Predictive and prognostic biomarkers in personalized gastrointestinal cancer treatment

A network-based framework to discover treatment-response-predicting biomarkers for complex diseases

Precision medicine's potential to transform complex autoimmune-disease treatment is often challenged by limited data availability and inadequate sample size when compared to the number of molecular features found in high-throughput multi-omics datasets. Addressing this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. ProBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. ProBeNet prioritizes biomarkers by considering (1) therapy-targeted proteins, (2) disease-specific molecular signatures, and (3) an underlying network of interactions among cellular components (the human interactome). With ProBeNet, biomarkers were discovered predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a MAPK3/1 inhibitor). Predictive power of ProBeNet biomarkers was validated with retrospective gene-expression data from ulcerative-colitis and rheumatoid-arthritis patients and prospective data from ulcerative-colitis and Crohn's disease patient-derived tissues. Machine-learning models using ProBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited (fewer than 20 samples). These results illustrate the value of ProBeNet for reducing features and for constructing robust machine-learning models when limited data are available. ProBeNet may be used to develop companion and complementary diagnostic assays for complex autoimmune-disease therapies, which may help stratify suitable patient subgroups in clinical trials, approve new drugs, and improve patient outcomes.

Biomarkers for prognosis of meningioma patients: A systematic review and meta-analysis

Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.

Altered immunoexpression of DNA polymerase delta 1 catalytic subunit (POLD1) in colorectal cancer

Introduction

The study aimed to determine the immunoexpression levels of polymerase delta 1 catalytic subunit (POLD1), a catalytic and proofreading subunit of DNA polymerase delta, in the sections of colorectal cancer (CRC), and to evaluate the significance of POLD1 as a potential prognostic factor in CRC.

Material and methods

Paired, tumour and non-cancerous tissue samples of the large intestine distant to the neoplasm were collected from the postoperative material of 78 patients who underwent surgical resection of CRC tumours. Polymerase delta 1 catalytic subunit protein levels were determined using immunohistochemistry. Clinical, pathomorphological, and survival data of the patients were pooled. In addition, POLD1 mRNA expression levels of 599 CRC patients were extracted from The Cancer Genome Atlas (TCGA) datasets and subjected to statistical and survival analysis including the Kaplan-Meier method followed by the log-rank test.

Results

Immunoexpression of POLD1 was found in the nuclei of the tumour cells and epithelial cells of unchanged intestinal mucosa. Polymerase delta 1 catalytic subunit immunoreactivity in the tumour was heterogenous, and the average immunoreactivity score was decreased in cancer cells when compared to the mucosa of matched sections of unchanged large intestine (p = 0.0259). However, POLD1 expression at the protein and mRNA levels did not associate with clinicopathological characteristics of the patients and their survival.

Conclusions

Despite previous studies suggesting that POLD1 genetic alterations could be promising molecular biomarkers in CRC, our results do not support any prognostic significance of POLD1 expression in CRC.

Quantitative PET-based biomarkers in lymphoma: getting ready for primetime

The use of functional quantitative biomarkers extracted from routine PET-CT scans to characterize clinical responses in patients with lymphoma is gaining increased attention, and these biomarkers can outperform established clinical risk factors. Total metabolic tumour volume enables individualized estimation of survival outcomes in patients with lymphoma and has shown the potential to predict response to therapy suitable for  risk-adapted treatment approaches in clinical trials. The deployment of machine learning tools in molecular imaging research can assist in recognizing complex patterns and, with image classification, in tumour identification and segmentation of data from PET-CT scans. Initial studies using fully automated approaches to calculate metabolic tumour volume and other PET-based biomarkers have demonstrated appropriate correlation with calculations from experts, warranting further testing in large-scale studies. The extraction of computer-based quantitative tumour characterization through radiomics can provide a comprehensive view of phenotypic heterogeneity that better captures the molecular and functional features of the disease. Additionally, radiomics can be integrated with genomic data to provide more accurate prognostic information. Further improvements in PET-based biomarkers are imminent, although their incorporation into clinical decision-making currently has methodological shortcomings that need to be addressed with confirmatory prospective validation in selected patient populations. In this Review, we discuss the current knowledge, challenges and opportunities in the integration of quantitative PET-based biomarkers in clinical trials and the routine management of patients with lymphoma.

