Incorporating biological information in sparse principal component analysis with application to genomic data

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

PGC-1α regulates the interplay between oxidative stress, senescence and autophagy in the ageing retina important in age-related macular degeneration

We previously showed that mice with knockout in the peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) gene encoding the PGC-1α protein, and nuclear factor erythroid 2 like 2 (NFE2L2) gene, exhibited some features of the age-related macular degeneration (AMD) phenotype. To further explore the mechanism behind the involvement of PGC-1α in AMD pathogenesis we used young (3-month) and old (12-month) mice with knockout in the PPARGC1A gene and age-matched wild-type (WT) animals. An immunohistochemical analysis showed age-dependent different expression of markers of oxidative stress defence, senescence and autophagy in the retinal pigment epithelium of KO animals as compared with their WT counterparts. Multivariate inference testing showed that senescence and autophagy proteins had the greatest impact on the discrimination between KO and WT 3-month animals, but proteins of antioxidant defence also contributed to that discrimination. A bioinformatic analysis showed that PGC-1α might coordinate the interplay between genes encoding proteins involved in antioxidant defence, senescence and autophagy in the ageing retina. These data support importance of PGC-1α in AMD pathogenesis and confirm the utility of mice with PGC-1α knockout as an animal model to study AMD pathogenesis.

Immunological characteristics of immunogenic cell death genes and malignant progression driving roles of TLR4 in anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma (ATC) was a rare malignancy featured with the weak immunotherapeutic response. So far, disorders of immunogenic cell death genes (ICDGs) were identified as the driving factors in cancer progression, while their roles in ATC remained poorly clear. Datasets analysis identified that most ICDGs were high expressed in ATC, while DE-ICDGs were located in module c1_112, which was mainly enriched in Toll-like receptor signalings. Subsequently, the ICD score was established to classify ATC samples into the high and low ICD score groups, and function analysis indicated that high ICD score was associated with the immune characteristics. The high ICD score group had higher proportions of specific immune and stromal cells, as well as increased expression of immune checkpoints. Additionally, TLR4, ENTPD1, LY96, CASP1 and PDIA3 were identified as the dynamic signature in the malignant progression of ATC. Notably, TLR4 was significantly upregulated in ATC tissues, associated with poor prognosis. Silence of TLR4 inhibited the proliferation, metastasis and clone formation of ATC cells. Eventually, silence of TLR4 synergistically enhanced paclitaxel-induced proliferation inhibition, apoptosis, CALR exposure and release of ATP. Our findings highlighted that the aberrant expression of TLR4 drove the malignant progression of ATC, which contributed to our understanding of the roles of ICDGs in ATC.

Identification and validation of fatty acid metabolism-related lncRNA signatures as a novel prognostic model for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a main subtype of renal cancer, and advanced ccRCC frequently has poor prognosis. Many studies have found that lipid metabolism influences tumor development and treatment. This study was to examine the prognostic and functional significance of genes associated with lipid metabolism in individuals with ccRCC. Using the database TCGA, differentially expressed genes (DEGs) associated with fatty acid metabolism (FAM) were identified. Prognostic risk score models for genes related to FAM were created using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Our findings demonstrate that the prognosis of patients with ccRCC correlate highly with the profiles of FAM-related lncRNAs (AC009166.1, LINC00605, LINC01615, HOXA-AS2, AC103706.1, AC009686.2, AL590094.1, AC093278.2). The prognostic signature can serve as an independent predictive predictor for patients with ccRCC. The predictive signature's diagnostic effectiveness was superior to individual clinicopathological factors. Between the low- and high-risk groups, immunity research revealed a startling difference in terms of cells, function, and checkpoint scores. Chemotherapeutic medications such lapatinib, AZD8055, and WIKI4 had better outcomes for patients in the high-risk group. Overall, the predictive signature can help with clinical selection of immunotherapeutic regimens and chemotherapeutic drugs, improving prognosis prediction for ccRCC patients.

