Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis

Circulating tumor cell clustering modulates RNA splicing and polyadenylation to facilitate metastasis

Circulating tumor cell (CTC) clusters exhibit significantly higher metastatic potential compared to single CTCs. However, the underlying mechanism behind this phenomenon remains unclear, and the role of posttranscriptional RNA regulation in CTC clusters has not been explored. Here, we conducted a comparative analysis of alternative splicing (AS) and alternative polyadenylation (APA) profiles between single CTCs and CTC clusters. We identified 994 and 836 AS events in single CTCs and CTC clusters, respectively, with ∼20% of AS events showing differential regulation between the two cell types. A key event in this differential splicing was observed in SRSF6, which disrupted AS profiles and contributed to the increased malignancy of CTC clusters. Regarding APA, we found a global lengthening of 3' UTRs in CTC clusters compared to single CTCs. This alteration was primarily governed by 14 core APA factors, particularly PPP1CA. The modified APA profiles facilitated the cell cycle progression of CTC clusters and indicated their reduced susceptibility to oxidative stress. Further investigation revealed that the proportion of H2AFY mRNA with long 3' UTR instead of short 3' UTR was higher in CTC clusters than single CTCs. The AU-rich elements (AREs) within the long 3' UTR of H2AFY mRNA enhance mRNA stability and translation activity, resulting in promoting cell proliferation and invasion, which potentially facilitate the establishment and rapid formation of metastatic tumors mediated by CTC clusters. These findings provide new insights into the mechanisms driving CTC cluster metastasis.

Molecular data representation based on gene embeddings for cancer drug response prediction

Cancer drug response prediction is a crucial task in precision medicine, but existing models have limitations in effectively representing molecular profiles of cancer cells. Specifically, when these models represent molecular omics data such as gene expression, they employ a one-hot encoding-based approach, where a fixed gene set is selected for all samples and omics data values are assigned to specific positions in a vector. However, this approach restricts the utilization of embedding-vector-based methods, such as attention-based models, and limits the flexibility of gene selection. To address these issues, our study proposes gene embedding-based fully connected neural networks (GEN) that utilizes gene embedding vectors as input data for cancer drug response prediction. The GEN allows for the use of embedding-vector-based architectures and different gene sets for each sample, providing enhanced flexibility. To validate the efficacy of GEN, we conducted experiments on three cancer drug response datasets. Our results demonstrate that GEN outperforms other recently developed methods in cancer drug prediction tasks and offers improved gene representation capabilities. All source codes are available at https://github.com/DMCB-GIST/GEN/ .

c-Myc inhibits LAPTM5 expression in B-cell lymphomas

Myc is a pivotal protooncogenic transcription factor that contributes to the development of almost all Burkitt's lymphomas and about one-third of diffuse large B-cell lymphomas. How B-cells sustain their uncontrolled proliferation due to high Myc is not yet well defined. Here, we found that Myc trans-represses the expression of murine LAPTM5, a gene coding a lysosome-associated protein, by binding to two E-boxes in the LAPTM5 promoter. While the product of intact mRNA (CDS+3'UTR) of LAPTM5 failed to suppress the growth of B-lymphomas, either the protein coded by coding sequence (CDS) itself or the non-coding 3'-untranslated region (3'UTR) mRNA was able to inhibit the growth of B-lymphomas. Moreover, Myc trans-activated miR-17-3p, which promoted tumor growth. Strikingly, LAPTM5 3'UTR contains 11 miR-17-3p-binding sites through which the LAPTM5 protein synthesis was inhibited. The functional interplay between low LAPTM5 mRNA and high miR-17-3p due to high Myc in B-lymphomas leads to further dampening of tumor-suppressive LAPTM5 protein, which promotes tumor progression. Our results indicate that Myc inhibits LAPTM5 expression in B-lymphoma cells by transcriptional and post-transcriptional modifications.

