CBX5 loss drives EGFR inhibitor resistance and results in therapeutically actionable vulnerabilities in lung cancer

Therapeutic advances of targeting receptor tyrosine kinases in cancer

Receptor tyrosine kinases (RTKs), a category of transmembrane receptors, have gained significant clinical attention in oncology due to their central role in cancer pathogenesis. Genetic alterations, including mutations, amplifications, and overexpression of certain RTKs, are critical in creating environments conducive to tumor development. Following their discovery, extensive research has revealed how RTK dysregulation contributes to oncogenesis, with many cancer subtypes showing dependency on aberrant RTK signaling for their proliferation, survival and progression. These findings paved the way for targeted therapies that aim to inhibit crucial biological pathways in cancer. As a result, RTKs have emerged as primary targets in anticancer therapeutic development. Over the past two decades, this has led to the synthesis and clinical validation of numerous small molecule tyrosine kinase inhibitors (TKIs), now effectively utilized in treating various cancer types. In this manuscript we aim to provide a comprehensive understanding of the RTKs in the context of cancer. We explored the various alterations and overexpression of specific receptors across different malignancies, with special attention dedicated to the examination of current RTK inhibitors, highlighting their role as potential targeted therapies. By integrating the latest research findings and clinical evidence, we seek to elucidate the pivotal role of RTKs in cancer biology and the therapeutic efficacy of RTK inhibition with promising treatment outcomes.

Advances in prognostic models for osteosarcoma risk

The risk prognosis model is a statistical model that uses a set of features to predict whether an individual will develop a specific disease or clinical outcome. It can be used in clinical practice to stratify disease severity and assess risk or prognosis. With the advancement of large-scale second-generation sequencing technology, along Prognosis models for osteosarcoma are increasingly being developed as large-scale second-generation sequencing technology advances and clinical and biological data becomes more abundant. This expansion greatly increases the number of prognostic models and candidate genes suitable for clinical use. This article will present the predictive effects and reliability of various prognosis models, serving as a reference for their evaluation and application.

Downregulation of PTPRT elevates the expression of survivin and promotes the proliferation, migration, and invasion of lung adenocarcinoma

BACKGROUND

Receptor-type tyrosine-protein phosphatase T (PTPRT) is a transmembrane protein that is involved in cell adhesion. We previously found that PTPRT was downregulated in multiple cancer types and the mutation of PTPRT was associated with cancer early metastasis. However, the impacts of PTPRT downregulation on tumour proliferation, invasion, and clinical interventions such as immune checkpoint inhibitor (ICI) therapies remained largely unknown.

METHODS

Gene expression data of non-small cell lung cancer (NSCLC) samples from The Cancer Genome Atlas database were downloaded and used to detect the differential expressed genes between PTPRT-high and PTPRT-low subgroups. Knockdown and overexpress of PTPRT in lung cancer cell lines were performed to explore the function of PTPRT in vitro. Western blot and qRT-PCR were used to evaluate the expression of cell cycle-related genes. CCK-8 assays, wound-healing migration assay, transwell assay, and colony formation assay were performed to determine the functional impacts of PTPRT on cell proliferation, migration, and invasion. KM-plotter was used to explore the significance of selected genes on patient prognosis.

RESULTS

PTPRT was found to be downregulated in tumours and lung cancer cell lines compared to normal samples. Cell cycle-related genes (BIRC5, OIP5, and CDCA3, etc.) were specifically upregulated in PTPRT-low lung adenocarcinoma (LUAD). Modulation of PTPRT expression in LUAD cell lines affected the expression of BIRC5 (survivin) significantly, as well as the proliferation, migration, and invasion of tumour cells. In addition, low PTPRT expression level was correlated with worse prognosis of lung cancer and several other cancer types. Furthermore, PTPRT downregulation was associated with elevated tumour mutation burden and tumour neoantigen burden in lung cancer, indicating the potential influence on tumour immunogenicity.

CONCLUSION

Our findings uncovered the essential roles of PTPRT in the regulation of proliferation, migration, and invasion of LUAD, and highlighted the clinical significance of PTPRT downregulation in lung cancer.

The emerging role of noncoding RNAs in the EGFR signaling pathway in lung cancer

Noncoding ribonucleic acids (ncRNAs) have surfaced as essential orchestrators within the intricate system of neoplastic biology. Specifically, the epidermal growth factor receptor (EGFR) signalling cascade shows a central role in the etiological underpinnings of pulmonary carcinoma. Pulmonary malignancy persists as a preeminent contributor to worldwide mortality attributable to malignant neoplasms, with non-small cell lung carcinoma (NSCLC) emerging as the most predominant histopathological subcategory. EGFR is a key driver of NSCLC, and its dysregulation is frequently associated with tumorigenesis, metastasis, and resistance to therapy. Over the past decade, researchers have unveiled a complex network of ncRNAs, encompassing microRNAs, long noncoding RNAs, and circular RNAs, which intricately regulate EGFR signalling. MicroRNAs, as versatile post-transcriptional regulators, have been shown to target various components of the EGFR pathway, influencing cancer cell proliferation, migration, and apoptosis. Additionally, ncRNAs have emerged as critical modulators of EGFR signalling, with their potential to act as scaffolds, decoys, or guides for EGFR-related proteins. Circular RNAs, a relatively recent addition to the ncRNA family, have also been implicated in EGFR signalling regulation. The clinical implications of ncRNAs in EGFR-driven lung cancer are substantial. These molecules exhibit diagnostic potential as robust biomarkers for early cancer detection and personalized treatment. Furthermore, their predictive value extends to predicting disease progression and therapeutic outcomes. Targeting ncRNAs in the EGFR pathway represents a novel therapeutic approach with promising results in preclinical and early clinical studies. This review explores the increasing evidence supporting the significant role of ncRNAs in modulating EGFR signalling in lung cancer, shedding light on their potential diagnostic, prognostic, and therapeutic implications.

Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods

BACKGROUND

Targeted therapy has revolutionized cancer treatment, greatly improving patient outcomes and quality of life. Lung cancer, specifically non-small cell lung cancer, is frequently driven by the G12C mutation at the KRAS locus. The development of KRAS inhibitors has been a breakthrough in the field of cancer research, given the crucial role of KRAS mutations in driving tumor growth and progression. However, over half of patients with cancer bypass inhibition show limited response to treatment. The mechanisms underlying tumor cell resistance to this treatment remain poorly understood.

METHODS

To address above gap in knowledge, we conducted a study aimed to elucidate the differences between tumor cells that respond positively to KRAS (G12C) inhibitor therapy and those that do not. Specifically, we analyzed single-cell gene expression profiles from KRAS G12C-mutant tumor cell models (H358, H2122, and SW1573) treated with KRAS G12C (ARS-1620) inhibitor, which contained 4297 cells that continued to proliferate under treatment and 3315 cells that became quiescent. Each cell was represented by the expression levels on 8687 genes. We then designed an innovative machine learning based framework, incorporating seven feature ranking algorithms and four classification algorithms to identify essential genes and establish quantitative rules.

RESULTS

Our analysis identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, that are known to be associated with the progression of multiple cancers.

CONCLUSION

Above genes were relevant to tumor cell resistance to targeted therapy. This study provides important insights into the molecular mechanisms underlying tumor cell resistance to KRAS inhibitor treatment.