StemSC: A Cross-dataset Human Stemness Index for Single-cell Samples

Background: Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these stemness indexes showed low correlations with differentiation time and the limitation to identify the high-stemness cells across datasets. Methods: Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time in all five datasets. Then, we used 13 RNA-seq datasets from both the bulk and single-cell ESC samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.Results: We validated the StemSC by its higher correlations with differentiation time in eight normal datasets and its higher correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. By using the StemSC, we can recognize the tissue-specific stem genes and automatically construct the cell differentiation trajectories. StemSC also could provide the same threshold to identify high-stemness cells across datasets. Results showed that the tumor cells with high-stemness had fewer interactions with anti-tumor immune cells. Besides, the immunotherapy-treated patients with high-stemness had worse survival than those with low-stemness. Conclusions: All above results showed StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.

Identification and Characterization of Metastasis-Associated Individualized Gene Expression Signature in Osteosarcoma

Background: Osteosarcoma (OS) patients with surgical resection still relapse with poor prognosis due to the inability to detect distant metastasis. It’s essential to identify metastasis-related biomarkers for OS. Methods: Here, a computational pipeline follow relative expression orderings (REOs) using gene expression was constructed in metastases and non-metastases OS patients. Results: 138 metastasis-associated gene pair signatures (MGPSs) were identified follow two independent datasets. A metastases-specific co-expressed MGPS network was constructed for extracting biomarker for clinical application. MGPS such as MYL5 and RPL27A showed strong positive correlation (Cor =0.75, P <0.001). There were thirteen prognostic MGPSs in above network. These prognostic MGPSs could become as a specific classifier to distinguish metastases and non-metastases OS patients. MGPSs were associated with cancer metastasis-related functions. Drug and MGPS network could provide some drug candidates for treatment of OS. Conclusions: Collectively, the roles of the MGPSs in OS were elucidated, which could be beneficial for understanding OS pathogenesis and treatment.

Identification and characterization of metastasis-associated individualized gene expression signature in osteosarcoma

Introduction: Osteosarcoma (OS) patients with complete surgical resection still relapse with poor prognosis. Part of this is due to the inability to accurately detect distant metastasis. Thus it’s enssential to identify metastasis-related biomarkers for OS.Methods: In present study, a computational pipeline based on relative expression orderings (REOs) using gene expression proles was constructed in metastases and non-metastases OS patients. Results: 138 metastasis-associated gene pair signature (MGPS) were identified follow two independent datasets. In order to further extract metastasis-associated biomarker for clinical application, a metastases-specific co-expressed MGPS network was constructed and analyzed. MGPS such as MYL5 and RPL27A showed strong positive correlation (Cor =0.75, P <0.001) in metastatic OS patients. There were thirteen MGPSs in above network were associated with prognosis. These prognostic MGPSs could become as a specific classifier to distinguish metastases and non-metastases OS patients. Functional analysis showed MGPSs were associated with cancer metastasis-related functions. Drug and MGPS network could provide some drug candidates for treatment of OS. Conclusions: Collectively, the roles of the MGPSs in OS were elucidated, which could be beneficial for understanding OS pathogenesis and treatment.

An absolute human stemness index associated with oncogenic dedifferentiation

The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values—the values with certain range—these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications.

A Qualitative Analysis Based on Relative Expression Orderings Identifies Transcriptional Subgroups for Alzheimer’s Disease

Background: Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease. However, few studies have investigated the heterogeneous gene expression patterns in AD. Objective and Methods: We examined the gene expression patterns in four brain regions of AD based on the within-sample relative expression orderings (REOs). Gene pairs with significantly reversed REOs in AD samples compared to non-AD controls were identified for each brain region using Fisher’s exact test, and filtered according to their transcriptional differences between AD samples. Subgroups of AD were classified by cluster analysis. Results: REO-based gene expression profiling analyses revealed that transcriptional differences, as well as distinct disease subsets, existed within AD patients. For each brain region, two main subgroups were classified: one subgroup reported differentially expressed genes overlapped with the age-related genes, and the other might relate to neuroinflammation. Conclusion: AD transcriptional subgroups might help understand the underlying pathogenesis of AD, and lend support to a personalized approach to AD management.

A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer

Background Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples. Results Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS. Conclusions The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.

A Qualitative Transcriptional Signature for Predicting Microsatellite Instability Status of Right-sided Colon Cancer

Background: Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples. Results: Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS. Conclusions: The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.