KCNAB2 overexpression inhibits human non-small-cell lung cancer cell growth in vitro and in vivo

Non-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases. NSCLC patients often have poor prognosis demanding urgent identification of novel biomarkers and potential therapeutic targets. KCNAB2 (regulatory beta subunit2 of voltage-gated potassium channel), encoding aldosterone reductase, plays a pivotal role in regulating potassium channel activity. In this research, we tested the expression of KCNAB2 as well as its potential functions in human NSCLC. Bioinformatics analysis shows that expression of KCNAB2 mRNA is significantly downregulated in human NSCLC, correlating with poor overall survival. In addition, decreased KCNAB2 expression was detected in different NSCLC cell lines and local human NSCLC tissues. Exogenous overexpression of KCNAB2 potently suppressed growth, proliferation and motility of established human NSCLC cells and promoted NSCLC cells apoptosis. In contrast, CRISPR/Cas9-induced KCNAB2 knockout further promoted the malignant biological behaviors of NSCLC cells. Protein chip analysis in the KCNAB2-overexpressed NSCLC cells revealed that KCNAB2 plays a possible role in AKT-mTOR cascade activation. Indeed, AKT-mTOR signaling activation was potently inhibited following KCNAB2 overexpression in NSCLC cells. It was however augmented by KCNAB2 knockout. In vivo, the growth of subcutaneous KCNAB2-overexpressed A549 xenografts was significantly inhibited. Collectively, KCNAB2 could be a novel effective gene for prognosis prediction of NSCLC. Targeting KCNAB2 may lead to the development of advanced therapies.

Histone Variants and Their Chaperones in Hematological Malignancies

Epigenetic regulation occurs on the level of compacting DNA into chromatin. The functional unit of chromatin is the nucleosome, which consists of DNA wrapped around a core of histone proteins. While canonical histone proteins are incorporated into chromatin through a replication-coupled process, structural variants of histones, commonly named histone variants, are deposited into chromatin in a replication-independent manner. Specific chaperones and chromatin remodelers mediate the locus-specific deposition of histone variants. Although histone variants comprise one of the least understood layers of epigenetic regulation, it has been proposed that they play an essential role in directly regulating gene expression in health and disease. Here, we review the emerging evidence suggesting that histone variants have a role at different stages of hematopoiesis, with a particular focus on the histone variants H2A, H3, and H1. Moreover, we discuss the current knowledge on how the dysregulation of histone variants can contribute to hematopoietic malignancies.

The Ion Channel Gene KCNAB2 Is Associated with Poor Prognosis and Loss of Immune Infiltration in Lung Adenocarcinoma

The malignancy with the greatest global mortality rate is lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype. The evidence demonstrated that voltage-gated potassium channel subunit beta-2 (KCNAB2) significantly participated in the initiation of colorectal cancer and its progression. However, the biological function of KCNAB2 in LUAD and its effect on the tumor immune microenvironment are still unknown. In this study, we found that the expression of KCNAB2 in tissues of patients with LUAD was markedly downregulated, and its downregulation was linked to accelerated cancer growth and poor clinical outcomes. In addition, low KCNAB2 expression was correlated with a deficiency in immune infiltration. The mechanism behind this issue might be that KCNAB2 influenced the immunological process such that the directed migration of immune cells was affected. Furthermore, overexpression of KCNAB2 in cell lines promoted the expression of CCL2, CCL3, CCL4, CCL18, CXCL9, CXCL10, and CXCL12, which are necessary for the recruitment of immune cells. In conclusion, KCNAB2 may play a key function in immune infiltration and can be exploited as a predictive biomarker for evaluating prognosis and a possible immunotherapeutic target.