A new CCCH-type zinc finger-related lncRNA signature predicts the prognosis of clear cell renal cell carcinoma patients

Background: Clear cell renal cell carcinoma (ccRCC) is the main component of renal cell carcinoma (RCC), and advanced ccRCC frequently indicates a poor prognosis. The significance of the CCCH-type zinc finger (CTZF) gene in cancer has been increasingly demonstrated during the past few years. According to studies, targeted radical therapy for cancer treatment may be a revolutionary therapeutic approach. Both lncRNAs and CCCH-type zinc finger genes are essential in ccRCC. However, the predictive role of long non-coding RNA (lncRNA) associated with the CCCH-type zinc finger gene in ccRCC needs further elucidation. This study aims to predict patient prognosis and investigate the immunological profile of ccRCC patients using CCCH-type zinc finger-associated lncRNAs (CTZFLs).

Methods: From the Cancer Genome Atlas database, RNA-seq and corresponding clinical and prognostic data of ccRCC patients were downloaded. Univariate and multivariate Cox regression analyses were conducted to acquire CTZFLs for constructing prediction models. The risk model was verified using receiver operating characteristic curve analysis. The Kaplan-Meier method was used to analyze the overall survival (OS) of high-risk and low-risk groups. Multivariate Cox and stratified analyses were used to assess the prognostic value of the predictive feature in the entire cohort and different subgroups. In addition, the relationship between risk scores, immunological status, and treatment response was studied.

Results: We constructed a signature consisting of eight CTZFLs (LINC02100, AC002451.1, DBH-AS1, AC105105.3, AL357140.2, LINC00460, DLGAP1-AS2, AL162377.1). The results demonstrated that the prognosis of ccRCC patients was independently predicted by CTZFLs signature and that the prognosis of high-risk groups was poorer than that of the lower group. CTZFLs markers had the highest diagnostic adequacy compared to single clinicopathologic factors, and their AUC (area under the receiver operating characteristic curve) was 0.806. The overall survival of high-risk groups was shorter than that of low-risk groups when patients were divided into groups based on several clinicopathologic factors. There were substantial differences in immunological function, immune cell score, and immune checkpoint expression between high- and low-risk groups. Additionally, Four agents, including ABT737, WIKI4, afuresertib, and GNE 317, were more sensitive in the high-risk group.

Conclusion: The Eight-CTZFLs prognostic signature may be a helpful prognostic indicator and may help with medication selection for clear cell renal cell carcinoma.

Identification of a Four Cancer Stem Cell-Related Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival of Pancreatic Adenocarcinoma

BACKGROUND

Cancer stem cells (CSCs) are now being considered as the initial component in the development of pancreatic adenocarcinoma (PAAD). Our aim was to develop a CSCrelated signature to assess the prognosis of PAAD patients for the optimization of treatment.

METHODS

Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue in the Cancer Genome Atlas (TCGA) were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the CSC-related gene sets. Then, univariate, Lasso Cox regression analyses and multivariate Cox regression were applied to construct a prognostic signature using the CSC-related genes. Its prognostic performance was validated in TCGA and ICGC cohorts. Furthermore, Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in PAAD, and a prognostic nomogram was established.

RESULTS

The Kaplan-Meier analysis, ROC curve and C-index indicated the good performance of the CSC-related signature at predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression revealed that the CSC-related signature was an independent prognostic factor in PAAD. The nomogram was superior to the risk model and AJCC stage in predicting OS. In terms of mutation and tumor immunity, patients in the high-risk group had higher tumor mutation burden (TMB) scores than patients in the low-risk group, and the immune score and the ESTIMATE score were significantly lower in the high-risk group. Moreover, according to the results of principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA), the low-risk and high-risk groups displayed different stemness statuses based on the risk model.

CONCLUSION

Our study identified four CSC-related gene signatures and established a prognostic nomogram that reliably predicts OS in PAAD. The findings may support new ideas for screening therapeutic targets to inhibit stem characteristics and the development of PAAD.

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).

Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.

Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.

Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

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The Immune-related gene pairs (IRGPs) pronostic signature for LGG is correlated with immune cells infiltration.

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WGCNA presented a gene set correlating with immune cells infiltration and genes co-expression relationships were visualized.

