Network cluster analysis of protein-protein interaction network identified biomarker for early onset colorectal cancer

Genes adaptability and NOL6 protein inhibition studies of fabricated flavan-3-ols lead skeleton intended to treat breast carcinoma

Breast cancer invasive 2.3 million women worldly and second prominent factor of cancer-related mortality. Finding a new site-specific and safe small molecule is a current need in this field. With the aid of deep learning Algorithms, we analyzed the published big database from cancer CBioportal to find the best target protein. Further, Multi-omics analysis such as enrichment analysis, scores of molecular, RNA biological function at a cellular level, and protein domain were obtained and matched to find the better hit molecules. The gene analysis output shows nucleolar protein 6 plays a significant responsibility in breast carcinoma and 354 natural and synthetic lead molecules are docked inside the active site. Docking result gave the output hit molecule falavan-3-ols with a binding score of -5.325 (Kcal/mol) and interaction analysis illustrates, 13 active amino acids favoring the binding interaction with functional groups of the hit molecule compared to the standard molecule Abemacilib (-2.857 (Kcal/mol)). Best docked complex of flavan-3-ols and NOL6 protein subjected to dynamic simulation 100 ns to study the stability. The results proved that π-π stacked, carbon‑hydrogen and electrostatic interactions are stable throughout the 100 ns simulation. The overall results conclude the hit molecule flavan-3-ol will be a safe and potent lead molecule to generate and treat breast carcinoma patients.

A cluster of blood-based protein biomarkers associated with decreased cerebral blood flow relates to future cardiovascular events in patients with cardiovascular disease

Biological processes underlying decreased cerebral blood flow (CBF) in patients with cardiovascular disease (CVD) are largely unknown. We hypothesized that identification of protein clusters associated with lower CBF in patients with CVD may explain underlying processes. In 428 participants (74% cardiovascular diseases; 26% reference participants) from the Heart-Brain Connection Study, we assessed the relationship between 92 plasma proteins from the Olink® cardiovascular III panel and normal-appearing grey matter CBF, using affinity propagation and hierarchical clustering algorithms, and generated a Biomarker Compound Score (BCS). The BCS was related to cardiovascular risk and observed cardiovascular events within 2-year follow-up using Spearman correlation and logistic regression. Thirteen proteins were associated with CBF (ρSpearman range: -0.10 to -0.19, pFDR-corrected <0.05), and formed one cluster. The cluster primarily reflected extracellular matrix organization processes. The BCS was higher in patients with CVD compared to reference participants (pFDR-corrected <0.05) and was associated with cardiovascular risk (ρSpearman 0.42, p < 0.001) and cardiovascular events (OR 2.05, p < 0.01). In conclusion, we identified a cluster of plasma proteins related to CBF, reflecting extracellular matrix organization processes, that is also related to future cardiovascular events in patients with CVD, representing potential targets to preserve CBF and mitigate cardiovascular risk in patients with CVD.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Testis-expressed gene 11 inhibits cisplatin-induced DNA damage and contributes to chemoresistance in testicular germ cell tumor

Testicular germ cell tumor (TGCT) is a rare cancer but the most common tumor among adolescent and young adult males. Patients with advanced TGCT often exhibit a worse prognosis due to the acquisition of therapeutic resistance. Cisplatin-based chemotherapy is a standard treatment for advanced TGCTs initially sensitive to cisplatin, as exemplified by embryonal carcinoma. The acquisition of cisplatin resistance, however, could be a fatal obstacle for TGCT management. To identify cisplatin resistance-related genes, we performed transcriptome analysis for cisplatin-resistant TGCT cells compared to parental cells. In two types of cisplatin-resistant TGCT cell models that we established from patient-derived TGCT cells, and from the NEC8 cell line, we found that mRNA levels of the high-mobility-group nucleosome-binding gene HMGN5 and meiosis-related gene TEX11 were remarkably upregulated compared to those in the corresponding parental cells. We showed that either HMGN5 or TEX11 knockdown substantially reduced the viability of cisplatin-resistant TGCT cells in the presence of cisplatin. Notably, TEX11 silencing in cisplatin-resistant TGCT cells increased the level of cleaved PARP1 protein, and the percentage of double-strand break marker γH2AX-positive cells. We further demonstrated the therapeutic efficiency of TEX11-specific siRNA on in vivo xenograft models derived from cisplatin-resistant patient-derived TGCT cells. Taken together, the present study provides a potential insight into a mechanism of cisplatin resistance via TEX11-dependent pathways that inhibit apoptosis and DNA damage. We expect that our findings can be applied to the improvement of cisplatin-based chemotherapy for TGCT, particularly for TEX11-overexpressing tumor.

