Colorectal Cancer and Bone Tissue: Fantastic Relations and Where to Find Them

Molecular landscape of prostate cancer bone metastasis

Prostate cancer (PC) has a high propensity to develop bone metastases, causing severe pain and pathological fractures that profoundly impact a patients' normal functions. Current clinical intervention is mainly palliative focused on pain management, and tumor progression is refractory to standard therapeutic regimens. This limited treatment efficacy is at least partially due to a lack of comprehensive understanding of the molecular landscape of the disease pathology, along with the intensive overlapping of physiological and pathological molecular signaling. The niche is overwhelmed with diverse cell types with inter- and intra-heterogeneity, along with growth factor-enriched cells that are supportive of invading cell proliferation, providing an additional layer of complexity. This review seeks to provide molecular insights into mechanisms underlying PC bone metastasis development and progression.

Prognostic impact of colorectal cancer patients with bone metastases: a single-center experience

The incidence of bone metastasis (BM) in colorectal cancer (CRC) patients is low and the prognosis is poor. There is no clear conclusion on the risk factors affecting the survival of CRC patients with BM. The aim of this study was to investigate the factors that may affect the prognosis of CRC patients with BM. The clinical and pathological data of CRC patients with BM were retrospectively analyzed. The overall survival after BM diagnosis was estimated using the Kaplan-Meier method and Log-rank test, and a multivariable cox regression model was used to identify the prognostic factors of overall survival. This study included 178 CRC patients with BM, of whom 151 had left-sided CRC and 27 had right-sided colon cancer. 1124 CRC patients with BM from the SEER database were included to perform a sensitivity analysis of the primary outcome. Multivariate analysis showed that the N staging, site of BM, and primary tumor sidedness (PTS) were independent prognostic factors for CRC with BM. Among them, right-sided colon cancer patients with BM had a poorer prognosis. Sensitivity analyses showed that PTS was an independent prognostic factor in CRC patients with BM. Primary tumor sidedness and N stage may be potential prognostic markers for BM of CRC. The prognosis of N0 stage CRC with BM is better, while the prognosis of right-sided colon cancer is poor.

Current and potential treatment of colorectal cancer metastasis to bone

BACKGROUND

Colorectal cancer (CRC) with subsequent bone metastasis is associated with a poor prognosis compared with patients who do not develop bone metastasis. However, metastasis in bone is rare, contrasted with more common locations such as the liver and lungs. As a result, the treatment methods targeting CRC bone lesions are limited. This review aims to compile information regarding current and potential medical and surgical treatment methods for colorectal cancer with specific regard to bone metastasis.

METHODS

A computer-based literature review of animal- and human-based studies was conducted using multiple database searches. Case reports were excluded.

RESULTS

Preliminary findings demonstrate that treatments specifically targeting bone metastasis due to colorectal cancer are categorized by local vs. systemic treatment. The primary goals are the alleviation of skeletal-related events and improvement in quality of life. Current options include: chemotherapy, radiation, monoclonal antibodies, and surgery. Emerging options include intratumoral mellitin, MRgFUS, and bone microenvironment targeting.

CONCLUSION

Treatment of CRC metastasis to bone is necessary to slow down metastatic progression, alleviate symptoms, and improve quality of life. With a possible rise in bone metastasis due to increased overall CRC survival rates, more clinical trials should be performed to address this growing concern.

A robust and stable gene selection algorithm based on graph theory and machine learning

BACKGROUND

Nowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffer from being a very underdetermined system. In a very large set of features but a very small sample size domain (e.g. DNA microarray, RNA-seq data, GWAS data, etc.), it is often reported that several contrasting feature subsets may yield near equally optimal results. This phenomenon is known as instability. Considering these facts, we have developed a robust and stable supervised gene selection algorithm to select a set of robust and stable genes having a better prediction ability from the gene expression datasets with phenotypes. Stability and robustness is ensured by class and instance level perturbations, respectively.

RESULTS

We have performed rigorous experimental evaluations using 10 real gene expression microarray datasets with phenotypes. They reveal that our algorithm outperforms the state-of-the-art algorithms with respect to stability and classification accuracy. We have also performed biological enrichment analysis based on gene ontology-biological processes (GO-BP) terms, disease ontology (DO) terms, and biological pathways.

CONCLUSIONS

It is indisputable from the results of the performance evaluations that our proposed method is indeed an effective and efficient supervised gene selection algorithm.

Development and validation of risk and prognostic nomograms for bone metastases in Chinese advanced colorectal cancer patients

Background

Bone metastases (BM) from colorectal cancer (CRC) are often accompanied by extraosseous metastases, resulting in a dismal prognosis. The present study aimed to determine the risk factors for BM in metastatic CRC (mCRC) and the prognostic factors for CRC patients with BM.

Methods

The study was based on a training cohort of 214 mCRC patients (of which, 101 patients had BM) from our center, and a validation cohort of 511 mCRC patients (of which, 173 patients had BM) from another institute. Risk and prognostic nomograms for BM were developed using univariate and multivariate analyses. The goodness of fit, discrimination, and calibration performance of the nomograms were assessed by R², concordance statistics (C-statistics), and the calibration curve. The results were internally validated using bootstrap resampling in the training cohort, and externally validated in the validation cohort.

Results

The novel BM risk nomogram comprised seven variables [degree of tumor differentiation, N-stage, serum alkaline phosphatase (ALP), lactate dehydrogenase (LDH), carcinoembryonic antigen (CEA), liver metastasis, and lung metastasis]. It showed good performance, with an R² of 0.447 and a C-statistic of 0.846 [95% confidence interval (CI), 0.793 to 0.898] in the training cohort, and an R² of 0.325 and a C-statistic of 0.792 (95% CI, 0.750 to 0.834) in the validation cohort. The optimal cutoff value to identify individuals at low or high risk was 56% probability, with a sensitivity of 71.3% and a specificity of 89.4%. The prognostic nomogram included five factors (tumor differentiation, number of extra-BM organs, number of BM lesions, ALP, and LDH), and had an R² of 0.284 and a C-statistic of 0.723 (95% CI, 0.657 to 0.789) in the training set. This nomogram was externally validated in the validation cohort, with an R² of 0.182 and a C-statistic of 0.682 (95% CI, 0.638 to 0.726).

Conclusions

The developed and validated risk and prognostic nomograms showed good performance for predicting the occurrence of BM in mCRC as well as the prognosis of CRC patients with BM. The risk nomogram can be used as a cost-effective preliminary screening tool prior to bone scanning.