The prognostic significance and relationship with body composition of CCR7-positive cells in colorectal cancer

Identification of Potential Biomarkers of Platelet RNA in Glioblastoma by Bioinformatics Analysis

Objective

Glioblastoma is one of the most common and fatal malignancies in adults. Current treatment is still not optimistic. Glioblastoma (GBM) transports RNA to platelets in the blood system via microvesicles, suggesting that platelet RNA can be a potential diagnostic and therapeutic target. The roles of specific platelet RNAs in treatment of GBM are not well understood.

Methods

Platelet RNA profiling of 8 GBM and 12 normal samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified between tumors and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of up- and downregulated genes. miRNA was predicted by miRTarBase, TargetScan, and miRDB databases. circBase and circBank were used for circRNA prediction. ceRNA (circRNA-mRNA-miRNA) network was constructed to investigate the potential interactions.

Results

22 genes were upregulated and 9 genes were downregulated. There are only two genes (CCR7 and FAM102A) that connect to miRNAs (hsa-let-7a-5p, hsa-miR-1-3p). We assessed the overall survival rates by Kaplan-Meier plotter, and relative expression of GBM and subtypes for overlapped mRNA (CCR7 and FAM102A) were evaluated, and further, we obtained circRNAs (has-circ-0015164, hsa-circ-0003243) by circBank and circBase and bind sites through the CSCD database. Finally, a ceRNA network (circRNA-mRNA-miRNA) was constructed based on 2 miRNAs, 2 mRNAs, and 2 circRNAs by Cytoscape. This study focused on potential mRNA and ceRNA biomarkers to targeted treatment of GBM and provided ideas for clinical treatment through the combination of hematology and oncology.

Conclusion

The findings of this study contribute to better understand the relationship between GBM and the blood system (platelets) and might lay a solid foundation for improving GBM molecule and gene diagnosis and prognosis.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Contrast-Enhanced Computed Tomography Does Not Provide More Information about Sarcopenia than Unenhanced Computed Tomography in Patients with Pancreatic Cancer

Objective

The aim of this study was to understand whether enhanced CT can provide more information than unenhanced CT on diagnosis of sarcopenia.

Materials and Methods

We reviewed the enhanced CT data of 45 patients of pancreatic cancer. Manual tracing of the psoas muscles was used for measuring the cross-sectional muscle areas and attenuation at umbilicus level; afterwards, PMI, PMD, and Δ PMD were calculated.

Results

In the unenhanced scanning, arterial, venous, and parenchymal phases of enhanced CT, PMI values were 6.905 ± 2.170, 6.886 ± 2.195, 6.923 ± 2.239, and 6.866 ± 2.218, respectively, and the difference was not statistically significant. The PMD values at different phases were 34.311 ± 7.535, 37.487 ± 7.118, 40.689 ± 7.116, and 42.989 ± 7.745, respectively, which were gradually increased, and the difference was statistically significant. Meanwhile, the PMD of arterial phase, venous phase, and parenchyma phase showed a linear correlation with PMD of unenhanced scanning phase. 31 patients had low PMD and 14 had normal PMD during the unenhanced scanning phase. With the addition of contrast agent, ΔPMD values increased faster in the low PMD group than in the normal PMD group during the venous and parenchymal phases (7.048 ± 3.067 vs 4.893 ± 2.558; 9.581 ± 3.033 vs 6.679 ± 2.621; p < 0.05), which made the gap between PMD after contrast-enhancement vs. unenhanced scanning smaller.

Conclusion

The use of contrast agent has no effect on the manually measured PMI values but can change the results of PMD. This change makes the difference of PMD in different enhancement phases smaller than that in plain scan phase and furthermore increases the examination cost; therefore, it is not recommended to use enhanced CT routinely with fixed dose administration of contrast agent for patients' assessment of PMI and PMD.

Association between Skeletal Muscle Loss and the Response to Neoadjuvant Chemotherapy for Breast Cancer

Simple Summary

The loss of skeletal muscle mass is known to be associated with poor treatment outcome, treatment-related toxicity, and high mortality. The association between loss of skeletal muscle mass and the response to treatment is not well-defined yet. In this study, we evaluated the impact of loss of skeletal muscle mass on responsiveness to neoadjuvant chemotherapy in breast cancer. The prediction of response to neoadjuvant chemotherapy could be helpful to guide the treatment direction.