A cluster of blood-based protein biomarkers reflecting coagulation relates to the burden of cerebral small vessel disease

Biological processes underlying cerebral small vessel disease (cSVD) are largely unknown. We hypothesized that identification of clusters of inter-related bood-based biomarkers that are associated with the burden of cSVD provides leads on underlying biological processes. In 494 participants (mean age 67.6 ± 8.7 years; 36% female; 75% cardiovascular diseases; 25% reference participants) we assessed the relation between 92 blood-based biomarkers from the OLINK cardiovascular III panel and cSVD, using cluster-based analyses. We focused particularly on white matter hyperintensities (WMH). Nineteen biomarkers individually correlated with WMH ratio (r range: 0.16-0.27, Bonferroni corrected p-values <0.05), of which sixteen biomarkers formed one biomarker cluster. Pathway analysis showed that this biomarker cluster predominantly reflected coagulation processes. This cluster related also significantly to other cSVD manifestations (lacunar infarcts, microbleeds, and enlarged perivascular spaces), which supports generalizability beyond WMHs. To study possible causal effects of biological processes reflected by the cluster we performed a mediation analysis that showed a mediation effect of the cluster on the relation between age and WMH ratio (proportion mediated 17%), and hypertension and WMH-volume (proportion mediated 21%). In conclusion, we identified a cluster of blood-based biomarkers reflecting coagulation, that is related to manifestations of cSVD, corroborating involvement of coagulation abnormalities in the etiology of cSVD.

A Gene-Based Machine Learning Classifier Associated to the Colorectal Adenoma-Carcinoma Sequence

Colorectal cancer (CRC) carcinogenesis is generally the result of the sequential mutation and deletion of various genes; this is known as the normal mucosa–adenoma–carcinoma sequence. The aim of this study was to develop a predictor-classifier during the “adenoma-carcinoma” sequence using microarray gene expression profiles of primary CRC, adenoma, and normal colon epithelial tissues. Four gene expression profiles from the Gene Expression Omnibus database, containing 465 samples (105 normal, 155 adenoma, and 205 CRC), were preprocessed to identify differentially expressed genes (DEGs) between adenoma tissue and primary CRC. The feature selection procedure, using the sequential Boruta algorithm and Stepwise Regression, determined 56 highly important genes. K-Means methods showed that, using the selected 56 DEGs, the three groups were clearly separate. The classification was performed with machine learning algorithms such as Linear Model (LM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN). The best classification method in terms of accuracy (88.06 ± 0.70) and AUC (92.04 ± 0.47) was k-NN. To confirm the relevance of the predictive models, we applied the four models on a validation cohort: the k-NN model remained the best model in terms of performance, with 91.11% accuracy. Among the 56 DEGs, we identified 17 genes with an ascending or descending trend through the normal mucosa–adenoma–carcinoma sequence. Moreover, using the survival information of the TCGA database, we selected six DEGs related to patient prognosis (SCARA5, PKIB, CWH43, TEX11, METTL7A, and VEGFA). The six-gene-based classifier described in the current study could be used as a potential biomarker for the early diagnosis of CRC.

Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications

Introduction

The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier.

Methods

Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features.

Results

Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes.

Conclusion

In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.