A novel adenosine signalling-based prognostic signature in gastric cancer and its association with cancer immune features and immunotherapy response

Reliable prognostic signatures that can reflect the intrinsic characteristics of gastric cancer (GC) are still rare. Here, we developed an adenosine-based prognostic signature and explored its association with the tumour immune in GC patients, aiming at confirming the prognostic value of adenosine-related genes and guiding the GC risk stratification and immunotherapeutic response prediction. We collected adenosine pathway-related genes from STRING websites and manual searching. We enrolled the The Cancer Genome Atlas cohort and four gene expression omnibus cohorts of GC for generating and validating the adenosine pathway-based signature using the Cox regression method. Gene expression in the signature was verified using polymerase chain reaction. We also performed gene set enrichment analysis, immune infiltration assessment and immunotherapy response prediction based on this signature. Our study resulted in a six-gene adenosine signature (GNAS, CXCR4, PPP1R1B, ADCY6, NT5E and NOS3) for risk stratification of GC prognosis, with the highest area under the receiver operating characteristic curve up to 0.767 for predicting 10-year overall survival (OS). In the training cohort, patients with signature-defined high risk had significantly poorer OS than those with low risk (p < .001). Multivariate analysis identified the signature as an independent prognostic factor (hazard ratio 2.863, 95% confidence interval [1.871-4.381], p < .001). These findings were confirmed in four independent cohorts. Expression detection showed that all signature genes were upregulated in both GC tissues and cell lines. Further analysis revealed that the signature-defined high-risk patients were characterised by immunosuppressive states and associated with a poor immunotherapy response. In conclusion, the adenosine pathway-based signature represents a promising risk stratification tool for GC in guiding individualised prognostication and immunotherapy.

Unveiling the interplay between mutational signatures and tumor microenvironment: a pan-cancer analysis

Background

While recent studies have separately explored mutational signatures and the tumor microenvironment (TME), there is limited research on the associations of both factors in a pan-cancer context.

Materials and methods

We performed a pan-cancer analysis of over 8,000 tumor samples from The Cancer Genome Atlas (TCGA) project. Machine learning methods were employed to systematically explore the relationship between mutational signatures and TME and develop a risk score based on TME-associated mutational signatures to predict patient survival outcomes. We also constructed an interaction model to explore how mutational signatures and TME interact and influence cancer prognosis.

Results

Our analysis revealed a varied association between mutational signatures and TME, with the Clock-like signature showing the most widespread influence. Risk scores based on mutational signatures mainly induced by Clock-like and AID/APOBEC activity exhibited strong pan-cancer survival stratification ability. We also propose a novel approach to predict transcriptome decomposed infiltration levels using genome-derived mutational signatures as an alternative approach for exploring TME cell types when transcriptome data are unavailable. Our comprehensive analysis revealed that certain mutational signatures and their interaction with immune cells significantly impact clinical outcomes in particular cancer types. For instance, T cell infiltration levels only served as a prognostic biomarker in melanoma patients with high ultraviolet radiation exposure, breast cancer patients with high homologous recombination deficiency signature, and lung adenocarcinoma patients with high tobacco-associated mutational signature.

Conclusion

Our study comprehensively explains the complex interplay between mutational signatures and immune infiltration in cancer. The results highlight the importance of considering both mutational signatures and immune phenotypes in cancer research and their significant implications for developing personalized cancer treatments and more effective immunotherapy.