Abstract

There are no means to predict patient response to neoadjuvant chemotherapy (NAC); the impact of skeletal muscle loss on the response to NAC remains undefined. We investigated the association between response to chemotherapy and skeletal muscle loss in breast cancer patients. Patients diagnosed with invasive breast cancer who were treated with NAC, surgery, and radiotherapy were analyzed. We quantified skeletal muscle loss using pre-NAC and post-NAC computed tomography scans. The response to treatment was determined using the Response Evaluation Criteria in Solid Tumors. We included 246 patients in this study (median follow-up, 28.85 months). The median age was 48 years old (interquartile range 42–54) and 115 patients were less than 48 years old (46.7%). Patients showing a complete or partial response were categorized into the responder group (208 patients); the rest were categorized into the non-responder group (38 patients). The skeletal muscle mass cut-off value was determined using a receiver operating characteristic curve; it showed areas under the curve of 0.732 and 0.885 for the pre-NAC and post-NAC skeletal muscle index (p < 0.001 for both), respectively. Skeletal muscle loss and cancer stage were significantly associated with poor response to NAC in locally advanced breast cancer patients. Accurately measuring muscle loss to guide treatment and delaying muscle loss through various interventions would help enhance the response to NAC and improve clinical outcomes.

Assessing skeletal muscle radiodensity by computed tomography: An integrative review of the applied methodologies

Low-radiodensity skeletal muscle has been related to the degree of muscle fat infiltration and seems to be associated with worse outcomes. The aim of this study was to summarize the methodologies used to appraise skeletal muscle radiodensity by computed tomography, to describe the terms used in the literature to define muscle radiodensity and to give recommendations for its measurement standardization. An integrative bibliographic review in four databases included studies published until August 2019 in Portuguese, English or Spanish and performed in humans, adults and/or the elderly, of both sex, which investigated skeletal muscle radiodensity through computed tomography (CT) of the region between the third and fifth lumbar vertebrae and evaluated at least two muscular groups. One hundred and seventeen studies were selected. We observed a trend towards selecting all abdominal region muscle. A significant methodological variation in terms of contrast use, selection of skeletal muscle areas, radiodensity ranges delimitation and their cut-off points, as well as the terminologies used, was also found. The methodological differences detected are probably due to the lack of more precise information about the correlation between skeletal muscle radiodensity by CT and its molecular composition, among others. Therefore, until the gaps are addressed in future studies, authors should avoid arbitrary approaches when reporting skeletal muscle radiodensity, especially when it comes to prognosis inference. Studies using both CT and direct methods of muscle composition evaluation are encouraged, to enable the definition and validation of the best approach to classify fat-infiltrated muscle tissue, which will favour the nomenclature uniformization.

Association of Muscle and Adiposity Measured by Computed Tomography With Survival in Patients With Nonmetastatic Breast Cancer

Importance

Sarcopenia (low muscle mass), poor muscle quality (low muscle radiodensity), and excess adiposity derived from computed tomography (CT) has been related to higher mortality in patients with metastatic breast cancer, but the association with prognosis in patients with nonmetastatic breast cancer is unknown.

Objective

To evaluate associations of all 3 body composition measures, derived from clinically acquired CT at diagnosis, with overall mortality in nonmetastatic breast cancer.

Design, Setting, and Participants

This observational study included 3241 women from Kaiser Permanente of Northern California and Dana Farber Cancer Institute diagnosed from January 2000 to December 2013 with stages II or III breast cancer. We calculated hazard ratios (HRs) to evaluate the associations of all-cause mortality with sarcopenia, low muscle radiodensity, and total adipose tissue (TAT). Models were adjusted for sociodemographics, tumor characteristics, treatment, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), and other body composition measures. We also evaluated the cross-classification of categories of sarcopenia (yes/no) and tertiles of TAT, with outcomes.

Main Outcomes and Measures

Overall survival time and all-cause mortality.

Results

Median (range) age of 3241 women included in this study was 54 (18-80) years, and median follow-up was 6.0 years; 1086 patients (34%) presented with sarcopenia, and 1199 patients (37%) had low muscle radiodensity. Among patients with nonmetastatic breast cancer, those with sarcopenia showed higher overall mortality (HR, 1.41; 95% CI, 1.18-1.69) compared with those without sarcopenia. Patients in the highest tertile of TAT also showed higher overall mortality (HR, 1.35; 95% CI, 1.08-1.69) compared with those in the lowest tertile. Low radiodensity was not associated with survival. In analyses of sarcopenia and TAT, highest mortality was seen in patients with sarcopenia and high TAT (HR, 1.89; 95% CI, 1.30-2.73); BMI alone was not significantly related to overall mortality and did not appropriately identify patients at risk of death owing to their body composition.

Conclusions and Relevance

Sarcopenia is underrecognized in nonmetastatic breast cancer and occurs in over one-third of newly diagnosed patients. Measures of both sarcopenia and adiposity from clinically acquired CT scans in nonmetastatic patients provide significant prognostic information that outperform BMI and will help to guide interventions to optimize survival outcomes.

