Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.

Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.

Identification of differential DNA methylation alterations of ovarian cancer in peripheral whole blood based on within-sample relative methylation orderings.

Leukocyte cell proportion changes affect the detection of cancer-associated aberrant DNA methylation alterations in peripheral blood samples. We aimed to detect cellular DNA methylation changes in ovarian cancer (OVC) blood samples avoiding the above-mentioned cell-composition effects. Based on the within-sample relative methylation orderings (RMOs) of CpG loci in leukocyte subtypes, we developed the Ref-RMO method to detect aberrant methylation alterations from OVC blood samples. Stable CpG pairs with consistent RMOs in different leukocyte subtypes were determined, more than 99% of which retained their RMO patterns in peripheral whole blood (PWB) in independent datasets. Based on the stable CpG pairs, significantly reversed CpG pairs were detected from OVC PWB samples, which were relative to clinical information such as age, subtype, grade, stage, or CA125 level. Results showed 439 CpG loci were determined to be significant differential DNA methylations between OVC and healthy blood samples. They were mainly enriched in KEGG pathways, such as cytokine-cytokine receptor interaction, apoptosis, proteoglycans in cancer, and immune-associated Gene Ontology terms. STRING analysis showed that they tended to have functional interactions with cancer-associated genes recorded in the COSMIC database. Leukocyte cellular differential DNA methylations could be identified by the proposed RMO-based method from OVC PWB samples, which were cancer-associated aberrant signals against cell-composition effects.

Integrative Pan-Cancer Analysis Reveals Decreased Melatonergic Gene Expression in Carcinogenesis and RORA as a Prognostic Marker for Hepatocellular Carcinoma.

BACKGROUND: Melatonin has been shown to play a protective role in the development and progression of cancer. However, the relationship between alterations in the melatonergic microenvironment and cancer development has remained unclear. METHODS: We performed a comprehensive investigation on 12 melatonergic genes and their relevance to cancer occurrence, progression and survival by integrating multi-omics data from microarray analysis and RNA sequencing across 11 cancer types. Specifically, the 12 melatonergic genes that we investigated, which reflect the melatonergic microenvironment, included three membrane receptor genes, three nuclear receptor genes, two intracellular receptor genes, one synthetic gene, and three metabolic genes. RESULTS: Widely coherent underexpression of nuclear receptor genes, intracellular receptor genes, and metabolic genes was observed in cancerous samples from multiple cancer types compared to that in normal samples. Furthermore, genomic and/or epigenetic alterations partially contributed to these abnormal expression patterns in cancerous samples. Moreover, the majority of melatonergic genes had significant prognostic effects in predicting overall survival. Nevertheless, few corresponding alterations in expression were observed during cancer progression, and alterations in expression patterns varied greatly across cancer types. However, the association of melatonergic genes with one specific cancer type, hepatocellular carcinoma, identified RORA as a tumor suppressor and a prognostic marker for patients with hepatocellular carcinoma. CONCLUSIONS: Overall, our study revealed decreased melatonergic gene expression in various cancers, which may help to better elucidate the relationship between melatonin and cancer development. Taken together, our findings highlight the potential prognostic significance of melatonergic genes in various cancers.

Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA.

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA-seq) is very challenging for non-research-oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow-up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non-research-oriented samples which were formalin-fixed paraffin-embedded (FFPE) colorectal carcinoma samples (CRC) using RNA-seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO-based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post-surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non-research-oriented clinical samples, compared to that of two published concise REO-based signatures. In conclusion, REO-based signatures with relatively more gene pairs could be robustly applied to non-research-oriented CRC samples.

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.

BACKGROUND: The qualitative transcriptional characteristics, the within-sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy. METHODS: Gene pairs with REOs significantly correlated with the biochemical recurrence-free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within-sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy. RESULTS: A signature consisting of 74 gene pairs, named 74-GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74-GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan-Meier survival analysis showed that the average BFS of the low-risk group was significantly better than that of the high-risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups. CONCLUSIONS: The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74-GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.

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.