0) samples (p < 0.05, Wilcoxon rank-sum test). Similar phenomena were also observed that tumor purities could confound the analysis of histological subtypes of cancer and the identification of microsatellite instability status (MSI) in both gastric and colon cancer. Finally, we suggested that the higher tumor purity, such as above 70%, rather than 60%, could be better to meet the requirement of mutation calling. In conclusion, the influence of tumor purity on the genomic mutation profile and pathological analyses should be fully considered in the further study.

Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy.

FOLFOX (5-fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within-sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F-score in predicting pathological response of CRC to FOLFOX. The REO-based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F-score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F-score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression-free survival than the predicted non-responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5-fluorouracil-based signatures. In conclusion, the REO-based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen.

Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer.

The heterogeneity of cancer is a big obstacle for cancer diagnosis and treatment. Prioritizing combinations of driver genes that mutate in most patients of a specific cancer or a subtype of this cancer is a promising way to tackle this problem. Here, we developed an empirical algorithm, named PathMG, to identify common and subtype-specific mutated sub-pathways for a cancer. By analyzing mutation data of 408 samples (Lung-data1) for lung cancer, three sub-pathways each covering at least 90% of samples were identified as the common sub-pathways of lung cancer. These sub-pathways were enriched with mutated cancer genes and drug targets and were validated in two independent datasets (Lung-data2 and Lung-data3). Especially, applying PathMG to analyze two major subtypes of lung cancer, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC), we identified 13 subtype-specific sub-pathways with at least 0.25 mutation frequency difference between LUAD and LSCC samples in Lung-data1, and 12 of the 13 sub-pathways were reproducible in Lung-data2 and Lung-data3. Similar analyses were done for colorectal cancer. Together, PathMG provides us a novel tool to identify potential common and subtype-specific sub-pathways for a cancer, which can provide candidates for cancer diagnoses and sub-pathway targeted treatments.

A qualitative transcriptional signature for the early diagnosis of colorectal cancer.

Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non-CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non-CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection.

Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection. Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples. Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10-03). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 1008, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups. Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.