Hongdong Li

Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms

The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.

Common DNA methylation alterations of Alzheimer’s disease and aging in peripheral whole blood

Alzheimers disease (AD) is a common aging-related neurodegenerative illness. Recently, many studies have tried to identify AD- or aging-related DNA methylation (DNAm) biomarkers from peripheral whole blood (PWB). However, the origin of PWB biomarkers is still controversial. In this study, by analyzing 2565 DNAm profiles for PWB and brain tissue, we showed that aging-related DNAm CpGs (Age-CpGs) and AD-related DNAm CpGs (AD-CpGs) observable in PWB both mainly reflected DNAm alterations intrinsic in leukocyte subtypes rather than methylation differences introduced by the increased ratio of myeloid to lymphoid cells during aging or AD progression. The PWB Age-CpGs and AD-CpGs significantly overlapped 107 sites (P-value = 2.61×10−12) and 97 had significantly concordant methylation alterations in AD and aging (P-value < 2.2×10−16), which were significantly enriched in nervous system development, neuron differentiation and neurogenesis. More than 60.8% of these 97 concordant sites were found to be significantly correlated with age in normal peripheral CD4+ T cells and CD14+ monocytes as well as in four brain regions, and 44 sites were also significantly differentially methylated in different regions of AD brain tissue. Taken together, the PWB DNAm alterations related to both aging and AD could be exploited for identification of AD biomarkers.

An individualized prognostic signature and multi‑omics distinction for early stage hepatocellular carcinoma patients with surgical resection

Previously reported prognostic signatures for predicting the prognoses of postsurgical hepatocellular carcinoma (HCC) patients are commonly based on predefined risk scores, which are hardly applicable to samples measured by different laboratories. To solve this problem, using gene expression profiles of 170 stage I/II HCC samples, we identified a prognostic signature consisting of 20 gene pairs whose within-sample relative expression orderings (REOs) could robustly predict the disease-free survival and overall survival of HCC patients. This REOs-based prognostic signature was validated in two independent datasets. Functional enrichment analysis showed that the patients with high-risk of recurrence were characterized by the activations of pathways related to cell proliferation and tumor microenvironment, whereas the low-risk patients were characterized by the activations of various metabolism pathways. We further investigated the distinct epigenomic and genomic characteristics of the two prognostic groups using The Cancer Genome Atlas samples with multi-omics data. Epigenetic analysis showed that the transcriptional differences between the two prognostic groups were significantly concordant with DNA methylation alternations. The signaling network analysis identified several key genes (e.g. TP53, MYC) with epigenomic or genomic alternations driving poor prognoses of HCC patients. These results help us understand the multi-omics mechanisms determining the outcomes of HCC patients.

Circumvent the uncertainty in the applications of transcriptional signatures to tumor tissues sampled from different tumor sites

The expression measurements of thousands of genes are correlated with the proportions of tumor epithelial cell (PTEC) in clinical samples. Thus, for a tumor diagnostic or prognostic signature based on a summarization of expression levels of the signature genes, the risk score for a patient may dependent on the tumor tissues sampled from different tumor sites with diverse PTEC for the same patient. Here, we proposed that the within-samples relative expression orderings (REOs) based gene pairs signatures should be insensitive to PTEC variations. Firstly, by analysis of paired tumor epithelial cell and stromal cell microdissected samples from 27 cancer patients, we showed that above 80% of gene pairs had consistent REOs between the two cells, indicating these REOs would be independent of PTEC in cancer tissues. Then, by simulating tumor tissues with different PTEC using each of the 27 paired samples, we showed that about 90% REOs of gene pairs in tumor epithelial cells were maintained in tumor samples even when PTEC decreased to 30%. Especially, the REOs of gene pairs with larger expression differences in tumor epithelial cells tend to be more robust against PTEC variations. Finally, as a case study, we developed a gene pair signature which could robustly distinguish colorectal cancer tissues with various PTEC from normal tissues. We concluded that the REOs-based signatures were robust against PTEC variations.

An individualized prognostic signature for gastric cancer patients treated with 5-Fluorouracil-based chemotherapy and distinct multi-omics characteristics of prognostic groups

5-Fluorouracil (5-FU)-based chemotherapy is currently the first-line treatment for gastric cancer. In this study, using gene expression profiles for a panel of cell lines with drug sensitivity data and two cohorts of patients, we extracted a signature consisting of two gene pairs (KCNE2 and API5, KCNE2 and PRPF3) whose within-sample relative expression orderings (REOs) could robustly predict prognoses of gastric cancer patients treated with 5-FU-based chemotherapy. This REOs-based signature was insensitive to experimental batch effects and could be directly applied to samples measured by different laboratories. Taking this unique advantage of the REOs-based signature, we classified gastric cancer samples of The Cancer Genome Atlas (TCGA) into two prognostic groups with distinct transcriptional characteristics, circumventing the usage of confounded TCGA survival data. We further showed that the two prognostic groups displayed distinct copy number, gene mutation and DNA methylation landscapes using the TCGA multi-omics data. The results provided hints for understanding molecular mechanisms determining prognoses of gastric cancer patients treated with 5-FU-based chemotherapy.

