Colorectal cancer: a comprehensive review of carcinogenesis, diagnosis, and novel strategies for classified treatments

Abedizadeh, Roya; Majidi, Fateme; Khorasani, Hamid Reza; Abedi, Hassan; Sabour, Davood

doi:10.1007/s10555-023-10158-3

Cited by 3 publications

(1 citation statement)

References 164 publications

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“…The continuous development of treatment technologies has enabled patients with early-stage CRC to achieve long-term survival through surgical tumor resection. However, those with advanced-stage CRC still have no chance of recovery 2 , 3 . Therefore, early preoperative diagnosis can provide vital information for treatment-related decision-making.…”

Section: Introductionmentioning

confidence: 99%

Changes in subset distribution and impaired function of circulating natural killer cells in patients with colorectal cancer

Zu,

Lu,

Xing

et al. 2024

Sci Rep

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Natural killer (NK) cells are closely associated with malignant tumor progression and metastasis. However, studies on their relevance in colorectal cancer (CRC) are limited. We aimed to comprehensively analyze the absolute counts, phenotypes, and function of circulating NK cells in patients with CRC using multiparametric flow cytometry. The distribution of NK cell subsets in the peripheral circulation of patients with CRC was significantly altered relative to the control group. This is shown by the decreased frequency and absolute count of CD56dimCD16+ NK cells with antitumor effects, contrary to the increased frequency of CD56bright NK and CD56dimCD16- NK cells with poor or ineffective antitumor effects. NK cells in patients with CRC were functionally impaired, with decreased intracellular interferon (IFN)-γ secretion and a significantly lower percentage of cell surface granzyme B and perforin expression. In addition, IFN-γ expression decreased significantly with the tumor stage progression. Based on a comprehensive analysis of the absolute counts, phenotypes, and functional markers of NK cells, we found an altered subset distribution and impaired function of circulating NK cells in patients with CRC.

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Section: Introductionmentioning

confidence: 99%

Changes in subset distribution and impaired function of circulating natural killer cells in patients with colorectal cancer

Zu,

Lu,

Xing

et al. 2024

Sci Rep

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Identification and validation of Rab GTPases RAB13 as biomarkers for peritoneal metastasis and immune cell infiltration in colorectal cancer patients

Lv,

Li,

Wang

et al. 2024

Front. Immunol.

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BackgroundAs one of the most common cancer, colorectal cancer (CRC) is with high morbidity and mortality. Peritoneal metastasis (PM) is a fatal state of CRC, and few patients may benefit from traditional therapies. There is a complex interaction between PM and immune cell infiltration. Therefore, we aimed to determine biomarkers associated with colorectal cancer peritoneal metastasis (CRCPM) and their relationship with immune cell infiltration.MethodsBy informatic analysis, differently expressed genes (DEGs) were selected and hub genes were screened out. RAB13, one of the hub genes, was identificated from public databases and validated in CRC tissues. The ESTIMATE, CEBERSORT and TIMER algorithms were applied to analyze the correlation between RAB13 and immune infiltration in CRC. RAB13’s expression in different cells were analyzed at the single-cell level in scRNA-Seq. The Gene Set Enrichment Analysis (GSEA) was performed for RAB13 enrichment and further confirmed. Using oncoPredict algorithm, RAB13’s impact on drug sensitivity was evaluated.ResultsHigh RAB13 expression was identified in public databases and led to a poor prognosis. RAB13 was found to be positively correlated with the macrophages and other immune cells infiltration and from scRNA-Seq, RAB13 was found to be located in CRC cells and macrophages. GSEA revealed that high RAB13 expression enriched in a various of biological signaling, and oncoPredict algorithm showed that RAB13 expression was correlated with paclitaxel sensitivity.ConclusionOur study indicated clinical role of RAB13 in CRC-PM, suggesting its potential as a therapeutic target in the future.

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A Novel Approach for Predicting the Survival of Colorectal Cancer Patients Using Machine Learning Techniques and Advanced Parameter Optimization Methods

Woźniacki,

Książek,

Mrowczyk

2024

Cancers

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Background: Colorectal cancer is one of the most prevalent forms of cancer and is associated with a high mortality rate. Additionally, an increasing number of adults under 50 are being diagnosed with the disease. This underscores the importance of leveraging modern technologies, such as artificial intelligence, for early diagnosis and treatment support. Methods: Eight classifiers were utilized in this research: Random Forest, XGBoost, CatBoost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset from Brazil, containing information on tens of thousands of patients. Results: The models developed in this study demonstrated high classification accuracy in predicting one-, three-, and five-year survival, as well as overall mortality and cancer-specific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest classifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks. Conclusions: This research enabled the development of effective classification models that can be applied in clinical practice.

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Colorectal cancer: a comprehensive review of carcinogenesis, diagnosis, and novel strategies for classified treatments

Cited by 3 publications

References 164 publications

Changes in subset distribution and impaired function of circulating natural killer cells in patients with colorectal cancer

Changes in subset distribution and impaired function of circulating natural killer cells in patients with colorectal cancer

Identification and validation of Rab GTPases RAB13 as biomarkers for peritoneal metastasis and immune cell infiltration in colorectal cancer patients

A Novel Approach for Predicting the Survival of Colorectal Cancer Patients Using Machine Learning Techniques and Advanced Parameter Optimization Methods

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