Ras p21 protein activator 1 (RASA1) is a regulator of Ras GDP and GTP and is involved in numerous physiological processes such as angiogenesis, cell proliferation, and apoptosis. As a result, RASA1 also contributes to pathological processes in vascular diseases and tumour formation. This review focuses on the role of RASA1 in multiple tumours types in the lung, intestines, liver, and breast. Furthermore, we discuss the potential mechanisms of RASA1 and its downstream effects through Ras/RAF/MEK/ERK or Ras/PI3K/AKT signalling. Moreover, miRNAs are capable of regulating RASA1 and could be a novel targeted treatment strategy for tumours.

Cancer is a complex, multifactorial disease that modern medicine ultimately aims to overcome. Downstream of tyrosine kinase 2 (DOK2) is a well-known tumor suppressor gene, and a member of the downstream protein DOK family of tyrosine kinases. Through a search of original literature indexed in PubMed and other databases, the present review aims to extricate the mechanisms by which DOK2 acts on cancer, thereby identifying more reliable and effective therapeutic targets to promote enhanced methods of cancer prevention and treatment. The review focuses on the role of DOK2 in multiple tumor types in the lungs, intestines, liver, and breast. Additionally, we discuss the potential mechanisms of action of DOK2 and the downstream consequences via the Ras/MPAK/ERK or PI3K/AKT/mTOR signaling pathways.

Anaplastic lymphoma kinase (ALK) inhibitors have been shown to be effective in treating patients with ALK‑positive non‑small cell lung cancer (NSCLC), and crizotinib, ceritinib and alectinib have been approved as clinical first‑line therapeutic agents. The availability of these inhibitors has also largely changed the treatment strategy for advanced ALK‑positive NSCLC. However, patients still inevitably develop resistance to ALK inhibitors, leading to tumor recurrence or metastasis. The most critical issues that need to be addressed in the current treatment of ALK‑positive NSCLC include the high cost of targeted inhibitors and the potential for increased toxicity and resistance to combination therapy. Recently, it has been suggested that the serine/threonine kinase 11 (STK11) mutation may serve as one of the biomarkers for immunotherapy in NSCLC. Therefore, the main purpose of this review was to summarize the role of STK11 in ALK‑positive NSCLC. The present review also summarizes the treatment and drug resistance studies in ALK‑positive NSCLC and the current status of STK11 research in NSCLC.

Non-small-cell lung cancer (NSCLC) has the highest morbidity and mortality rates among all malignant tumor types. Although therapies targeting the mutated genes such as KRAS have been used in the clinic for many years, the prognosis remains poor. Therefore, it is necessary to further study the aberrant expression or mutation of non-target genes affecting the survival and prognosis. To explore the impact of simultaneous abnormalities of multiple genes on the prognosis and survival of patients. We used R packages to analyze gene expression data and clinical data down loaded from The Cancer Genome Atlas (TCGA) database. We also collected samples from 85 NSCLC patients from the First People’s Hospital of Jingzhou City and retrospectively followed the patients. Multivariate Cox regression analysis and survival analysis were performed. Analysis of gene expression data from TCGA revealed that the overexpression of the following single genes affected overall survival: TP53 (P = 0.79), PTEN (P = 0.94), RB1 (P = 0.49), CTNNB1 (P = 0.24), STK11 (P = 0.32), and PIK3CA (P = 0.013). However, the probability of multiple genes (TP53, PTEN, RB1, and STK11) affecting survival was 0.025. Retrospective analysis of clinical data revealed that sex (hazard ratio [HR] = 1.29; [95%CI: 0.64-2.62]), age (HR = 1.05; [95%CI: 1.02-1.07]), smoking status (HR = 2.26; [95%CI: 1.16-4.39]), tumor histology (HR = 0.58; [95%CI: 0.30-1.11]), cancer stage (HR = 16.63; [95%CI: 4.8-57.63]), epidermal growth factor receptor (EGFR) mutation (HR = 1.82; [95%CI: 1.05-3.16]), abundance (HR = 4.95; [95%CI: 0.78-31.36]), and treatment with tyrosine kinase inhibitors (TKIs) (HR = 0.58; [95%CI: 0.43-0.78]) affected patient survival. Co-occurring mutations of TP53, PTEN, RB1, and STK11 did not significantly affect the overall survival of patients receiving chemotherapy (P = 0.96) but significantly affected the overall survival of patients receiving TKIs (P = 0.045). Co-occurring mutation or overexpression of different genes has different effects on the overall survival and prognosis of NSCLC patients. Combined with TKI treatment, the co-occurring mutation of some genes may have a synergistic effect on the survival and prognosis of NSCLC patients.

Colorectal cancer (CRC) ranks third in global incidence and second in mortality. However, a comprehensive predictive model for CRC prognosis, immunotherapy response, and drug sensitivity is still lacking. Various types of programmed cell death (PCD) are crucial for cancer occurrence, progression, and treatment, indicating their potential as valuable predictors. Fourteen PCD genes were collected and subjected to dimensionality reduction using regression methods to identify key hub genes. Predictive models were constructed and validated based on bulk transcriptomes and single-cell transcriptomes. Furthermore, the tumor microenvironment, immunotherapy response, and drug sensitivity profiles among patients with CRC were explored and stratified by risk. A risk score incorporating the PCD genes FABP4, AQP8, and NAT1 was developed and validated across four independent datasets. Patients with CRC who had a high-risk score exhibited a poorer prognosis. Unsupervised clustering algorithms were used to identify two molecular subtypes of CRC with distinct features. The risk score was combined with the clinical features to create a nomogram model with superior predictive performance. Additionally, patients with high-risk scores exhibited decreased immune cell infiltration, higher stromal scores, and reduced responsiveness to immunotherapy and first-line clinical drugs compared with low-risk patients. Furthermore, the top ten non-clinical first-line drugs for treating CRC were selected based on their predicted IC50 values. Our results indicate the efficacy of the model and its potential value in predicting prognosis, response to immunotherapy, and sensitivity to different drugs in patients with CRC.