Exogenous interleukin-1α signaling negatively impacts acquired chemoresistance and alters cell adhesion molecule expression pattern in colorectal carcinoma cells HCT116

IL-1α and colorectal cancer pathogenesis: Enthralling candidate for anti-cancer therapy

Inflammation has been well-established as a hallmark of colorectal cancer (CRC). Interleukin-1 alpha (IL-1α) is one of the primary inflammatory mediators driving the pathogenesis of inflammation-associated CRC. This systematic review presents the roles of IL-1α in the pathogenesis of the disease. Bibliographic databases PubMed, Science Direct, Scopus and Web of Science were systematically searched for articles that addresses the relationship between IL-1α and colorectal cancer. We highlighted various mechanisms by which IL-1α promotes the pathogenesis of CRC including enhancement of angiogenesis, metastasis, resistance to therapy, and inhibition of tumour suppressive genes. We also discussed the potential mechanisms by which IL-1α expression is induced or secreted in various studies. Beyond these, the systematic review also highlights several potential therapeutic strategies which should be further explored in the future; to target IL-1α and/or its associated pathways; paving our way in finding effective treatments for CRC patients.

The association between IL-1 family gene polymorphisms and colorectal cancer: A meta-analysis

BACKGROUND

Colorectal cancer (CRC) is a major public health problem given its high incidence and mortality. This study focuses on examining the associations between IL-1α, IL-1β, and IL-1RN polymorphisms and colorectal cancer susceptibility.

METHODS

A systematic literature search of PubMed, Embase, Web of Science, CNKI (China National Knowledge Infrastructure) and Wan Fang databases was conducted to identify relevant studies. Relevant data were extracted from the original included studies. The correlation was demonstrated based on the odds ratio (OR) and corresponding 95% confidence intervals (95% CIs). Publication bias was investigated by Egger's line regression test and Begg's funnel plot.

RESULTS

Eighteen independent studies involving 6218 cases and 10160 controls were eligible for this pooled analysis. Overall, the result revealed that the IL-1α rs3783553 polymorphism was significantly associated with an increased risk of CRC (G vs. C, OR = 1.02, 95% CI = 0.90-1.15, I² = 51%, P = 0.78; GG vs. CC, OR = 1.97, 95% CI = 1.04-3.74, I² = 70%, P = 0.04; GC vs. CC, OR = 1.75, 95% CI = 1.12-2.75, I² = 42%, P = 0.01; GG + GC vs. CC, OR = 1.85, 95% CI = 1.08-3.18, I² = 63%, P = 0.03; and GG vs. GC + CC, OR = 1.28, 95% CI = 1.04-1.58, I² = 39%, P = 0.02), and significance was also noted for IL-1RN VNTR under the dominant model (22 + 2L vs. LL, OR = 1.49, 95% CI = 1.01-2.19, I² = 77%, P = 0.045) and allelic contrast model (2 vs. L, OR = 1.28, 95% CI = 1.00-1.64, I² = 58.6%, P = 0.047). For IL-1β + 31C/T, significance was observed in the dominant model (CC + CT vs. TT, OR = 0.83, 95% CI = 0.69-0.99, I² = 52%, P = 0.034) and the heterozygous model (CT vs. TT, OR = 0.80, 95% CI = 0.65-0.98, I² = 60%, P = 0.04). For IL-1β + 511 C/T, a significant association was noted in four gene models (CT vs. TT, OR = 0.72, 95% CI = 0.63-0.83, I² = 0%, P < 0.001; CC + CT vs. TT, OR = 0.74, 95% CI = 0.65-0.84, I² = 0%, P < 0.001; CC vs. TT, OR = 0.77, 95% CI = 0.65-0.91, I² = 30.9%, P = 0.003; C vs. T, OR = 0.87, 95% CI = 0.80-0.95, I² = 38%, P = 0.001), but a significant relationship was not found in the recessive model (CC vs. CT + TT, OR = 1.09, 95% CI = 0.86-1.38, I² = 57.1%, P = 0.25). In addition, borderline statistical significance was noted between IL-1β + 3954 Ins/Del and CRC in the homozygous model, but no significance was identified for IL-1β + 3737 G/A, Il-1β + 1464 G/C, and IL-1RN + 2018 T/C under all five genetic models. In the subgroup analysis of ethnic groups, significant associations with CRC were found for IL-1β + 31 (CC vs. TT: OR = 0.82, 95% CI = 0.67-0.99, I² = 20.2%, P = 0.04; CT vs. TT: OR = 0.62, 95% CI = 0.47-0.82, I² = 0%, P < 0.001; CC + CT vs. TT: OR = 0.69, 95% CI = 0.55-0.87, I² = 29.7%, P = 0.001), IL-1β + 511 (CT vs. TT, OR = 0.65, 95% CI = 0.55-0.77, I² = 0%, P < 0.001; CC + CT vs. TT, OR = 0.67, 95% CI = 0.58-0.78, I² = 0%, P < 0.001; C vs. T, OR = 0.83, 95% CI = 0.75-0.92, I² = 49.6%, P < 0.001) and IL-1RN + 2018 T/C in the allelic contrast model (T vs. C, OR = 0.66, 95% CI = 0.44-0.98, I² = 0%, P = 0.04) among Asians but not in Caucasians. A significant association between IL-1β + 1464 G/C polymorphisms in Caucasians was observed under the recessive model (OR = 0.87, 95% CI = 0.77-0.98, I² = 45%, P = 0.03).

