KIF20B Promotes Cell Proliferation and May Be a Potential Therapeutic Target in Pancreatic Cancer

Evaluation of genetic response of mesenchymal stem cells to nanosecond pulsed electric fields by whole transcriptome sequencing

BACKGROUND

Mesenchymal stem cells (MSCs) modulated by various exogenous signals have been applied extensively in regenerative medicine research. Notably, nanosecond pulsed electric fields (nsPEFs), characterized by short duration and high strength, significantly influence cell phenotypes and regulate MSCs differentiation via multiple pathways. Consequently, we used transcriptomics to study changes in messenger RNA (mRNA), long noncoding RNA (lncRNA), microRNA (miRNA), and circular RNA expression during nsPEFs application.

AIM

To explore gene expression profiles and potential transcriptional regulatory mechanisms in MSCs pretreated with nsPEFs.

METHODS

The impact of nsPEFs on the MSCs transcriptome was investigated through whole transcriptome sequencing. MSCs were pretreated with 5-pulse nsPEFs (100 ns at 10 kV/cm, 1 Hz), followed by total RNA isolation. Each transcript was normalized by fragments per kilobase per million. Fold change and difference significance were applied to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate gene functions, complemented by quantitative polymerase chain reaction verification.

RESULTS

In total, 263 DEGs were discovered, with 92 upregulated and 171 downregulated. DEGs were predominantly enriched in epithelial cell proliferation, osteoblast differentiation, mesenchymal cell differentiation, nuclear division, and wound healing. Regarding cellular components, DEGs are primarily involved in condensed chromosome, chromosomal region, actin cytoskeleton, and kinetochore. From aspect of molecular functions, DEGs are mainly involved in glycosaminoglycan binding, integrin binding, nuclear steroid receptor activity, cytoskeletal motor activity, and steroid binding. Quantitative real-time polymerase chain reaction confirmed targeted transcript regulation.

CONCLUSION

Our systematic investigation of the wide-ranging transcriptional pattern modulated by nsPEFs revealed the differential expression of 263 mRNAs, 2 miRNAs, and 65 lncRNAs. Our study demonstrates that nsPEFs may affect stem cells through several signaling pathways, which are involved in vesicular transport, calcium ion transport, cytoskeleton, and cell differentiation.

Cancer testis antigens: Emerging therapeutic targets leveraging genomic instability in cancer

Cancer care has witnessed remarkable progress in recent decades, with a wide array of targeted therapies and immune-based interventions being added to the traditional treatment options such as surgery, chemotherapy, and radiotherapy. However, despite these advancements, the challenge of achieving high tumor specificity while minimizing adverse side effects continues to dictate the benefit-risk balance of cancer therapy, guiding clinical decision making. As such, the targeting of cancer testis antigens (CTAs) offers exciting new opportunities for therapeutic intervention of cancer since they display highly tumor specific expression patterns, natural immunogenicity and play pivotal roles in various biological processes that are critical for tumor cellular fitness. In this review, we delve deeper into how CTAs contribute to the regulation and maintenance of genomic integrity in cancer, and how these mechanisms can be exploited to specifically target and eradicate tumor cells. We review the current clinical trials targeting aforementioned CTAs, highlight promising pre-clinical data and discuss current challenges and future perspectives for future development of CTA-based strategies that exploit tumor genomic instability.

Exploring KRAS-mutant pancreatic ductal adenocarcinoma: a model validation study

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has the highest mortality rate among all solid tumors. Tumorigenesis is promoted by the oncogene KRAS, and KRAS mutations are prevalent in patients with PDAC. Therefore, a comprehensive understanding of the interactions between KRAS mutations and PDAC may expediate the development of therapeutic strategies for reversing the progression of malignant tumors. Our study aims at establishing and validating a prediction model of KRAS mutations in patients with PDAC based on survival analysis and mRNA expression.

Methods

A total of 184 and 412 patients with PDAC from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC), respectively, were included in the study.

