Development and validation of a prognostic model for cervical cancer by combination of machine learning and high-throughput sequencing

BACKGROUND

Cervical cancer holds the highest morbidity and mortality rates among female reproductive tract tumors. However, the curative outcomes for patients with persistent, recurrent, or metastatic cervical cancer remain unsatisfactory. There is a lack of comprehensive prognostic indicators for cervical cancer. This study aims to develop a model that evaluates the prognosis of cervical cancer in combination of high-throughput sequencing and various machine learning algorithms.

METHODS

In this study, we combined two single-cell RNA sequencing (scRNA-seq) projects and TCGA data for cervical cancer to obtain shared differentially expressed genes (DEGs). A LASSO regression and several learners were applied for signature feature selection. Six machine learning algorithms including Linear Discriminant Analysis, Naive Bayes, K Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting were utilized to construct a prognostic model for cervical cancer. External validation was conducted using the CGCI-HTMCP-CC dataset, and the accuracy of the model was assessed through ROC curve analysis.

RESULTS

The results demonstrated the successful construction of a prognostic model based on DEGs from bulk- and scRNA-seq data. Ten genes CXCL8, DLC1, GRN, MPLKIP, PRDX1, RUNX1, SNX3, TFRC, UBE2V2, and UQCRC1 were screened by feature selection and applied for model construction. Random Forest exhibited the best performance in predicting the risk of cervical cancer. Patients in the high-risk group presented worse overall survival compared to those in the low-risk group.

CONCLUSION

Conclusively, our model based on DEGs from bulk-seq and scRNA-seq data effectively evaluates the prognosis of cervical cancer and provides valuable insights for comprehensive clinical management.

Integrated analysis of multiple methods reveals characteristics of the immune microenvironment in medulloblastoma

To explore the characteristics of the immune microenvironment (IME) of medulloblastoma (MB) by four methods: flow cytometry (FCM), immunohistochemical (IHC), bulk RNA expression and single cell RNA sequencing (scRNA-seq), we collected the intraoperative specimens of MB, ependymoma (EPN), high-grade glioma (HGG), and low-grade glioma (LGG) to make a cross-cancer comparison. The specimens were subjected to FCM and IHC respectively, and deconvolution from bulk RNA expression data and scRNA-seq analysis were performed in MB from the GEO database. FCM and IHC analysis found that the proportion of lymphocytes (LC) and T cells between MB and other brain tumors were significantly different. The deconvolution of bulk RNA expression data showed that only the proportion of cell types in MCPCOUNTER changed greatly. scRNA-seq found that the proportion of various immune cells in the IME of MB differed between different subtypes. Techniques such as FCM, IHC, bulk RNA expression, and scRNA-seq can sort out different immune cell subsets to a certain extent and quantify their proportions. The four methods have their own strengthens and limitations, but for highly heterogeneous tumor such as MB, integrated analysis of multiple methods is a better choice.

An omics strategy increasingly improves the discovery of genetic loci and genes for seed-coat color formation in soybean

The phenotypic color of seeds is a complex agronomic trait and has economic and biological significance. The genetic control and molecular regulation mechanisms have been extensively studied. Here, we used a multi-omics strategy to explore the color formation in soybean seeds at a big data scale. We identified 13 large quantitative trait loci (QTL) for color with bulk segregating analysis in recombinant inbreeding lines. GWAS analysis of colors and decomposed attributes in 763 germplasms revealed associated SNP sites perfectly falling in five major QTL, suggesting inherited regulation on color during natural selection. Further transcriptomics analysis before and after color accumulation revealed 182 differentially expression genes (DEGs) in the five QTL, including known genes CHS, MYB, and F3'H involved in pigment accumulation. More DEGs with consistently upregulation or downregulation were identified as shared regulatory genes for two or more color formations while some DEGs were only for a specific color formation. For example, five upregulated DEGs in QTL qSC-3 were in flavonoid biosynthesis responsible for black and brown seed. The DEG (Glyma.08G085400) was identified in the purple seed only, which encodes gibberellin 2-beta-dioxygenase in the metabolism of colorful terpenoids. The candidate genes are involved in flavonoid biosynthesis, transcription factor regulation, gibberellin and terpenoid metabolism, photosynthesis, ascorbate and aldarate metabolism, and lipid metabolism. Seven differentially expressed transcription factors were also speculated that may regulate color formation, including a known MYB. The finds expand QTL and gene candidates for color formation, which could guide to breed better cultivars with designed colors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01414-z.

