In Silico Integration of Transcriptome and Interactome Predicts an ETP-ALL-Specific Transcriptional Footprint that Decodes its Developmental Propensity

Novel hematopoietic progenitor kinase 1 inhibitor KHK-6 enhances T-cell activation

Inhibiting the functional role of negative regulators in immune cells is an effective approach for developing immunotherapies. The serine/threonine kinase hematopoietic progenitor kinase 1 (HPK1) involved in the T-cell receptor signaling pathway attenuates T-cell activation by inducing the degradation of SLP-76 through its phosphorylation at Ser-376, reducing the immune response. Interestingly, several studies have shown that the genetic ablation or pharmacological inhibition of HPK1 kinase activity improves the immune response to cancers by enhancing T-cell activation and cytokine production; therefore, HPK1 could be a promising druggable target for T-cell-based cancer immunotherapy. To increase the immune response against cancer cells, we designed and synthesized KHK-6 and evaluated its cellular activity to inhibit HPK1 and enhance T-cell activation. KHK-6 inhibited HPK1 kinase activity with an IC50 value of 20 nM and CD3/CD28-induced phosphorylation of SLP-76 at Ser-376 Moreover, KHK-6 significantly enhanced CD3/CD28-induced production of cytokines; proportion of CD4+ and CD8+ T cells that expressed CD69, CD25, and HLA-DR markers; and T-cell-mediated killing activity of SKOV3 and A549 cells. In conclusion, KHK-6 is a novel ATP-competitive HPK1 inhibitor that blocks the phosphorylation of HPK1 downstream of SLP-76, enhancing the functional activation of T cells. In summary, our study showed the usefulness of KHK-6 in the drug discovery for the HPK1-inhibiting immunotherapy.

The prognostic significance of ubiquitination-related genes in multiple myeloma by bioinformatics analysis

BACKGROUND

Immunoregulatory drugs regulate the ubiquitin-proteasome system, which is the main treatment for multiple myeloma (MM) at present. In this study, bioinformatics analysis was used to construct the risk model and evaluate the prognostic value of ubiquitination-related genes in MM.

METHODS AND RESULTS

The data on ubiquitination-related genes and MM samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The consistent cluster analysis and ESTIMATE algorithm were used to create distinct clusters. The MM prognostic risk model was constructed through single-factor and multiple-factor analysis. The ROC curve was plotted to compare the survival difference between high- and low-risk groups. The nomogram was used to validate the predictive capability of the risk model. A total of 87 ubiquitination-related genes were obtained, with 47 genes showing high expression in the MM group. According to the consistent cluster analysis, 4 clusters were determined. The immune infiltration, survival, and prognosis differed significantly among the 4 clusters. The tumor purity was higher in clusters 1 and 3 than in clusters 2 and 4, while the immune score and stromal score were lower in clusters 1 and 3. The proportion of B cells memory, plasma cells, and T cells CD4 naïve was the lowest in cluster 4. The model genes KLHL24, HERC6, USP3, TNIP1, and CISH were highly expressed in the high-risk group. AICAr and BMS.754,807 exhibited higher drug sensitivity in the low-risk group, whereas Bleomycin showed higher drug sensitivity in the high-risk group. The nomogram of the risk model demonstrated good efficacy in predicting the survival of MM patients using TCGA and GEO datasets.

CONCLUSIONS

The risk model constructed by ubiquitination-related genes can be effectively used to predict the prognosis of MM patients. KLHL24, HERC6, USP3, TNIP1, and CISH genes in MM warrant further investigation as therapeutic targets and to combat drug resistance.

The potential role of RNA sequencing in diagnosing unexplained insensitivity to conventional chemotherapy in pediatric patients with B-cell acute lymphoblastic leukemia

Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is a highly heterogeneous disease. According to large-scale RNA sequencing (RNA-seq) data, B-ALL patients can be divided into more than 10 subgroups. However, many genomic defects associated with resistance mechanisms have not yet been identified. As an individual clinical tool for molecular diagnostic risk classification, RNA-seq and gene expression pattern-based therapy could be potential upcoming strategies. In this study, we retrospectively analyzed the RNA-seq gene expression profiles of 45 children whose molecular diagnostic classifications were inconsistent with the response to chemotherapy. The relationship between the transcriptome and chemotherapy response was analyzed. Fusion gene identification was conducted for the included patients who did not have known high-risk associated fusion genes or gene mutations. The most frequently detected fusion gene pair in the high-risk group was the DHRSX duplication, which is a novel finding. Fusions involving ABL1, LMNB2, NFATC1, PAX5, and TTYH3 at onset were more frequently detected in the high-risk group, while fusions involving LFNG, TTYH3, and NFATC1 were frequently detected in the relapse group. According to the pathways involved, the underlying drug resistance mechanism is related to DNA methylation, autophagy, and protein metabolism. Overall, the implementation of an RNA-seq diagnostic system will identify activated markers associated with chemotherapy response, and guide future treatment adjustments.

Circulating-tumour DNA methylation of HAND1 gene: a promising biomarker in early detection of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the significant global health concerns with an increase in cases. Regular screening tests are crucial for early detection as it is often asymptomatic in the initial stages. Liquid biopsies, a non-invasive approach that examines biomarkers in biofluids, offer a promising future in diagnosing and screening cancer. Circulating-tumour DNA (ctDNA) is the genetic material in biofluids released into the circulatory system by cells. ctDNA is a promising marker for monitoring patients since cancer cells display distinct DNA methylation patterns compared to normal cells. The potential of our research to contribute to early detection and improved patient outcomes is significant.

AIMS

The primary objective of this research project was to explore the HAND1 methylation levels in plasma ctDNA as a potential biomarker for diagnosing CRC and evaluate the methylation level of the well-established gene SPET9 to compare it with the methylation level of HAND1.

MATERIALS AND METHODS

Plasma samples were collected from 30 CRC patients and 15 healthy individuals, with CRC samples obtained pre-treatment. ctDNA was extracted and treated with bisulfite for methylation status assessment. Quantitative methylation-specific PCR (qMS-PCR) was performed for HAND1 and SEPT9, using β-actin (ACTB gene) as a reference. The study aims to evaluate the potential of these genes as diagnostic biomarkers for CRC, contributing to early detection and improved patient outcomes.

RESULTS

Our study yielded significant results: 90% of CRC patients (27 out of 30) had hypermethylation in the SEPT9 gene, and 83% (25 out of 30) exhibited hypermethylation in the HAND1 gene. The methylation levels of both genes were significantly higher in CRC patients than in healthy donors. These findings underscore the potential of SEPT9 and HAND1 methylation as promising biomarkers for diagnosing CRC, potentially leading to early detection and improved patient outcomes.

CONCLUSION

These findings highlight the potential of SEPT9 and HAND1 methylation as promising biomarkers for diagnosing CRC. However, further research and validation studies are needed to confirm these findings and to explore their clinical utility in CRC diagnosis and management.

The RNA sequencing results revealed the expression of different genes and signaling pathways during chemotherapy resistance in peripheral T-cell lymphoma

BACKGROUND

Peripheral T-cell lymphoma (PTCL) is a subtype of non-Hodgkin's lymphoma that occurs primarily at extranodal sites and is commonly treated using chemotherapy and radiotherapy. PTCL is more malignant than other lymphoid tumors, resulting in a poor prognosis.The 5-year recurrence rate remains high, and there is a lack of standard treatment for patients with relapse-resistant disease. However, the molecular mechanisms underlying the resistance of peripheral T-cell lymphoma cells to chemotherapeutic drugs, as well as identifying strategies to overcome drug resistance remains unclear. In this study, we aimed to identify pivotal genes and signaling pathways associated with chemotherapy resistance in PTCL.

METHODS

In this study, a total of 5 healthy controls and 7 clinical patients were enrolled; 4 patients were classified as chemotherapy sensitive, and 3 patients were classified as chemotherapy resistant. Peripheral blood samples were collected from each participant, and total RNA was extracted from the white blood cells. RNA sequencing was conducted on the Illumina HiSeq platform to obtain comprehensive gene expression profiles. Subsequently, the expression patterns of the DEGs associated with the most enriched signaling pathways, with a special focus on cancer-related genes, were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in peripheral TCL patients.