Prognostic value of immunoexpression of CCR4, CCR5, CCR7 and CXCR4 in squamous cell carcinoma of tongue and floor of the mouth

Background

Diverse studies have evidenced that chemokines can play a critical role in pathogenesis of oral squamous cell carcinoma (SCC). The main chemokines involved in oral carcinogenesis, tumor invasion and metastasis are CCR4, CCR5, CCR7 and CXCR4, and our aim was to evaluate the prognostic value of the immunoexpression of these chemokines in SCC of tongue and floor of the mouth.

Material and Methods

A retrospective descriptive study of the immunohistochemical expression of CCR4, CCR5, CCR7 and CXCR4 in paraffin-embedded samples of 124 patients with SCC of the tongue and floor of the mouth was performed, considering 98 cases from Brazil and 26 cases from Chile. Associations between variables were analyzed using chi-square test. Survival curves were performed using the Kaplan-Meier method and compared with long-rank test. For multivariate survival analysis, the Cox hazard model was established. The level of significance established was p≤0.05.

Results

The statistical analysis showed that samples with well or moderate WHO model differentiation (p=0.001) and a high expression of CCR5 (p=0.05) were significantly associated with a higher disease specific survival, which were also observed in Cox´s multivariate analysis (p=0.01). A higher expression of CCR7 (p=0.01) interfered significantly in disease-free survival in univariate analysis and in Cox´s multivariate analysis (p=0.05).

Conclusions

These results support additional evidence, showing that chemokine receptors CCR5 and CCR7 are helpful as biomarkers of poor prognosis in patients with SCC of the tongue and floor of the mouth.

Key words:Oral squamous cell carcinoma, prognosis, survival, chemokine receptor.

Role of liver ICAM-1 in metastasis

Intercellular adhesion molecule (ICAM)-1, is a transmembrane glycoprotein of the immunoglobulin (Ig)-like superfamily, consisting of five extracellular Ig-like domains, a transmembrane domain and a short cytoplasmic tail. ICAM-1 is expressed in various cell types, including endothelial cells and leukocytes, and is involved in several physiological processes. Furthermore, it has additionally been reported to be expressed in various cancer cells, including melanoma, colorectal cancer and lymphoma. The majority of studies to date have focused on the expression of the ICAM-1 on the surface of tumor cells, without research into ICAM-1 expression at sites of metastasis. Cancer cells frequently metastasize to the liver, due to its unique physiology and specialized liver sinusoid capillary network. Liver sinusoidal endothelial cells constitutively express ICAM-1, which is upregulated under inflammatory conditions. Furthermore, liver ICAM-1 may be important during the development of liver metastasis. Therefore, it is necessary to improve the understanding of the mechanisms mediated by this adhesion molecule in order to develop host-directed anticancer therapies.

CCL21/CCR7 interaction promotes cellular migration and invasion via modulation of the MEK/ERK1/2 signaling pathway and correlates with lymphatic metastatic spread and poor prognosis in urinary bladder cancer

Lymph node metastasis in patients with urinary bladder cancer (UBC) is always associated with poor prognosis and is the determinant for tumor staging and the development of treatment regimens; however, its underlying mechanisms remain to be studied. Immunohistochemical staining of tumor sections from 62 UBC patients was performed using CCR7, D2-40 and CD34 antibodies. We showed that increased CCR7 expression was significantly associated with positive lymph node status (P=0.008), pT3-T4 tumor stage (P=0.015), tumor grade (P=0.010) and worse overall survival (OS, P<0.001) and that both CCR7 expression and lymph node metastasis were independent prognostic factors for OS (P=0.031 and P=0.001, respectively) based on multivariate analysis. We found that there was a significant association between MLVD and lymph node status (P=0.006), but this relation was not observed for MVD. Furthermore, we showed that increased CCR7 expression correlated significantly with higher MLVD (P=0.014) and MVD (P=0.002). Wound-healing and Matrigel Transwell assays indicated that activation of CCR7 with CCL21 significantly enhanced the invasion and migration abilities of UM-UC-3 cells, and this enhanced effect was significantly abrogated by CCR7 knockdown using siRNA. Western blot analysis revealed that the phospho-ERK1/2 level was markedly increased when UM-UC-3 cells were treated with CCL21 and significantly decreased when the CCR7 gene was silenced. MEK/ERK1/2 inhibition with PD98059 significantly suppressed the migration and invasion abilities of UM-UC-3 cells and also significantly abrogated the effects of CCL21/CCR7 on cell migration and invasion. Based on these results, we conclude that activation of the CCL21/CCR7 chemoaxis promotes lymph node metastasis of UBC in at least two ways. Firstly, although CCR7 is a promoting factor that induces both lymphangiogenesis and angiogenesis, it may promote lymph node metastasis through its lymphangiogenic effect rather than through its angiogenic effect. Secondly, the CCL21/CCR7 chemoaxis promotes the migration and invasion of UBC cells via the MEK/ERK1/2 signaling pathway rather than the PI3K/AKT pathway.