Novel variants provide differential stabilisation of human equilibrative nucleoside transporter 1 states

Human equilibrative nucleoside transporters represent a major pharmaceutical target for cardiac, cancer and viral therapies. Understanding the molecular basis for transport is crucial for the development of improved therapeutics through structure-based drug design. ENTs have been proposed to utilise an alternating access mechanism of action, similar to that of the major facilitator superfamily. However, ENTs lack functionally-essential features of that superfamily, suggesting that they may use a different transport mechanism. Understanding the molecular basis of their transport requires insight into diverse conformational states. Differences between intermediate states may be discrete and mediated by subtle gating interactions, such as salt bridges. We identified four variants of human equilibrative nucleoside transporter isoform 1 (hENT1) at the large intracellular loop (ICL6) and transmembrane helix 7 (TM7) that stabilise the apo-state (∆Tm 0.7–1.5°C). Furthermore, we showed that variants K263A (ICL6) and I282V (TM7) specifically stabilise the inhibitor-bound state of hENT1 (∆∆Tm 5.0 ± 1.7°C and 3.0 ± 1.8°C), supporting the role of ICL6 in hENT1 gating. Finally, we showed that, in comparison with wild type, variant T336A is destabilised by nitrobenzylthioinosine (∆∆Tm -4.7 ± 1.1°C) and binds it seven times worse. This residue may help determine inhibitor and substrate sensitivity. Residue K263 is not present in the solved structures, highlighting the need for further structural data that include the loop regions.

Marine-Inspired Drugs and Biomaterials in the Perspective of Pancreatic Cancer Therapies

Despite its low prevalence, pancreatic cancer (PC) is one of the deadliest, typically characterised as silent in early stages and with a dramatically poor prognosis when in its advanced stages, commonly associated with a high degree of metastasis. Many efforts have been made in pursuing innovative therapeutical approaches, from the search for new cytotoxic drugs and other bioactive compounds, to the development of more targeted approaches, including improved drug delivery devices. Marine biotechnology has been contributing to this quest by providing new chemical leads and materials originating from different organisms. In this review, marine biodiscovery for PC is addressed, particularly regarding marine invertebrates (namely sponges, molluscs, and bryozoans), seaweeds, fungi, and bacteria. In addition, the development of biomaterials based on marine-originating compounds, particularly chitosan, fucoidan, and alginate, for the production of advanced cancer therapies, is also discussed. The key role that drug delivery can play in new cancer treatments is highlighted, as therapeutical outcomes need to be improved to give further hope to patients.

Model-free screening for variables with treatment interaction

Precision medicine is a medical paradigm that focuses on making effective treatment decision based on individual patient characteristics. When there are a large amount of patient information, such as patient’s genetic information, medical records and clinical measurements, available, it is of interest to select the covariates which have interactions with the treatment, for example, in determining the individualized treatment regime where only a subset of covariates with treatment interactions involves in decision making. We propose a marginal feature ranking and screening procedure for measuring interactions between the treatment and covariates. The method does not require imposing a specific model structure on the regression model and is applicable in a high dimensional setting. Theoretical properties in terms of consistency in ranking and selection are established. We demonstrate the finite sample performance of the proposed method by simulation and illustrate the applications with two real data examples from clinical trials.

Associations of Complete Blood Count Parameters with Disease-Free Survival in Right- and Left-Sided Colorectal Cancer Patients

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Some complete blood count (CBC) parameters are found to be associated with CRC prognosis. In this study, ninety-seven pretreated CRC patients were included, and the patients were divided into two groups: left-sided and right-sided, depending on the anatomical location of the tumor. Based on clinicopathologic features including tumor budding, disease stages, and tumor anatomical location, levels of CBC parameters were compared, and disease-free survivals (DFS) were determined. There were differences between patients with different tumor budding scores for only three parameters, including red cell distribution width (RDW), numbers of platelets, and mean platelet volume (MPV). Furthermore, numbers of WBCs, monocytes, and MPV in CRC patients with early disease stages were higher than those with advanced stages. However, levels of eosinophil in CRC patients with advanced stages were higher than those with early stages. Depending on the tumor anatomical location, we observed that numbers of red blood cells (RBCs), hemoglobin (Hgb), and hematocrit (Hct) in CRC patients with left-sided tumors were higher than those with right-sided tumors. We found that low levels of MPV were associated with shorter DFS. However, high levels of eosinophils were associated with shorter DFS in all CRC patients. When patients were divided based on the tumor anatomical location, higher levels of MPV, MCHC, and Hgb were associated with better DFS in the left-sided but not right-sided CRC patients. However, left-sided, but not right-sided, CRC patients with high levels of eosinophil and RDW had shorter DFS. Furthermore, right-sided, but not left-sided, CRC patients with high levels of platelets tended to have a shorter DFS. Our data show that MPV and eosinophils could serve as potential prognostic biomarkers in pre-treatment CRC patients, regardless of the tumor anatomical location. Additionally, lower levels of MPV, MCHC, and Hgb, and high levels of eosinophils and RDW could be negative predictive biomarkers in left-sided CRC patients.