Identifying Reproducible Molecular Biomarkers for Gastric Cancer Metastasis with the Aid of Recurrence Information

To precisely diagnose metastasis state is important for tailoring treatments for gastric cancer patients. However, the routinely employed radiological and pathologic tests for tumour metastasis have considerable high false negative rates, which may retard the identification of reproducible metastasis-related molecular biomarkers for gastric cancer. In this research, using three datasets, we firstly shwed that differentially expressed genes (DEGs) between metastatic tissue samples and non-metastatic tissue samples could hardly be reproducibly detected with a proper statistical control when the metastatic and non-metastatic samples were defined by TNM stage alone. Then, assuming that undetectable micrometastases are the prime cause for recurrence of early stage patients with curative resection, we reclassified all the “non-metastatic” samples as metastatic samples whenever the patients experienced tumour recurrence during follow-up after tumour resection. In this way, we were able to find distinct and reproducible DEGs between the reclassified metastatic and non-metastatic tissue samples and concordantly significant DNA methylation alterations distinguishing metastatic tissues and non-metastatic tissues of gastric cancer. Our analyses suggested that the follow-up recurrence information for patients should be employed in the research of tumour metastasis in order to decrease the confounding effects of false non-metastatic samples with undetected micrometastases.

A simple way to detect disease-associated cellular molecular alterations from mixed-cell blood samples

&NA; Blood is a promising surrogate for solid tissue to investigate disease‐associated molecular biomarkers. However, proportion changes of the constituent cells in the often‐used peripheral whole blood (PWB) or peripheral blood mononuclear cell (PBMC) samples may influence the detection of cell‐specific alterations under disease states. We propose a simple method, Ref‐REO, to detect molecular alterations in leukocytes using the mixed‐cell blood samples. The method is based on the predetermined within‐sample relative expression orderings (REOs) of genes in purified leukocytes of healthy people. Both the simulated and real mixed‐cell blood gene expression profiles were used to evaluate the method. Approximately 99% of the differentially expressed genes (DEGs) detected by Ref‐REO in the simulated mixed‐cell data are owing to the transcriptional alterations in leukocytes rather than the proportion changes of leukocytes. For the real mixed‐cell data, the DEGs detected by Ref‐REO in the PBMCs expression data for systemic lupus erythematosus (SLE) patients overlap significantly with the DEGs detected in the expression data of SLE CD4 + T cells and B cells and they are mainly enriched with mRNA editing and interferon‐associated genes. The detected DEGs in the PWB data for lung carcinoma patients are significantly enriched with coagulation‐associated functional categories that are closely associated with cancer progression. In conclusion, the proposed method is capable of detecting the disease‐associated leukocyte‐specific molecular alterations, using mixed‐cell blood samples, which provides simple, transferable and easy‐to‐use candidates for disease biomarkers.

Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings

Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.

Identification of molecular alterations in leukocytes from gene expression profiles of peripheral whole blood of Alzheimer’s disease

Blood-based test has been considered as a promising way to diagnose and study Alzheimer’s disease (AD). However, the changed proportions of the leukocytes under disease states could confound the aberrant expression signals observed in mixed-cell blood samples. We have previously proposed a method, Ref-REO, to detect the leukocyte specific expression alterations from mixed-cell blood samples. In this study, by applying Ref-REO, we detect 42 and 45 differentially expressed genes (DEGs) between AD and normal peripheral whole blood (PWB) samples in two datasets, respectively. These DEGs are mainly associated with AD-associated functions such as Wnt signaling pathways and mitochondrion dysfunctions. They are also reproducible in AD brain tissue, and tend to interact with the reported AD-associated biomarkers and overlap with targets of AD-associated PWB miRNAs. Moreover, they are closely associated with aging and have severer expression alterations in the younger adults with AD. Finally, diagnostic signatures are constructed from these leukocyte specific alterations, whose area under the curve (AUC) for predicting AD is higher than 0.73 in the two AD PWB datasets. In conclusion, gene expression alterations in leukocytes could be extracted from AD PWB samples, which are closely associated with AD progression, and used as a diagnostic signature of AD.

Shared liver-like transcriptional characteristics in liver metastases and corresponding primary colorectal tumors

Background & Aims: Primary tumors of colorectal carcinoma (CRC) with liver metastasis might gain some liver-specific characteristics to adapt the liver micro-environment. This study aims to reveal potential liver-like transcriptional characteristics associated with the liver metastasis in primary colorectal carcinoma. Methods: Among the genes up-regulated in normal liver tissues versus normal colorectal tissues, we identified “liver-specific” genes whose expression levels ranked among the bottom 10% (“unexpressed”) of all measured genes in both normal colorectal tissues and primary colorectal tumors without metastasis. These liver-specific genes were investigated for their expressions in both the primary tumors and the corresponding liver metastases of seven primary CRC patients with liver metastasis using microdissected samples. Results: Among the 3958 genes detected to be up-regulated in normal liver tissues versus normal colorectal tissues, we identified 12 liver-specific genes and found two of them, ANGPTL3 and CFHR5, were unexpressed in microdissected primary colorectal tumors without metastasis but expressed in both microdissected liver metastases and corresponding primary colorectal tumors (Fishers exact test, P < 0.05). Genes co-expressed with ANGPTL3 and CFHR5 were significantly enriched in metabolism pathways characterizing liver tissues, including “starch and sucrose metabolism” and “drug metabolism-cytochrome P450”. Conclusions: For primary CRC with liver metastasis, both the liver metastases and corresponding primary colorectal tumors may express some liver-specific genes which may help the tumor cells adapt the liver micro-environment.