Body composition measurement using computed tomography: Does the phase of the scan matter?

OBJECTIVES

The aim of this study was to determine, from the methodologic standpoint, the effect of the presence or absence of intravenous contrast on body composition variables obtained by analysis of computed tomography (CT) images.

METHODS

Triphasic abdominal (noncontrast, arterial phase, and portovenous phase contrast) CT scans from 111 patients were analyzed by two independent assessors at the third lumbar vertebral level using SliceOmatic software (version 5.0, TomoVision, Montreal, Canada). Variables included skeletal muscle index (SMI), fat and fat-free mass (FM and FFM, respectively), and mean skeletal muscle Hounsfield units (SMHU).

RESULTS

Mean SMHU was lowest in the noncontrast phase (29.4, standard deviation [SD] 8.9 HU), followed by arterial (32.4, SD 9.3 HU) then portovenous phases (34.9, SD 9.4 HU). The mean skeletal muscle attenuation was significantly different depending on the phase of the scan in which the images were obtained. Calculated FM was significantly lower in both arterial (28.6, SD 8.8 kg, P < 0.0001) and portovenous phase scans (28.5, SD 8.9 kg, P < 0.0001) when compared with noncontrast (29.2, SD 8.9 kg). The mean FFM was not significantly different as measured on noncontrast, arterial, or portovenous phase CT scans (48, SD 11.2; 48.1, SD 9.8; and 48.6, SD 10.2 kg, respectively). No difference was seen in SMI. Interobserver reliability was high.

CONCLUSIONS

The definition of myosteatosis should include a standardized phase of CT for analysis and this should be incorporated within its definition. However, as the magnitudes of the differences were relatively small, the effect of the phase of the scan on predicting outcome needs to be determined.

Prognostic value of CC-chemokine receptor seven expression in patients with metastatic renal cell carcinoma treated with tyrosine kinase inhibitor

Background

CC-chemokine receptor seven (CCR7), a G-protein coupled receptor normally facilitating immune cells lymphatic homing, has recently been identified on several cancer cells in promoting invasion and lymphatic specific metastasis by mimicking normal leukocytes. As tyrosine kinase inhibitors for metastatic renal cell carcinoma (mRCC) mostly emphasized on vascular inhibition, whether the CCR7 expressing tumor cells with potential lymphatic invasion function could have an impact on mRCC patient’s drug response and survival, was unknown.

Methods

In this study, in a clinical aspect, we retrospectively investigated the prognostic and predictive impact of tumoral CCR7 expression in 110 mRCC patients treated with sunitinib and sorafenib, and its correlation with pre- or post-administration lymphatic involvement. Immunohistochemistry on tissue microarrays were conducted for CCR7 expression evaluation.

Results

Kaplan-Meier and univariate analyses suggested high tumoral CCR7 expression as an adverse prognosticator for mRCC patients’ overall survival (OS), which was further confirmed in the multivariate analyses (P = 0.002, P = 0.003 for bootstrap). This molecule could be combined with Heng’s risk model for better patient OS prediction. High tumoral CCR7 expression was also an independent dismal predictor for patients’ progression free survival (PFS) (P = 0.010, P = 0.013 for bootstrap), and correlated with poorer best drug response. Moreover, a possible correlation of CCR7 high expression and patients’ baseline and post-administration lymph node metastasis was found.

Conclusions

High tumoral CCR7 expression correlated with potential lymphatic involvement and poor prognosis of mRCC patients treated with tyrosine kinase inhibitors. Further external validations and basic researches were needed to confirm these results.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3065-3) contains supplementary material, which is available to authorized users.

Transferring the C-terminus of the chemokine CCL21 to CCL19 confers enhanced heparin binding

Chemokines direct the migration of cells during various immune processes and are involved in many disease states. For example, CCL19 and CCL21, through activation of the CCR7 receptor, recruit dendritic cells and naïve T-cells to the secondary lymphoid organs aiding in balancing immune response and tolerance. However, CCL19 and CCL21 can also direct the metastasis of CCR7 expressing cancers. Chemokine binding to glycosaminoglycans, such as heparin, is as important to chemokine function as receptor activation. CCL21 is unique in that it contains an extended C-terminus not found in other chemokines like CCL19. Deletion of this extended C-terminus reduces CCL21's affinity for heparin and transferring the CCL21 C-terminus to CCL19 enhances heparin binding mainly through non-specific, electrostatic interactions.