Biomarkers of non-communicable chronic disease: an update on contemporary methods

Chronic diseases constitute a major global burden with significant impact on health systems, economies, and quality of life. Chronic diseases include a broad range of diseases that can be communicable or non-communicable. Chronic diseases are often associated with modifications of normal physiological levels of various analytes that are routinely measured in serum and other body fluids, as well as pathological findings, such as chronic inflammation, oxidative stress, and mitochondrial dysfunction. Identification of at-risk populations, early diagnosis, and prediction of prognosis play a major role in preventing or reducing the burden of chronic diseases. Biomarkers are tools that are used by health professionals to aid in the identification and management of chronic diseases. Biomarkers can be diagnostic, predictive, or prognostic. Several individual or grouped biomarkers have been used successfully in the diagnosis and prediction of certain chronic diseases, however, it is generally accepted that a more sophisticated approach to link and interpret various biomarkers involved in chronic disease is necessary to improve our current procedures. In order to ensure a comprehensive and unbiased coverage of the literature, first a primary frame of the manuscript (title, headings and subheadings) was drafted by the authors working on this paper. Second, based on the components drafted in the preliminary skeleton a comprehensive search of the literature was performed using the PubMed and Google Scholar search engines. Multiple keywords related to the topic were used. Out of screened papers, only 190 papers, which are the most relevant, and recent articles were selected to cover the topic in relation to etiological mechanisms of different chronic diseases, the most recently used biomarkers of chronic diseases and finally the advances in the applications of multivariate biomarkers of chronic diseases as statistical and clinically applied tool for the early diagnosis of chronic diseases was discussed. Recently, multivariate biomarkers analysis approach has been employed with promising prospect. A brief discussion of the multivariate approach for the early diagnosis of the most common chronic diseases was highlighted in this review. The use of diagnostic algorithms might show the way for novel criteria and enhanced diagnostic effectiveness inpatients with one or numerous non-communicable chronic diseases. The search for new relevant biomarkers for the better diagnosis of patients with non-communicable chronic diseases according to the risk of progression, sickness, and fatality is ongoing. It is important to determine whether the newly identified biomarkers are purely associations or real biomarkers of underlying pathophysiological processes. Use of multivariate analysis could be of great importance in this regard.

Target Nanoparticles against Pancreatic Cancer: Fewer Side Effects in Therapy

Pancreatic cancer is the most common lethal tumor in America. This lethality is related to limited treatment options. Conventional treatments involve the non-specific use of chemotherapeutical agents such as 5-FU, capecitabine, gemcitabine, paclitaxel, cisplatin, oxaliplatin, or irinotecan, which produce several side effects. This review focuses on the use of targeted nanoparticles, such as metallic nanoparticles, polymeric nanoparticles, liposomes, micelles, and carbon nanotubes as an alternative to standard treatment for pancreatic cancer. The principal objective of nanoparticles is reduction of the side effects that conventional treatments produce, mostly because of their non-specificity. Several molecular markers of pancreatic cancer cells have been studied to target nanoparticles and improve current treatment. Therefore, properly functionalized nanoparticles with specific aptamers or antibodies can be used to recognize pancreatic cancer cells. Once cancer is recognized, these nanoparticles can attack the tumor by drug delivery, gene therapy, or hyperthermia.

Molecular Biomarkers: A Review of Multiple Applications in Clinical Care of Colorectal Cancer

BACKGROUND

Advancements in tumor profiling have identified multiple molecular biomarkers that influence tumor growth and behavior. Molecular biomarkers provide clinical prognostic and predictive information, which guides treatment decisions and forms the backbone of precision oncology.

OBJECTIVES

This article identifies key predictive and prognostic molecular biomarkers used in the treatment of colorectal cancer and provides greater understanding of their biologic significance and usefulness in guiding treatment decisions.

