Skeletal muscle metastasis with bone metaplasia from colon cancer: A case report and review of the literature

Gefitinib induces apoptosis in Caco-2 cells via ER stress-mediated mitochondrial pathways and the IRE1α/JNK/p38 MAPK signaling axis

Gefitinib, a selective EGFR-tyrosine kinase inhibitor, exhibits potent cytotoxic effects on colorectal cancer cells, though its precise mechanisms are not fully understood. In this study, we demonstrated that gefitinib induces a dose-dependent cytotoxic response in Caco-2 cells, characterized by disrupted microtubule networks, impaired migration, and reduced viability. Gefitinib triggered apoptosis, as indicated by increased levels of cleaved caspase-3, PARP, and elevated late apoptosis rates. Mechanistically, gefitinib-induced endoplasmic reticulum (ER) stress, marked by the upregulation of IRE1α, CHOP, and ATF4. ER stress inhibition by 4-PBA significantly reduced apoptosis and restored mitochondrial membrane potential (MMP). Additionally, gefitinib-induced apoptosis was mediated through the mitochondrial pathway, reflected by the modulation of Bcl-2 family proteins, including the upregulation of Bax and Bim. Inhibition of the IRE1α-mediated JNK/p38 MAPK pathway further mitigated gefitinib-induced apoptosis and restored MMP. These findings highlight the critical role of ER stress and the IRE1α-JNK/p38 MAPK axis in gefitinib-induced mitochondrial apoptosis, offering potential therapeutic targets for colorectal cancer.

18F-FDG PET/CT imaging of several asymptomatic skeletal muscle metastases of mixed germ cell ovarian cancer

Metastasis to muscles caused by ovarian cancer is very rare and has a poor prognosis. Performing a whole-body F18-FDG PET/CT scan makes it possible to examine the whole body in one study and detected lesions in unexpected places.

Ossifying skeletal muscle metastasis as an initial manifestation of colorectal adenocarcinoma

Skeletal muscle metastases from colorectal cancer (CRC) are very rare. They have usually been described in patients with known CRC and as a well-defined mass. In this case report, we present a unique case of diffuse and ossifying skeletal muscle metastasis as the first manifestation of CRC. A 36-year-old man presented with progressive left thigh swelling and left hip pain of 6 months duration. He denied any gastrointestinal complaints. CT and MRI of the thigh showed diffuse muscular swelling, signal abnormalities with linear calcifications involving the muscles of the posterior and medial compartments of the left thigh and both gluteal regions. Histology confirmed the metastatic nature of a colorectal adenocarcinoma. The patient was treated with chemotherapy and showed a good evolution after 6 months of follow-up.

Machine learning algorithms to predict atypical metastasis of colorectal cancer patients after surgical resection

Background

The prognosis of colorectal cancer with atypical metastasis is poor. However, atypical metastasis was less common and under-appreciated.

Methods

In this study we attempted to present the first machine learning models to predict the risk of atypical metastasis in colorectal cancer patients. We evaluated the differences between metastasis and non-metastasis groups, assessed factors associated with atypical metastasis using univariate and multivariate logistic regression analyses, and preliminarily developed the multiple machine learning models to predict atypical metastasis.

Results

168 patients were included. Prognostic Nutritional Index (PNI) [OR =  0.998; P = 0.030], Cancer antigen 19-9 (CA19-9) [OR = 1.011; P = 0.043] and MR-Distance [-mid OR = 0.289; P = 0.009] [-high OR = 0.248; P = 0.021] were shown to be independent risk factors for the atypical metastasis via multivariate analysis. Furthermore, the machine learning model based on AdaBoost algorithm (AUC: 0736) has better predictive performance comparing to Logistic Regression (AUC: 0.671) and KNeighbors Classifier (AUC: 0.618) by area under the curve (AUC) in the validation cohorts. The accuracy, sensitivity, and specificity of the model trained using the Adaboost method in the validation set are 0.786, 0.776 and 0.700, while 0.601, 0.933, 0.508 using Logistic Regression and 0.743, 0.390, 0.831 using KNeighbors Classifier.

Conclusion

Machine-learning approaches containing PNI, CA19-9 and MR-Distance show great potentials in atypical metastasis prediction.

Skeletal muscle metastasis from colorectal adenocarcinoma: A literature review

BACKGROUND

Colorectal adenocarcinoma is the third most common cancer worldwide. It accounts for almost 10% of all cancer-related deaths. Skeletal muscle is a very unusual site for metastasis from colorectal cancers and is associated with a poor prognosis and high mortality.

AIM

To review the literature for cases of skeletal muscle metastasis (SMM) from colorectal adenocarcinoma.

METHODS

A systematic literature search using a validated search strategy was carried out to identify the incidence of SMM associated with colorectal adenocarcinoma. The studies identified were tabulated in a PRISMA, and data was extracted in a tabulated form.

RESULTS

Twenty-nine studies were included in this literature review. SMM was most commonly detected in the thigh muscles. Most of the tumours had originated from the rectum or the right colon. The histopathology of the primary tumour was generally advanced. The mean time interval between the primary tumour and onset of SMM was 22 mo. In 3 cases, asymptomatic SMM had been picked up by advanced imaging systems, like fluorodeoxyglucose-positron emission tomography scan.

CONCLUSION

SMM from colorectal adenocarcinomas is a rare complication. However, it is possible that the low incidence could be due to under-reporting. Early use of advanced imaging techniques and a high index of clinical suspicion might increase the reporting of SMM from colorectal adenocarcinoma.