The Importance of Sex in the Discovery of Colorectal Cancer Prognostic Biomarkers

Colorectal cancer (CRC) is the third leading cause of cancer deaths. Advances within bioinformatics, such as machine learning, can improve biomarker discovery and ultimately improve CRC survival rates. There are clear sex differences in CRC characteristics, but the impact of sex has not been considered with regards to CRC biomarkers. Our aim here was to investigate sex differences in the transcriptome of a normal colon and CRC, and between paired normal and tumor tissue. Next, we attempted to identify CRC diagnostic and prognostic biomarkers and investigate if they are sex-specific. We collected paired normal and tumor tissue, performed RNA-seq, and applied feature selection in combination with machine learning to identify the top CRC diagnostic biomarkers. We used The Cancer Genome Atlas (TCGA) data to identify sex-specific CRC diagnostic biomarkers and performed an overall survival analysis to identify sex-specific prognostic biomarkers. We found transcriptomic sex differences in both the normal colon tissue and in CRC. Forty-four of the top-ranked biomarkers were sex-specific and 20 biomarkers showed a sex-specific prognostic value. Our data show the importance of sex in the discovery of CRC biomarkers. We propose 20 sex-specific CRC prognostic biomarkers, including ESM1, GUCA2A, and VWA2 for males and CLDN1 and FUT1 for females.

A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions

It is meaningful to assess the risk of cancer incidence among patients with precancerous colorectal lesions. Comparing the within-sample relative expression orderings (REOs) of colorectal cancer patients measured by multiple platforms with that of normal colorectal tissues, a qualitative transcriptional signature consisting of 1,840 gene pairs was identified in the training data. Within an evaluation dataset of 16 active and 18 inactive (remissive) ulcerative colitis subjects, the median incidence risk score of colorectal carcinoma was 0.6402 in active ulcerative colitis subjects, significantly higher than that in remissive subjects (0.3114). Evaluation of two other independent datasets yielded similar results. Moreover, we found that the score significantly positively correlated with the degree of dysplasia in the case of colorectal adenomas. In the merged dataset, the median incidence risk score was 0.9027 among high-grade adenoma samples, significantly higher than that among low-grade adenomas (0.8565). In summary, the developed incidence risk score could well predict the incidence risk of precancerous colorectal lesions and has value in clinical application.

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes

Type 2 diabetes (T2D) is known as a disease caused by gene alterations characterized by insulin resistance, thus the insulin-responsive tissues are of great interest for T2D study. It’s of great relevance to systematically investigate commonalities and specificities of T2D among those tissues. Here we establish a multi-level comparative framework across three insulin target tissues (white adipose, skeletal muscle, and liver) to provide a better understanding of T2D. Starting from the ranks of gene expression, we constructed the ‘disease network’ through detecting diverse interactions to provide a well-characterization for disease affected tissues. Then, we applied random walk with restart algorithm to the disease network to prioritize its nodes and edges according to their association with T2D. Finally, we identified a merged core module by combining the clustering coefficient and Jaccard index, which can provide elaborate and visible illumination of the common and specific features for different tissues at network level. Taken together, our network-, gene-, and module-level characterization across different tissues of T2D hold the promise to provide a broader and deeper understanding for T2D mechanism.

The miR-17-92 cluster/QKI2/β-catenin axis promotes osteosarcoma progression

Quaking(QKI) is an RNA binding protein, and it has been shown to serve as a tumor suppressor. However, the expression and functions of QKI in osteosarcoma progression remain poorly understood. In this study, we aimed to explore the expression of QKI2 in osteosarcoma tissues and to determine the mechanisms underlying aberrant QKI2 expression and the effect of QKI2 on osteosarcoma progression. We found that QKI2 was significantly down-regulated in osteosarcoma tissues compared with adjacent normal bone tissues. Using a series of molecular biological techniques, we demonstrated that all members of the miR-17-92 cluster were up-regulated and contributed to the down-regulation of QKI2 expression in osteosarcoma. Functional examination showed that QKI2 inhibited the proliferation, migration and invasion of osteosarcoma cells via decreasing the expression of β-catenin. Conclusively, we revealed that the regulatory axis consisting of the miR-17-92 cluster/QKI2/β-catenin plays a crucial role in the development and progression of osteosarcoma.