Identification of T cell-related biomarkers for breast cancer based on weighted gene co-expression network analysis

Breast cancer is the most frequent malignancy worldwide, with immunotherapy and targeted therapy being key strategies to improving the prognosis. We downloaded mRNA expression dataset of breast cancer from The Cancer Genome Atlas (TCGA) database, and divided preprocessed genes into 12 modules based on gene expression profile by weighted gene co-expression network analysis (WGCNA). The StromalScore, ImmuneScore and ESTIMATEScore of samples were assessed. The Kaplan-Meier curve showed that ImmuneScore was notably correlated with breast cancer patient's prognosis. By analyzing the connectivity between module eigengenes and clinical traits, the gene module closely related to ImmuneScore was obtained. Further, through intramodular gene connectivity and protein-protein interaction network topology analysis of module genes, hub genes (HLA-E, HLA-DPB1 and HLA-DRB1) in immune-related module were screened out. Finally, bioinformatics analysis displayed that HLA-DPB1 and HLA-DRB1 were notably overexpressed and HLA-E was underexpressed in breast cancer tissues. TIMER database analysis showed that three hub gene levels were significantly correlated with infiltration levels of CD8+ T cells and CD4+ T cells. Meanwhile, Pearson correlation analysis revealed positive correlation between three hub genes and those of immune checkpoint genes (LAG3, PD-1, PD-L1). Additionally, prognosis could be effectively evaluated by HLA-DPB1 and HLA-DRB1 levels, and differentially activated signalling pathways between high- and low-expression groups of HLA-E and HLA-DPB1 were obtained by gene set enrichment analysis. To conclude, this study identified three T cell-related biomarkers for breast cancer based on TCGA-BRCA dataset, and the screened genes could provide references for breast cancer immunotherapy.

Spine‑specific downregulation of LAPTM5 expression promotes the progression and spinal metastasis of estrogen receptor‑positive breast cancer by activating glutamine‑dependent mTOR signaling

Estrogen receptor-positive (ER⁺) breast cancer (BC) is a malignancy that is prone to metastasis to the spine, which is difficult to treat and often results in poor prognosis. However, the mechanism underlying the tumorigenesis and spinal metastasis of ER⁺ BC remains unclear. Lysosomal protein transmembrane 5 (LAPTM5) has been reported as a tumor suppressor in several types of cancer, but its role in ER⁺ BC has not been described. Here, by analyzing a gene sequencing dataset and ER⁺ BC tissues, tumor-adjacent normal tissues and spinal metastatic tissues from patients and mouse models, we found that LAPTM5 expression is negatively related to the progression and spinal metastasis of ER⁺ BC. Subsequently, in vitro experiments demonstrated that downregulation of LAPTM5 expression promoted the proliferation, migration, and chemoresistance of ER⁺ BC cells by activating glutamine-dependent mTOR signaling. A high level of CX3CL1 could inhibit LAPTM5 expression, explaining how ER⁺ BC metastasized to the spine. Thus, we found that LAPTM5 functions as a tumor suppressor in ER⁺ BC and that the CX3CL/CX3CR1/LAPTM5/glutamine axis mediates the spinal metastasis of ER⁺ BC. This axis may be a promising therapeutic target for ER⁺ BC.

Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis

Current international prognostic index is widely questioned on the risk stratification of peripheral T‐cell lymphoma and does not accurately predict the outcome for patients. We postulated that multiple mRNAs could combine into a model to improve risk stratification and helping clinicians make treatment decisions. In this study, the gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co‐expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs, and then built a mRNA signature using a LASSO Cox regression model and validated the prognostic accuracy of it. Finally, a nomogram was constructed and the performance was assessed. A total of 799 WGCNA‐selected mRNAs in black module were identified, and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low‐risk group and high‐risk group (training set:hazard ratio [HR] 4.3, 95% CI 2.4‐7.4, P < .0001; internal testing set:hazard ratio [HR] 2.4, 95% CI 1.2‐4.8, P < .01; external testing set:hazard ratio [HR] 2.3, 95% CI 1.10‐4.7, P = .02). Furthermore, multivariate regression demonstrated that the signature was an independently prognostic factor. Moreover, the nomogram which combined the mRNA signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. The signature is a reliable prognostic tool for patients with PTCLs, and it has the potential for clinicians to implement personalized therapeutic regimen for patients with PTCLs.