Predictive value of MRI-detected tumor deposits in locally advanced rectal cancer

Diagnostic Value of Plasma Long Non-coding SLC26A4 Antisense RNA 1 Combined with Magnetic Resonance Imaging in Rectal Cancer

Background/Aims

The prevalence of rectal cancer is increasing every year due to changes in living and eating habits. Early diagnosis contributes to the treatment and survival of patients. This study investigated the feasibility of employing SLC26A4-AS1 combined with magnetic resonance imaging (MRI) for diagnosing rectal cancer.

Materials and Methods

The current study involved 125 patients with rectal cancer and an equal number of healthy individuals. The study focused on assessing the relationship between SLC26A4-AS1 expression and clinical data among patients with rectal cancer by analyzing the expression levels. MRI blood perfusion parameters (Ktrans, Kep, Ve, and incremental area under the curve (iAUC)) were measured in the patients with rectal cancer. The regulation of SLC26A4-AS1 on the biological function of rectal cancer cells was analyzed by Cell Counting Kit-8 (CCK-8) method, flow cytometry, and Transwell assay. Furthermore, luciferase activity assays and RNA-binding protein immunoprecipitation assay (RIP) were conducted to elucidate the relationship between SLC26A4-AS1 and microRNA-3174 (miR-3174).

Results

A significant reduction in SLC26A4-AS1 expression was observed in rectal cancer alongside a significant increase in miR-3174 levels. SLC26A4-AS1 expression was negatively correlated with Ktrans and Kep values, but not with Ve or iAUC values. Cell experiments confirmed the inhibitory effect of SLC26A4-AS1 overexpression on the growth of rectal cancer cells. Additionally, SLC26A4-AS1 sponged miR-3174 mediated the progression of rectal cancer. The enriched miR-3174 may counteract the suppression of the biological activity of oe-SLC26A4-AS1 on rectal cancer cells.

Conclusion

SLC26A4-AS1 may serve as a diagnostic tool for rectal cancer, mediating tumor progression by directly targeting miR-3174.

Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.