Construction of key signal regulatory network in metastatic colorectal cancer

There are many stages in the development and metastasis of colorectal cancer (CRC). In this study, we compared the differential expression genes in different stages of metastatic CRC. Then, we screened the continuously up-regulated genes and the continuously down-regulated genes that were associated with the development and metastasis of CRC. After analyzing the intersection of differential expression genes in each stage, we screened the continuously up-regulated genes and deviated genes in the extracellular matrix and the continuously down-regulated genes and deviated genes in the mitochrondia of CRC. Then, we performed gene ontology enrichment analysis of the deviated genes in different phases, and we found that key molecular events occurred in the period extending from stage II to III (early stage of metastasis) of CRC. Furthermore, in this period we found that the chemotaxis of inflammatory cells had decreased in the extracellular matrix. On the other hand, the aerobic respiration had increased in the mitochondrion. Then, we constructed protein-protein interaction network of deviated genes in the extracellular matrix and mitochondrion. We used the network module and hub network to analyze the protein-protein interaction network. The network module analysis showed that the protein complex of VEGFA and CCL7-CCR3 is the key node in the extracellular matrix, while MAPK1 is the key node in the mitochondrion. The hub network analysis showed that the signal transmission chain FN1→SPARC→COL1A1→MMP2 is the key regulatory pathway for extracellular signal transmission. Furthermore, it also showed that CAV1→MAPK3→RAF1→NR3C1→MAPK1→ESR1 is the key regulatory pathway for signal transmission in mitochondrion.

Interacting partners of FEN1 and its role in the development of anticancer therapeutics

Protein-protein interaction (PPI) plays a key role in cellular communication, Protein-protein interaction connected with each other with hubs and nods involved in signaling pathways. These interactions used to develop network based biomarkers for early diagnosis of cancer. FEN1(Flap endonuclease 1) is a central component in cellular metabolism, over expression and decrease of FEN1 levels may cause cancer, these regulation changes of Flap endonuclease 1reported in many cancer cells, to consider this data may needs to develop a network based biomarker. The current review focused on types of PPI, based on nature, detection methods and its role in cancer. Interacting partners of Flap endonuclease 1 role in DNA replication repair and development of anticancer therapeutics based on Protein-protein interaction data.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis

Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis strategy. The biomarker-target interrelated molecules associated with diabetes and its complications were identified via the Comparative Toxicogenomics Database (CTD); the Search Tool for Recurring Instances of Neighboring Genes was utilized for network construction. Functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery software to investigate connections between diabetes and its complications. A total of 142 (including 122 biomarkers, 10 therapeutic targets and 10 overlapping molecules) biomarker-target interrelated molecules associated with diabetes and its complications were identified via the CTD database, and analysis of the network yielded 1,087 biological processes and fifteen Kyoto Encyclopedia of Genes and Genomes pathways with significant P‑values. Various critical aspects of the networks were examined in the present study: a) Intermolecular horizontal and vertical combinations in biomarkers and therapeutic targets associated with diabetes and its complicationb) network topology properties associated with molecular pathological responsec) contribution of key molecules to integrated regulation; and d) crosstalk between multiple pathways. Based on a multi-dimensional analysis, it was concluded that the integrated molecular pathological development of diabetes and its complications does not proceed randomly, which suggests a requirement for integrated, multi-target intervention.

Sporadic early-onset colon cancer expresses unique molecular features

BACKGROUND

The overall incidence of colon cancer (CC) has steadily declined in the last decades but has increased in patients under age 50 y. The etiology of early-onset (EO) CC is not understood. The aim of this study was to elucidate gene expression patterns in EOCC and show its uniqueness compared to late-onset (LO) disease.

METHODS

Two cohorts of patients with sporadic CC were identified. Tumors and matching noninvolved tissues from six EOCC patients (<50) and six late-onset colon cancers (LOCC) patients (>65) were obtained from pathology archives. De-paraffinized tissues were macrodissected from FFPE sections, RNA isolated, and used for expression profiling of 770 cancer-related genes representing 13 canonical pathways.

