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

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

Comparative Multi-Omics Analysis Reveals Lignin Accumulation Affects Peanut Pod Size

Pod size is one of the important factors affecting peanut yield. However, the metabolites relating to pod size and their biosynthesis regulatory mechanisms are still unclear. In the present study, two peanut varieties (Tif and Lps) with contrasting pod sizes were used for a comparative metabolome and transcriptome analysis. Developing peanut pods were sampled at 10, 20 and 30 days after pegging (DAP). A total of 720 metabolites were detected, most of which were lipids (20.3%), followed by phenolic acids (17.8%). There were 43, 64 and 99 metabolites identified as differentially accumulated metabolites (DAMs) at 10, 20 and 30 DAP, respectively, and flavonoids were the major DAMs between Tif and Lps at all three growth stages. Multi-omics analysis revealed that DAMs and DEGs (differentially expressed genes) were significantly enriched in the phenylpropanoid biosynthesis (ko00940) pathway, the main pathway of lignin biosynthesis, in each comparison group. The comparisons of the metabolites in the phenylpropanoid biosynthesis pathway accumulating in Tif and Lps at different growth stages revealed that the accumulation of p-coumaryl alcohol (H-monolignol) in Tif was significantly greater than that in Lps at 30 DAP. The differential expression of gene-LOC112771695, which is highly correlated with p-coumaryl alcohol and involved in the biosynthesis of monolignols, between Tif and Lps might explain the differential accumulation of p-coumaryl alcohol. The content of H-lignin in genetically diverse peanut varieties demonstrated that H-lignin content affected peanut pod size. Our findings would provide insights into the metabolic factors influencing peanut pod size and guidance for the genetic improvement of the peanut.

Comprehensive Analysis of the Oncogenic Role of Targeting Protein for Xklp2 (TPX2) in Human Malignancies

Mitosis and spindle assembly require the microtubule-associated protein Xenopus kinesin-like protein 2 (TPX2). Although TPX2 is highly expressed in several malignant tumor forms, little is known about its role in cancer. In this study, we performed the gene set enrichment analysis of TPX2 in 33 types of cancers and an extensive pan-cancer bioinformatic analysis using prognosis, tumor mutational burdens, microsatellite instability, tumor microenvironment, and immune cell infiltration data. According to the differential expression study, TPX2 was found to be overexpressed across all studied cancer types. Based on the survival analysis, increased TPX2 expression was associated with a poor prognosis for most cancers. The TPX2 expression level was confirmed to correlate with the clinical stage, microsatellite instability, and tumor mutational burden across all cancer types. Furthermore, TPX2 expression has been linked to tumor microenvironments and immune cell infiltration, particularly in bladder urothelial carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. Finally, the gene set enrichment analysis implicated TPX2 in the regulation of aminoacyl tRNA biosynthesis, which is the most important tumor cell cycle signaling pathway. This comprehensive pan-cancer analysis shows that TPX2 is a prognostic molecular biomarker for most cancers and suggests its potential as an effective therapeutic target for the treatment of these diseases.

Systematic exploration of the underlying mechanism of gemcitabine resistance in pancreatic adenocarcinoma

Resistance to gemcitabine is the main challenge of chemotherapy for pancreatic ductal adenocarcinoma (PDAC). Hence, the development of a response signature to gemcitabine is essential for precision therapy of PDAC. However, existing quantitative signatures of gemcitabine are susceptible to batch effects and variations in sequencing platforms. Therefore, based on within-sample relative expression ordering of pairwise genes, we developed a transcriptome-based gemcitabine signature consisting of 28 gene pairs (28-GPS) that could predict response to gemcitabine for PDAC at the individual level. The 28-GPS was superior to previous quantitative signatures in terms of classification accuracy and prognostic performance. Resistant samples classified by 28-GPS showed poorer overall survival, higher genomic instability, lower immune infiltration, higher metabolic level and higher-fidelity DNA damage repair compared with sensitive samples. In addition, we found that gemcitabine combined with phosphoinositide 3-kinase (PI3K) inhibitor may be an alternative treatment strategy for PDAC. Single-cell analysis revealed that cancer cells in the same PDAC sample showed both the characteristics of sensitivity and resistance to gemcitabine, and the activation of the TGFβ signalling pathway could promote progression of PDAC. In brief, 28-GPS could robustly determine whether PDAC is resistant or sensitive to gemcitabine, and may be an auxiliary tool for clinical treatment.

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.