Identification of two immune-related risk score signatures through integrated analysis of multi-omics data in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Immunotherapy has become a major treatment for advanced HCC, but the therapeutic effects remain unsatisfactory. In this study, we constructed an immune cell risk score (ICS) and an immune cell-related gene risk score (ICRGS) for the prognosis prediction of HCC through integrated analysis of bulk and single-cell RNA (scRNA) sequencing data. These two risk score signatures both showed good predictive values in the training and validation cohorts. The potential interactions among these prognostic immune cell types were elucidated by cell-cell communication analysis. The results of enrichment analysis and gene set enrichment analysis (GSEA) of the prognostic genes showed that metabolic-related processes were involved in the immune response of HCC. Furthermore, the results of correlation analyses further confirmed the hub genes that were strongly correlated with immune cells. Finally, potential therapeutic drugs targeting these hub genes were screened by CellMiner based on NCI-60 cell line set. Taken together, two useful models for the prognosis prediction of HCC patients were constructed in this study. The functional differences between the two groups of HCC patients separated by ICS or ICRGS provide fundamental knowledge for finding synergistic therapeutic targets for HCC immunotherapy.

Transcriptional configurations of myeloproliferative neoplasms

Myeloproliferative neoplasms (MPNs) is an umbrella term for several heterogenous diseases, which are characterized by their stem cell origin, clonal hematopoiesis and increase of blood cells of the myeloid lineage. The focus will be on BCR-ABL1 negative MPNs, polycythemia vera (PV), primary myelofibrosis (PMF), essential thrombocythemia (ET). Seminal findings in the field of MPN were driven by genomic analysis, focusing on dissecting genomic changes MPN patients. This led to identification of major MPN driver genes, JAK2, MPL and CALR. Transcriptomic analysis promises to bridge the gap between genetic and phenotypic characterization of each patient's tumor and with the advent of single cell sequencing even for each MPN cancer cell. This review will focus on efforts to mine the bulk transcriptome of MPN patients, including analysis of fusion genes and splicing alterations which can be addressed with RNA-seq technologies. Furthermore, this paper aims to review recent endeavors to elucidate tumor heterogeneity in MPN hematopoietic stem and progenitor cells using single cell technologies. Finally, it will highlight current shortcoming and future applications to advance the field in MPN biology and improve patient diagnostics using RNA-based assays.

Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data

BACKGROUND

Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities.

RESULT

In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data.

CONCLUSIONS

In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

Single-Cell Sequencing of T cell Receptors: A Perspective on the Technological Development and Translational Application

T cells recognize peptides bound to major histocompatibility complex (MHC) class I and class II molecules at the cell surface. This recognition is accomplished by the expression of T cell receptors (TCR) which are required to be diverse and adaptable in order to accommodate the various and vast number of antigens presented on the MHCs. Thus, determining TCR repertoires of effector T cells is necessary to understand the immunological process in responding to cancer progression, infection, and autoimmune development. Furthermore, understanding the TCR repertoires will provide a solid framework to predict and test the antigen which is more critical in autoimmunity. However, it has been a technical challenge to sequence the TCRs and provide a conceptual context in correlation to the vast number of TCR repertoires in the immunological system. The exploding field of single-cell sequencing has changed how the repertoires are being investigated and analyzed. In this review, we focus on the biology of TCRs, TCR signaling and its implication in autoimmunity. We discuss important methods in bulk sequencing of many cells. Lastly, we explore the most pertinent platforms in single-cell sequencing and its application in autoimmunity.

Phylogenetics and biogeography of the dung beetle genus Onthophagus inferred from mitochondrial genomes

Phylogenetic relationships of dung beetles in the tribe Onthophagini, including the species-rich, cosmopolitan genus Onthophagus, were inferred using whole mitochondrial genomes. Data were generated by shotgun sequencing of mixed genomic DNA from >100 individuals on 50% of an Illumina MiSeq flow cell. Genome assembly of the mixed reads produced contigs of 74 (nearly) complete mitogenomes. The final dataset included representatives of Onthophagus from all biogeographic regions, closely related genera of Onthophagini, and the related tribes Onitini and Oniticellini. The analysis defined four major clades of Onthophagini, which was paraphyletic for Oniticellini, with Onitini as sister group to all others. Several (sub)genera considered as members of Onthophagus in the older literature formed separate deep lineages. All New World species of Onthophagus formed a monophyletic group, and the Australian taxa are confined to a single or two closely related clades, one of which forms the sister group of the New World species. Dating the tree by constraining the basal splits with existing calibrations of Scarabaeoidea suggests an origin of Onthophagini sensu lato in the Eocene and a rapid spread from an African ancestral stock into the Oriental region, and secondarily to Australia and the Americas at about 20-24 Mya. The successful assembly of mitogenomes and the well-supported tree obtained from these sequences demonstrates the power of shotgun sequencing from total genomic DNA of species pools as an efficient tool in genus-level phylogenetics.