RESULTS

Among 770 genes assayed, changes in expression levels of 93 genes were statistically significant between EOCC and matching noninvolved tissues. There were also significant differences in expression levels of 118 genes between LOCC and matching noninvolved tissues. Detailed comparative gene expression analysis between EOCC and LOCC normalized to their matching noninvolved tissues revealed that changes in expression of 88 genes were unique to EOCC using the cutoff criteria of expression levels difference >2 fold and P value <0.01. From these differentially expressed genes specific to EOCC, 28 genes were upregulated and 60 genes downregulated. At the pathway level, RAS, MAPK, WNT, and DNARepair pathways were similarly deregulated in both age groups, whereas PI3K-AKT signaling was more specific to EOCC and cell cycle pathway to LOCC.

CONCLUSIONS

These results suggest that sporadic EOCC is characterized by distinct molecular events compared to LOCC.

Molecular approach to genetic and epigenetic pathogenesis of early-onset colorectal cancer

Colorectal cancer (CRC) is the third most frequent cancer type and the incidence of this disease is increasing gradually per year in individuals younger than 50 years old. The current knowledge is that early-onset CRC (EOCRC) cases are heterogeneous population that includes both hereditary and sporadic forms of the CRC. Although EOCRC cases have some distinguishing clinical and pathological features than elder age CRC, the molecular mechanism underlying the EOCRC is poorly clarified. Given the significance of CRC in the world of medicine, the present review will focus on the recent knowledge in the molecular basis of genetic and epigenetic mechanism of the hereditary forms of EOCRC, which includes Lynch syndrome, Familial CRC type X, Familial adenomatous polyposis, MutYH-associated polyposis, Juvenile polyposis syndrome, Peutz-Jeghers Syndrome and sporadic forms of EOCRC. Recent findings about molecular genetics and epigenetic basis of EOCRC gave rise to new alternative therapy protocols. Although exact diagnosis of these cases still remains complicated, the present review paves way for better predictions and contributes to more accurate diagnostic and therapeutic strategies into clinical approach.

Drug-target networks for Tanshinone IIA identified by data mining

Tanshinone IIA is a pharmacologically active compound isolated from Danshen (Salvia miltiorrhiza), a traditional Chinese herbal medicine for the management of cardiac diseases and other disorders. But its underlying molecular mechanisms of action are still unclear. The present investigation utilized a data mining approach based on network pharmacology to uncover the potential protein targets of Tanshinone IIA. Network pharmacology, an integrated multidisciplinary study, incorporates systems biology, network analysis, connectivity, redundancy, and pleiotropy, providing powerful new tools and insights into elucidating the fine details of drug-target interactions. In the present study, two separate drug-target networks for Tanshinone IIA were constructed using the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals) methods. Analysis of the ALS-constructed network revealed a target network with a scale-free topology and five top nodes (protein targets) corresponding to Fos, Jun, Src, phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), and mitogen-activated protein kinase kinase 1 (MAP2K1), whereas analysis of the STITCH-constructed network revealed three top nodes corresponding to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and nuclear factor kappa B1 (NFκB1). The discrepancies were probably due to the differences in the divergent computer mining tools and databases employed by the two methods. However, it is conceivable that all eight proteins mediate important biological functions of Tanshinone IIA, contributing to its overall drug-target network. In conclusion, the current results may assist in developing a comprehensive understanding of the molecular mechanisms and signaling pathways of in a simple, compact, and visual manner.

Time-dependent variation of pathways and networks in a 24-hour window after cerebral ischemia-reperfusion injury

Background

Cerebral ischemia-reperfusion injury may simultaneously result in functional variation of multiple genes/pathways. However, most prior time-sequence studies on its pathomechanism only focused on a single gene or pathway. Our study aimed to systematically analyze the time-dependent variation in the expression of multiple pathways and networks within 24 h after cerebral ischemia-reperfusion injury.

