Cancer Management and Research最新文献

Introduction: Breast cancer is highly metastatic. One protein that may participate in breast cancer cell migration is the actin motor protein myosin 6 (MYO6), which is likely regulated by the GIPC1 protein. Additionally, septins (SEPTs) appear to participate in breast cancer motility. Here, we investigated the effects of loss of MYO6 on cell morphology, migration, and expression of GIPC1, SEPT2, and SEPT7 in two breast cancer cell lines.

Material and methods: The research material consisted of two breast cancer cell lines, MCF-7 and MDA-MB-231, in which the level of MYO6 was reduced and the effect of knockdown on the migration potential and the expression of GIPC1, SEPT2 and SEPT7 was determined. The levels of these proteins were also analyzed in silico.

Results: siRNA-mediated knock down of MYO6 altered the morphology of MCF-7 cells and reduced the expression of GIPC1 and SEPT7 in both MCF-7 and MDA-MB-231 cells. In in silico data, GIPC1, SEPT2, and SEPT7 were all overexpressed in breast cancer tissue samples from patients. Finally, MYO6 knock down impaired migration and adhesion in both MCF-7 and MDA-MB-231 cells.

Conclusion: Our study substantiates that downregulation of MYO6 diminishes the migratory abilities of breast cancer cell lines with varying invasiveness. Furthermore, we have demonstrated that decreased MYO6 protein leads to reduced expression of GIPC1, SEPT2, and SEPT7 in breast cancer cells. These findings contribute to a more comprehensive understanding of the pathways influencing breast cancer cell migration, a critical aspect of metastasis.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Tumor immune microenvironment (TIME), angiogenesis, epithelial-mesenchymal transformation (EMT), invasion, metastasis, metabolism, and drug resistance are the main factors affecting the development and treatment of tumors. MiRNAs play crucial roles in almost all major cellular biological processes. Studies have been carried out on miRNAs as biomarkers and therapeutic targets. Their dysregulation contributes to the progression and prognosis of HCC. This review aims to explore the molecular cascades and corresponding phenotypic changes caused by aberrant miRNA expression and their regulatory mechanisms, summarize and analyze novel biomarkers from somatic fluids (plasma/serum/urine), and highlight the latent capacity of miRNAs as therapeutic targets.

Introduction: Gastric cancer, the fifth most common malignant tumor in the world, poses a serious threat to human health. However, the role of fatty acid metabolism (FAM) in gastric cancer remains incompletely understood. We aim to provide guidance for clinical decisions by utilizing public database of gastric adenocarcinoma to establish an FAM-related gene subtypes via machine learning algorithm.

Methods: The intersection of FMGs from KEGG, Hallmark, and Reactome bioinformatics databases and the DEGs of the TCGA-STAD cohort was used to decompose the gene matrix related to establish FAM-related gene subtypes by NMF. Comparison of immune infiltrating differences between subtypes using ESTIMATE and Cibersort algorithms. The multifactor Cox regression to identify independent risk genes for patient prognosis based on the subtypes. A prognostic model including independent risk genes was built using random survival forest and Cox regression. IHC validation in gastric cancer and adjacent tissues confirmed the above gene expression level.

Results: 71 DEGs related to FMGs of STAD were identified, which was used to established the FAM-related gene subtypes, C1 and C2. The immune infiltrating analysis showed that most immune features of C2 were significantly upregulated compared to C1. The independent risk genes were CGβ8, UPK1B, and OR51G based on the subtypes. A gastric cancer prognostic model consisting of independent risk genes was constructed and patients were classified into high-risk and low-risk groups with survival differential analysis. Finally, IHC showed that CGβ8 and UPK1B expression were upregulated in gastric cancer, while OR51G2 did not detect differences in expression.

Conclusion: The study developed a machine learning-based gastric cancer prognosis risk model using FMGs. This model effectively stratifies patients according to their risk levels and provides valuable insights for clinical decision-making, enabling accurate evaluation of patient prognosis.

Background: The research on lymph node metastasis (LNM) in locally advanced gastric cancer (LAGC) infiltrating the subserous tissue and serous membrane (T3-4a) is significantly inadequate. This study aims to explore the clinicopathological factors related to LNM in stages T3 and T4a LAGC, while also developing predictive nomograms.

Methods: After systematic searching and rigorous screening, 1995 T3 and 1244 T4a LAGC cases who underwent surgery without neoadjuvant or perioperative chemotherapy were selected. The risk factors associated with LNM were identified using both univariate and multivariate logistic regression analyses. Subsequently, the independent variables identified through the multivariate analyses were utilized to construct a nomogram.