RESULTS

RNA sequencing (RNA-seq) analysis revealed 4063 differentially expressed genes (DEGs) in peripheral T-cell lymphoma specimens from patients with chemotherapy resistance, of which 1128 were upregulated and 2935 were downregulated. Subsequent quantitative gene expression analysis confirmed a differential expression pattern in all the libraries, with 9 downregulated genes and 10 upregulated genes validated through quantitative real-time PCR in 6 clinical specimens from patients with chemotherapy resistance. KEGG pathway analysis revealed significant alterations in several pathways, with 6 downregulated pathways and 9 upregulated pathways enriched in the DEGs. Notably, the TNF signaling pathway, which is extensively regulated, was among the pathways that exhibited significant changes. These findings suggest that DEGs and the TNF signaling pathway may play crucial roles in chemotherapy resistance in peripheral T-cell lymphoma.

CONCLUSION

Our study revealed that the expression of specific genes, including TNFRSF1B, TRADD2, and MAP3K7, may play an important role in chemotherapy resistance in peripheral T-cell lymphoma. Moreover, we identified the downregulation of the TNF signaling pathway, a crucial pathway involved in cell survival, death, and differentiation, as a potential contributor to the development of chemotherapy resistance in peripheral T-cell lymphoma. These findings provide valuable insights into the molecular mechanisms underlying chemotherapy resistance and highlight potential targets for overcoming treatment resistance in this challenging disease.

Unlocking potential biomarkers bridging coronary atherosclerosis and pyrimidine metabolism-associated genes through an integrated bioinformatics and machine learning approach

BACKGROUND

This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS.

METHODS

A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets.

RESULTS

This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results.

CONCLUSION

In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

Bioinformatics-based analysis of the relationship between disulfidptosis and prognosis and treatment response in pancreatic cancer

Tumor formation is closely associated with disulfidptosis, a new form of cell death induced by disulfide stress-induced. The exact mechanism of action of disulfidptosis in pancreatic cancer (PCa) is not clear. This study analyzed the impact of disulfidptosis-related genes (DRGs) on the prognosis of PCa and identified clusters of DRGs, and based on this, a risk score (RS) signature was developed to assess the impact of RS on the prognosis, immune and chemotherapeutic response of PCa patients. Based on transcriptomic data and clinical information from PCa tissue and normal pancreatic tissue samples obtained from the TCGA and GTEx databases, differentially expressed and differentially surviving DRGs in PCa were identified from among 15 DRGs. Two DRGs clusters were identified by consensus clustering by merging the PCa samples in the GSE183795 dataset. Analysis of DRGs clusters about the PCa tumor microenvironment and differential analysis to obtain differential genes between the two DRG clusters. Patients were then randomized into the training and testing sets, and a prognostic prediction signature associated with disulfidptosis was constructed in the training set. Then all samples were divided into high-disulfidptosis-risk (HDR) and low-disulfidptosis-risk (LDR) subgroups based on the RS calculated from the signature. The predictive efficacy of the signature was assessed by survival analysis, nomograms, correlation analysis of clinicopathological characteristics, and the receiver operating characteristic (ROC) curves. To assess differences between different risk subgroups in immune cell infiltration, expression of immune checkpoint molecules, somatic gene mutations, and effectiveness of immunotherapy and chemotherapy. The GSE57495 dataset was used as external validation, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression levels of DRGs. A total of 12 DRGs with differential expression and prognosis in PCa were identified, based on which a risk-prognosis signature containing five differentially expressed genes (DEGs) was developed. The signature was a good predictor and an independent risk factor. The nomogram and calibration curve shows the signature's excellent clinical applicability. Functional enrichment analysis showed that RS was associated with tumor and immune-related pathways. RS was strongly associated with the tumor microenvironment, and analysis of response to immunotherapy and chemotherapy suggests that the signature can be used to assess the sensitivity of treatments. External validation further demonstrated the model's efficacy in predicting the prognosis of PCa patients, with RT-qPCR and immunohistochemical maps visualizing the expression of each gene in PCa cell lines and the tissue. Our study is the first to apply the subtyping model of disulfidptosis to PCa and construct a signature based on the disulfidptosis subtype, which can provide an accurate assessment of prognosis, immunotherapy, and chemotherapy response in PCa patients, providing new targets and directions for the prognosis and treatment of PCa.