METHODS

A review of the literature and professional guidelines was performed to evaluate approved molecular biomarkers, targeted agents, and tumor testing modalities used for the management and treatment of colorectal cancer, with an emphasis on treatment decision making.

FINDINGS

Genomic biomarkers are increasingly used for the prevention, diagnosis, prognostication, and management of colorectal cancer. The introduction of targeted agents and advancements in tumor profiling technologies have increased treatment opportunities and improved clinical outcomes for patients with colorectal cancer.

The diagnostic value of extracellular protein kinase A (ECPKA) in serum for gastric and colorectal cancer

Background

For the biomarkers of cancer, many chromosomal and genetic alterations have been examined as possible. However, some tumors do not display a clear molecular and genetic signature. While there are some cellular processes regulated by second messenger intracellular pathways indeed involved in carcinogenesis. The first intracellular second messenger was described as cyclic adenosine 3',5'-monophosphate (cAMP), and cAMP-dependent protein kinase (PKA) play a crucial role in several biological processes; The dysregulation of PKA-mediated signaling in several types of cancer should be investigated. More interesting, the alpha catalytic subunit of PKA (PKACα) could be secreted into the conditioned medium by different types of cancer cells, and it also existed in the serum of some cancer patients, defined as extracellular protein kinase A (ECPKA).

Methods

The levels of serum PKACα from healthy people, gastric cancer and colon cancer patients were detected by ELISA kits. Western blotting was used to detect the expression of PKACα in cancer tissue and the adjacent mucosa. Mann-Whitney test was applied to analyze the patients’ characteristics and serum PKACα. ROC analyses were performed to further evaluate the utility of PKACα in cancer diagnosis. The correlation of serum PKACα and T stage, age, and tumor markers were analyzed by Spearman rank and Pearson correlation analysis, respectively.

Results

There were significant differences of PKACα in serum between the volunteers and the gastric cancers (P<0.01), but not the colorectal cancers (P>0.05). ROC analyses evaluated the utility of PKACα for gastric cancer with 61.90% sensitivities and 87.50% specificities. The serum PKACα was correlated with tumor marker CA50, while there was no significant difference of PKACα expression between the gastric/colorectal cancer tissue and the adjacent mucosa.

Conclusions

The above results implied that PKACα levels might be a potential biomarker for the early screening of gastric cancers. Moreover, further research is still needed to investigate the role of secreted PKACα and the regulatory mechanism in tumor progression.

Prognostic Significance of Serum Uric Acid and Gamma-Glutamyltransferase in Patients with Advanced Gastric Cancer

PURPOSE

In this study, we aim to evaluate the prognostic role of serum uric acid and gamma-glutamyltransferase in advanced gastric cancer patients.

METHODS

A total of 180 patients pathologically diagnosed with advanced gastric cancer were included in this retrospective study. We used time-dependent receiver operating characteristic (ROC) curves to identify the optimal cut-off value of serum uric acid (UA) and gamma-glutamyltransferase (GGT). Survival analysis was performed using the Kaplan-Meier method and log-rank test, and multivariate Cox regression analyses were applied. A nomogram was formulated, and the calibration and discrimination of the nomogram were determined by calibration curve and concordance index (C-index). We validated the results using bootstrap resampling and a separate study on 60 patients collected from 2015 to 2017 using the same criteria in other medical center.

RESULTS

Both higher serum uric acid (>228 μmol/L) and higher gamma-glutamyltransferase (>14 U/L) had worse OS and PFS. Univariate analysis indicated that serum uric acid (UA) (p < 0.001 and p < 0.001) and gamma-glutamyltransferase (GGT) (p < 0.001 and p = 0.044) were significantly related to overall survival (OS) and progression-free survival (PFS), respectively. Multivariate analysis revealed serum uric acid (UA) and gamma-glutamyltransferase (GGT) were independent prognostic factors for OS (p = 0.012, p = 0.001). The optimal agreement between actual observation and nomogram prediction was shown by calibration curves. The C-indexes of the nomogram for predicting OS and PFS were 0.748 (95% CI: 0.70-0.79) and 0.728 (95% CI: 0.6741-0.7819), respectively. The results were confirmed in the validation cohort.