Results: The incidence of LNM in T3 and T4a LAGC was 77.1% (1539/1995) and 83.8% (1043/1244), respectively. The following factors were found to be independently associated with LNM in T3 LAGC: preoperative serum albumin <41g/L (P=0.007), gastrointestinal obstruction (P<0.001), tumor location (P=0.040), tumor size >4cm (P=0.002), mixed (P=0.001) and undifferentiated histological types (P=0.002), presence of lymphovascular invasion (LVI) (P<0.001) and nerve invasion (P<0.001). Additionally, in T4a LAGC cases, serum albumin < 39g/L (P=0.004), tumor size >6cm (P=0.020), mixed (P<0.001) and undifferentiated histological types (P<0.001), presence of gastrointestinal hemorrhage (P=0.016), neuroendocrine differentiation (P=0.024), and LVI (P<0.001) independently influenced the occurrence of LNM.

Conclusion: This study identified the risk factors associated with LNM in T3-4a LAGC cases and constructed nomograms, thereby providing valuable guidance for formulating and implementing a multidisciplinary perioperative treatment program.

Objective: To investigate the therapeutic efficacy of totally laparoscopic total gastrectomy (TLTG) versus laparoscopy-assisted total gastrectomy (LATG) in gastric cancer (GC) treatment, as well as their effects on postoperative inflammation and glucose and lipid metabolic status.

Methods: Clinical data of 68 individuals with GC who underwent LATG (n=31) and TLTG (n=37) from January 2020 to December 2022 were procured. This included intraoperative blood loss, operative time, incision length, number of lymph nodes dissected, postoperative complication rates, and recovery indicators, such as inflammation, glucose metabolism, and lipid metabolism.

Results: The TLTG cohort demonstrated significant advantages in intraoperative blood loss, operative time, and incision length compared to the LATG cohort. Furthermore, TLTG was superior in reducing the incidence of complications. Nevertheless, no substantial variation was observed in the quantity of lymph nodes dissected. Additionally, TLTG showed benefits in postoperative recovery, including better control of the inflammatory response, reduction of complication risks, shorter hospital stay, and alleviation of postoperative pain. TLTG also exhibited a reduced impact on inflammation and demonstrated greater effectiveness in improving postoperative glucose and lipid levels.

Conclusion: TLTG surgery is associated with superior clinical outcomes in the treatment of GC compared to LATG, particularly in reducing surgical trauma and accelerating postoperative recovery. Furthermore, TLTG facilitates the resolution of postoperative inflammatory responses and the amelioration of metabolic disorders. The findings from this investigation advocate for the broader adoption of TLTG in the surgical treatment of GC.

Objective: Colorectal carcinoma (CRC) is the third most common malignancy. In addition to comprehensive cancer treatments, such as surgery, chemotherapy, and radiotherapy, the adoptive immune cell therapy (ACT) has played an increasingly important role in recent years, and the adaptive transfusion of autologous NK cells and CIK cells is a brand-new approach to cellular therapy for solid tumors.

Case presentation: A 57-year-old man underwent a radical resection of microsatellite stable (MSS) rectal cancer with synchronous liver metastases. After surgery of the primary lesion surgery, he was treated with autologous CIK/NK cells combined with XELOX translational therapy. Each cycle can obtain over 10 × 10⁹ CIK cells or over 6 × 10⁹ NK cells combined chemotherapy of XELOX every 3 weeks. After 2 cycles of therapy, he achieved partial response (PR). He immediately underwent a hepatic metastasis resection. After surgery, the patient continued to receive autologous CIK/NK cells in combined with 4 cycles of XELOX. To date, he has achieved and maintained no evidence of disease (NED) for over 40 months.

Conclusion: This is a case of successful treatment of rectal cancer with liver metastasis using ACT in conjunction with first-line chemotherapy. The advantage of this treatment plan is that it has few side effects and achieves long-term control of tumor recurrence by improving the patient's immune function. However, its responsiveness and benefit rate still need further investigation.

Objective: This study was designed to investigate the clinical, pathological, endoscopic, and imaging characteristics of gastrointestinal metastasis in patients with lung cancer.

Methods: The clinical data of 20 patients with primary lung cancer with gastrointestinal metastasis.

Results: This study included sixteen men and four women, ranging in age from 31 to 75 years. The time interval from the diagnosis of lung cancer to the detection of gastrointestinal metastasis ranged from 13 to 142 months. The most common sites of metastasis were the small intestine (eight cases), colon (four cases), and upper gastrointestinal tract (eight cases). The major symptoms included obstruction, perforation, abdominal pain, abdominal distension, anorexia, and anemia. The predominant pathological type was poorly differentiated adenocarcinoma (seventeen cases). A single ulcer was mostly seen on endoscopy, and some cases showed a slight depression of the intestinal wall. The CT and PET-CT scan revealed bowel wall thickening, intraluminal polypoid masses, and intestinal perforation.