Results

By uploading 374 ischemia-related genes into the MetaCore software, the variation in the expression of multiple pathways and networks in 3 h, 12 h, and 24 h after cerebral ischemia-reperfusion injury had been analyzed. The conserved TNFR1-signaling pathway, among the top 10 pathways, was consistently enriched in 3 h, 12 h, and 24 h groups. Three overlapping pathways were found between 3 h and 12 h groups; 2 between 12 h and 24 h groups; and 1 between 3 h and 24 h groups. Five, 4, and 6 non-overlapping pathways were observed in 3 h, 12 h, and 24 h groups, respectively. Apart from pathways reported by earlier studies, we identified a novel pathway related to the time-dependent development of cerebral ischemia pathogenesis. The process of apoptosis stimulation by external signals, among the top 10 processes, was consistently enriched in 3 h, 12 h, and 24 h groups; 2, 1, and 2 processes overlapped between 3 h and 12 h groups, 12 h and 24 h groups, and 3 h and 24 h groups, respectively. Four, 5, and 5 non-overlapping processes were found in 3 h, 12 h and 24 h groups, respectively. The presence of apoptotic processes was observed in all the 3 groups; while anti-apoptotic processes only existed in 3 h and 12 h groups. Additionally, according to node degree, network comparison identified 1, 8,and 5 important genes or proteins (e.g. Pyk2, PKC, E2F1, and VEGF-A) in 3 h, 12 h, and 24 h groups, respectively. The Jaccard similarity index revealed a higher level of similarity between 12 h and 24 h groups than that between 3 h and 12 h groups.

Conclusion

Time-dependent treatment can be utilized to reduce apoptosis, which may activate anti-apoptotic pathways within 12 h after cerebral ischemia-reperfusion injury. Pathway and network analyses may help identify novel pathways and genes implicated in disease pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0152-4) contains supplementary material, which is available to authorized users.

Loss of stromal caveolin-1 expression in colorectal cancer predicts poor survival

AIM: To investigate the clinicopathological significance and prognostic value of caveolin-1 (CAV-1) in both tumor and stromal cells in colorectal cancer (CRC).

METHODS: A total of 178 patients with CRC were included in this study. The correlation between CAV-1 expression and clinicopathologic features and survival was studied.

RESULTS: CAV-1 expression was detected in tumor and stromal cells. The expression of stromal CAV-1 was closely associated with histological type (P = 0.022), pathologic tumor-node-metastasis stage (P = 0.047), pathologic N stage (P = 0.035) and recurrence (P = 0.000). However, tumor cell CAV-1 did not show any correlation with clinical parameters. Additionally, the loss of stromal CAV-1 expression was associated with shorter disease-free survival (P = 0.000) and overall survival (P = 0.000). Multivariate analysis revealed that the loss of stromal CAV-1 expression was an independent prognostic factor for both overall survival (P = 0.014) and disease-free survival (P = 0.006).

CONCLUSION: The loss of stromal CAV-1 expression in CRC was associated with poor prognosis and could be a prognostic factor for CRC patients.

Early-onset colorectal cancer: A sporadic or inherited disease?

Colorectal cancer is the third most common cancer diagnosed worldwide. Although epidemiology data show a marked variability around the world, its overall incidence rate shows a slow but steady decrease, mainly in developed countries. Conversely, early-onset colorectal cancer appears to display an opposite trend with an overall prevalence in United States and European Union ranging from 3.0% and 8.6%. Colorectal cancer has a substantial proportion of familial cases. In particular, early age at onset is especially suggestive of hereditary predisposition. The clinicopathological and molecular features of colorectal cancer cases show a marked heterogeneity not only between early- and late-onset cases but also within the early-onset group. Two distinct subtypes of early-onset colorectal cancers can be identified: a “sporadic” subtype, usually without family history, and an inherited subtype arising in the context of well defined hereditary syndromes. The pathogenesis of the early-onset disease is substantially well characterized in the inherited subtype, which is mainly associated to the Lynch syndrome and occasionally to other rare mendelian diseases, whereas in the “sporadic” subtype the origin of the disease may be attributed to the presence of various common/rare genetic variants, so far largely unidentified, displaying variable penetrance. These variants are thought to act cumulatively to increase the risk of colorectal cancer, and presumably to also anticipate its onset. Efforts are ongoing in the attempt to unravel the intricate genetic basis of this “sporadic” early-onset disease. A better knowledge of molecular entities and pathways may impact on family-tailored prevention and clinical management strategies.