CONCLUSION

The current meta-analysis demonstrated that IL-1α rs3783553, IL-1β + 31C/T, IL-1β + 511C/T, and IL-1RN VNTR are critical genes for CRC susceptibility.

The Role of Interleukins in Colorectal Cancer

Despite great progress has been made in treatment strategies, colorectal cancer (CRC) remains the predominant life-threatening malignancy with the feature of high morbidity and mortality. It has been widely acknowledged that the dysfunction of immune system, including aberrantly expressed cytokines, is strongly correlated with the pathogenesis and progression of colorectal cancer. As one of the most well-known cytokines that were discovered centuries ago, interleukins are now uncovering new insights into colorectal cancer therapy. Herein, we divide currently known interleukins into 6 families, including IL-1 family, IL-2 family, IL-6 family, IL-8 family, IL-10 family and IL-17 family. In addition, we comprehensively reviewed the oncogenic or antitumour function of each interleukin involved in CRC pathogenesis and progression by elucidating the underlying mechanisms. Furthermore, by providing interleukins-associated clinical trials, we have further driven the profound prospect of interleukins in the treatment of colorectal cancer.

Deriving a Boolean dynamics to reveal macrophage activation with in vitro temporal cytokine expression profiles

BACKGROUND

Macrophages show versatile functions in innate immunity, infectious diseases, and progression of cancers and cardiovascular diseases. These versatile functions of macrophages are conducted by different macrophage phenotypes classified as classically activated macrophages and alternatively activated macrophages due to different stimuli in the complex in vivo cytokine environment. Dissecting the regulation of macrophage activations will have a significant impact on disease progression and therapeutic strategy. Mathematical modeling of macrophage activation can improve the understanding of this biological process through quantitative analysis and provide guidance to facilitate future experimental design. However, few results have been reported for a complete model of macrophage activation patterns.

RESULTS

We globally searched and reviewed literature for macrophage activation from PubMed databases and screened the published experimental results. Temporal in vitro macrophage cytokine expression profiles from published results were selected to establish Boolean network models for macrophage activation patterns in response to three different stimuli. A combination of modeling methods including clustering, binarization, linear programming (LP), Boolean function determination, and semi-tensor product was applied to establish Boolean networks to quantify three macrophage activation patterns. The structure of the networks was confirmed based on protein-protein-interaction databases, pathway databases, and published experimental results. Computational predictions of the network evolution were compared against real experimental results to validate the effectiveness of the Boolean network models.

CONCLUSION

Three macrophage activation core evolution maps were established based on the Boolean networks using Matlab. Cytokine signatures of macrophage activation patterns were identified, providing a possible determination of macrophage activations using extracellular cytokine measurements.