Conclusion: Gastrointestinal metastasis of lung cancer is mainly observed in the small intestine, colon, and stomach, and is often detected when severe complications such as gastrointestinal obstruction and perforation occurred. Regular evaluation of gastrointestinal conditions during lung cancer diagnosis and treatment is recommended to improve the diagnostic accuracy and prevent misdiagnosis.

Purpose: DNA methylation plays a regulatory role in the oncogenesis and tumor progression and is valuable in the diagnosis and prognosis of cancer. While circulating tumor DNA (ctDNA) is widely used in the detection of oncogenic mutations and the guidance of treatment in advanced non-small cell lung cancer (NSCLC), studies of ctDNA methylation remains insufficient. We aim to investigate the methylation profiles of ctDNA in patients with advanced NSCLC undergoing EGFR-tyrosine kinase inhibitor (EGFR-TKI) therapy and to discover novel biomarkers with predictive or prognostic value.

Patients and methods: We recruited 49 patients with EGFR-mutated advanced NSCLC undergoing EGFR-TKI as first-line treatment. Utilizing next-generation sequencing, we examined the somatic mutations and methylation signatures within the tumor-associated genomic regions of ctDNA from pre-treatment blood. Subsequently, we explored the association of these molecular features with the patients' response to therapy and their progression-free survival (PFS).

Results: Genomic mutation profiling revealed no significant association of PFS or best overall response (BOR) and ctDNA status. Evaluation of ctDNA methylation showed a negative correlation between the methylation of small nucleolar RNA (snoRNA) genes and PFS (R=-0.31, P=0.043). Furthermore, high-level methylation of SNORD3F was associated with poorer PFS (mPFS 346d vs 243d, HR 0.49, 95% CI 0.24-0.93, P=0.029).

Conclusion: Our study explored the prognostic value of ctDNA methylation in patients with advanced NSCLC undergoing targeted therapies and first revealed the predictive role of SNORD3F.

Colorectal cancer (CRC) is a diverse disease entity and a leading cause of cancer-related mortality worldwide. CRC results from the accumulation of multiple genetic and epigenetic alterations. This heterogeneity of CRC underscores the significance of understanding its molecular landscape, as variations in tumor genetics can greatly influence both patient prognosis and therapeutic response. The molecular complexity of CRC is defined by three major carcinogenesis pathways: chromosomal instability (CIN), microsatellite instability (MSI), and the CpG island methylator phenotype (CIMP). These pathways contribute to the onset and progression of CRC through mutations, epigenetic modifications, and dysregulated cellular signalling networks. The heterogeneous nature of CRC continues to pose challenges in identifying universally effective treatments, highlighting the need for personalized approaches. Hence, the present review aims at unravelling the molecular complexity of CRC that is essential for improving diagnosis, prognostication, and treatment. We detail on the current understanding of the molecular framework of CRC, central signalling pathways of CRC associated with its initiation to a malignant phenotype, further invasion, progression, metastases, and response to therapy. Continued research into CRC's pathways and biomarkers will pave the way for the development of more precise and effective therapeutic strategies, ultimately improving patient outcomes.

Background: Recurrence is the main factor for poor prognosis in ovarian cancer, but few prognostic biomarkers were reported. In this study, we used machine learning methods based on multiple biomarkers to develop a specific prediction model for the recurrence of ovarian cancer.

Methods: A total of 277 ovarian cancer patients were enrolled in this study and randomly classified into training and testing cohorts. The prediction information was obtained through 47 clinical parameters using six supervised clustering machine learning algorithms, including K-Nearest Neighbor (K-NN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost).

Results: In predicting the recurrence of ovarian cancer, machine learning algorithm was superior to conventional logistic regression analysis. In this study, XGBoost showed the best performance in predicting the recurrence of ovarian cancer, with an accuracy of 0.95. In addition, neoadjuvant chemotherapy, Monocyte ratio (MONO%), Hematocrit (HCT), Prealbumin (PAB), Aspartate aminotransferase (AST), and carbohydrate antigen 125 (CA125) are the most important biomarkers to predict the recurrence of ovarian cancer.

Conclusion: The machine learning techniques can achieve a more accurate assessment of the recurrence of ovarian cancer, which can help clinicians make decisions, and develop personalized treatment strategies.