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The nomogram constrcted by three IRGPs and clinical factors is a novel tailored tool for individual-level prediction.

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.

Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components

The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.

Identification and validation of stemness-related lncRNA prognostic signature for breast cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) are emerging as crucial contributors to the development of breast cancer and are involved in the stemness regulation of breast cancer stem cells (BCSCs). LncRNAs are closely associated with the prognosis of breast cancer patients. It is critical to identify BCSC-related lncRNAs with prognostic value in breast cancer.

METHODS

A co-expression network of BCSC-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA) was constructed. Univariate and multivariate Cox proportional hazards analyses were used to identify a stemness risk model with prognostic value. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation were conducted to analyze the risk model.

RESULTS

In this study, BCSC-related lncRNAs in breast cancer were identified. We evaluated the prognostic value of these BCSC-related lncRNAs and eventually obtained a prognostic risk model consisting of 12 BCSC-related lncRNAs (Z68871.1, LINC00578, AC097639.1, AP003119.3, AP001207.3, LINC00668, AL122010.1, AC245297.3, LINC01871, AP000851.2, AC022509.2 and SEMA3B-AS1). The risk model was further verified as a novel independent prognostic factor for breast cancer patients based on the calculated risk score. Moreover, based on the risk model, the low- risk and high-risk groups displayed different stemness statuses.

CONCLUSIONS

These findings suggested that the 12 BCSC-related lncRNA signature might be a promising prognostic factor for breast cancer and can promote the management of BCSC-related therapy in clinical practice.

Bayesian generalized biclustering analysis via adaptive structured shrinkage

Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses such as linear regression and multivariate analysis. In this article, we propose a novel Bayesian biclustering method that can handle multiple data types including Gaussian, Binomial, and Negative Binomial. In addition, our method uses a Bayesian adaptive structured shrinkage prior that enables feature selection guided by existing biological information. Our simulation studies and application to multi-omics datasets demonstrate robust and superior performance of the proposed method, compared to other existing biclustering methods.

Knowledge-Guided Statistical Learning Methods for Analysis of High-Dimensional -Omics Data in Precision Oncology

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of complex diseases such as cancer at an unprecedented scale and in multiple dimensions. However, a number of analytical challenges complicate analysis of high-dimensional -omics data. One is the growing recognition that complex diseases such as cancer are multifactorial and may be attributed to harmful changes on multiple -omics levels and on the pathway level. When individual genes in an important pathway have relatively weak signals, it can be challenging to detect them on their own, but the aggregated signal in the pathway can be considerably stronger and hence easier to detect with the same sample size. To address these challenges, there is a growing body of literature on knowledge-guided statistical learning methods for analysis of high-dimensional -omics data that can incorporate biological knowledge such as functional genomics and functional proteomics. These methods have been shown to improve predication and classification accuracy and yield biologically more interpretable results compared with statistical learning methods that do not use biological knowledge. In this review, we survey current knowledge-guided statistical learning methods, including both supervised learning and unsupervised learning, and their applications to precision oncology, and we discuss future research directions.

A Predictive Model Assessing Genetic Susceptibility Risk at Workplace

(1) Background: The study of susceptibility biomarkers in the immigrant workforce integrated into the social tissue of European host countries is always a challenge, due to high individual heterogeneity and the admixing of different ethnicities in the same workplace. These workers having distinct cultural backgrounds, beliefs, diets, and habits, as well as a poor knowledge of the foreign language, may feel reluctant to donate their biological specimens for the biomonitoring research studies. (2) Methods: A model predicting ethnicity-specific susceptibility based on principal component analysis has been conceived, using the genotype frequency of the investigated populations available in publicly accessible databases. (3) Results: Correlations among ethnicities and between ethnic and polymorphic genes have been found, and low/high-risk profiles have been identified as valuable susceptibility biomarkers. (4) Conclusions: In the absence of workers’ consent or access to blood genotyping, ethnicity represents a good indicator of the subject’s genotype. This model, associating ethnicity-specific genotype frequency with the susceptibility biomarkers involved in the metabolism of toxicants, may replace genotyping, ensuring the necessary safety and health conditions of workers assigned to hazardous jobs.