Results

After tumor mutation profile and copy number variation (CNV) analyses, we established and validated a prediction model of KRAS mutations, based on survival analysis and mRNA expression, that contained seven genes: CSTF2, FAF2, KIF20B, AKR1A1, APOM, KRT6C, and CD70. We confirmed that the model has a good predictive ability for the prognosis of overall survival (OS) in patients with KRAS-mutated PDAC. Then, we analyzed differential biological pathways, especially the ferroptosis pathway, through principal component analysis, pathway enrichment analysis, Gene Ontology (GO) enrichment analysis, and gene set enrichment analysis (GSEA), with which patients were classified into low- or high-risk groups. Pathway enrichment results revealed enrichment in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450, and viral protein interaction with cytokine and cytokine receptor pathways. Most of the enriched pathways are metabolic pathways predominantly enriched by downregulated genes, suggesting numerous downregulated metabolic pathways in the high-risk group. Subsequent tumor immune infiltration analysis indicated that neutrophil infiltration, resting CD4 memory T cells, and resting natural killer (NK) cells correlated with the risk score. After verifying that the seven gene expression levels in different KRAS-mutated pancreatic cancer cell lines were similar to that in the model, we screened potential drugs related to the risk score.

Discussion

This study established, analyzed, and validated a model for predicting the prognosis of PDAC based on risk stratification according to KRAS mutations, and identified differential pathways and highly effective drugs.

Integrating cell cycle score for precise risk stratification in ovarian cancer

Background: Ovarian cancer (OC) is a highly heterogeneous disease, of which the mesenchymal subtype has the worst prognosis, is the most aggressive, and has the highest drug resistance. The cell cycle pathway plays a vital role in ovarian cancer development and progression. We aimed to screen the key cell cycle genes that regulated the mesenchymal subtype and construct a robust signature for ovarian cancer risk stratification.

Methods: Network inference was conducted by integrating the differentially expressed cell cycle signature genes and target genes between the mesenchymal and non-mesenchymal subtypes of ovarian cancer and identifying the dominant cell cycle signature genes.

Results: Network analysis revealed that two cell cycle signature genes (POLA2 and KIF20B) predominantly regulated the mesenchymal modalities of OC and used to construct a prognostic model, termed the Cell Cycle Prognostic Signature of Ovarian Cancer (CCPOC). The CCPOC-high patients showed an unfavorable prognosis in the GSE26712 cohort, consistent with the results in the seven public validation cohorts and one independent internal cohort (BL-OC cohort, qRT-PCR, n = 51). Functional analysis, drug-sensitive analysis, and survival analysis showed that CCPOC-low patients were related to strengthened tumor immunogenicity and sensitive to the anti-PD-1/PD-L1 response rate in pan-cancer (r = −0.47, OC excluded), which indicated that CCPOC-low patients may be more sensitive to anti-PD-1/PD-L1.

Conclusion: We constructed and validated a subtype-specific, cell cycle-based prognostic signature for ovarian cancer, which has great potential for predicting the response of anti-PD-1/PD-L1.

Construction of a Novel Prognostic Model in Lung Adenocarcinoma Based on 7-Methylguanosine-Related Gene Signatures

Increasing evidence has implicated the modification of 7-methylguanosine (m7G), a type of RNA modification, in tumor progression. However, no comprehensive analysis to date has summarized the predicted role of m7G-related gene signatures in lung adenocarcinoma (LUAD). Herein, we aimed to develop a novel prognostic model in LUAD based on m7G-related gene signatures. The LUAD transcriptome profiling data and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus datasets. After screening, we first obtained 29 m7G-related genes, most of which were upregulated in tumor tissues and negatively associated with overall survival (OS). According to the expression similarity of m7G-related genes, the combined samples from the TCGA-LUAD and GSE68465 datasets were further classified as two clusters that exhibit distinct OS rates and genetic heterogeneity. Then, we constructed a novel prognostic model involving four genes by using 130 differentially expressed genes among the two clusters. The combined samples were randomly divided into a training cohort and an internal validation cohort in a 1:1 ratio, and the GSE72094 dataset was used as an external validation cohort. The samples were divided into high- and low-risk groups. We demonstrated that a higher risk score was an independent negative prognostic factor and predicted poor OS. A nomogram was further constructed to better predict the survival of LUAD patients. Functional enrichment analyses indicated that cell cycle and DNA replication-related biological processes and pathways were enriched in the high-risk group. More importantly, the low-risk group had greater infiltration and enrichment of most immune cells, as well as higher ESTIMATE, immune, and stromal scores. In addition, the high-risk group had a lower TIDE score and higher expressions of most immune checkpoint-related genes. We finally noticed that patients in the high-risk group were more sensitive to chemotherapeutic agents commonly used in LUAD. In conclusion, we herein summarized for the first time the alterations and prognostic role of m7G-related genes in LUAD and then constructed a prognostic model based on m7G-related gene signatures that could accurately and stably predict survival and guide individualized treatment decision-making in LUAD patients.