CONCLUSION

We observed that both serum UA and GGT were poor prognostic factors in patients with advanced gastric cancer. And we also formulated and validated a nomogram which can predict individual survival for advanced gastric cancer patients.

Identification of potential biomarkers in cervical cancer with combined public mRNA and miRNA expression microarray data analysis

Cervical cancer is the fourth most prevalent malignancy in females worldwide. Early diagnosis is key to improving survival rates. Molecular biomarkers are an important method for diagnosing a number of types of cancer, including cervical cancer. The present study utilized public data from three mRNA microarray datasets and one microRNA dataset to analyze the key genes involved in cervical cancer. The mRNA and microRNA expression profile datasets (GSE9750, GSE46857, GSE67522 and GSE30656) were downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) and microRNAs (DEMs) were screened using the online tool GEO2R. By using the DEGs consistent across the three mRNA datasets, a functional and pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed and module analysis performed using the Search Tool for the Retrieval of Interacting Genes. Validated target genes of the DEMs were identified using the miRecords website. Using the identified target genes of the DEMs, a survival analysis was performed using the OncoLnc online tool. A total of 73 DEGs and 19 DEMs were screened from the microarray expression profile datasets. ‘Integrin-mediated’, ‘proteolysis’ and ‘phosphoinositide 3 kinase-protein kinase 3’ signaling pathways were the most enriched in the DEGs. Three of the DEGs, including Ras homolog family member B (RhoB), stathmin 1 (STMN1) and cyclin D1 (CCNB1) were validated DEM target genes. The OncoLnc survival analysis identified that RhoB was associated with a significantly longer overall survival, whereas STMN1 was associated with a significantly reduced overall survival time in patients with cervical cancer. Finally, data from The Cancer Genome Atlas revealed an association between the mRNA expression levels of RhoB and STMN1, and the overall survival time for patients with cervical cancer. In conclusion, RhoB and STMN1 were identified as key genes that may provide potential targets for cervical cancer diagnosis and treatment.

What Will We Expect From Novel Therapies to Esophageal and Gastric Malignancies?

Esophageal cancer and gastric cancer are aggressive diseases for which treatment approaches are facing a new era. Some molecular pathways, such as VEGF, EGFR, fibroblast growth factor receptor, PIK3CA, and PARP-1, have been studied, and novel targeted drugs are presumed to be developed in the near future. From The Cancer Genome Atlas report, 80% of Epstein-Barr virus tumors and 42% of tumors with microsatellite instability have PIK3CA mutations, suggesting that this pathway could be reevaluated as a possible target for new systemic treatment of gastric cancer. Notably, higher PARP-1 expression can be found in gastric cancer, which might be related to more advanced disease and worse prognosis. In addition, PD-L1 expression, high microsatellite instability, and mismatch repair deficiency can be found in gastric cancer, thus suggesting that immunotherapy may also play a role in those patients. We discuss trends related to the potential of novel therapies for patients with esophageal and gastric cancers in the near future.

Combination of TNM staging and pathway based risk score models in patients with gastric cancer

Due to the complexity and heterogeneity of gastric cancer (GC) in individual patient, current staging system is inadequate for predicting outcome of GC. Comprehensive computational and bioinformatics approach may triumph for the prediction. In this study, GC patients were devided according to stage and treatment: curative surgery plus chemoradiotherapy in stage II, curative surgery plus chemoradiotherapy in stages III, and IV, unresectable metastatic gastric cancer. The training sets were downloaded from GEO datasets (GSE26253 and GSE14208). Gene set enrichment analysis (GSEA) was performed to explore enriched difference between recurrence and nonrecurrence. The core enrichment genes of enriched pathways significantly associated with recurrence or progression were identified using Cox proportional hazards analysis. Thereafter, the risk score models were externally validated in independent datasets-GSE15081 and The Cancer Genome Atlas (TCGA). We generated respective risk score models of patients in different stages and treatment. A five-gene signature comprising FARP1, SGCE, SGCA, LAMA4, and COL9A2 was strongly associated with recurrence of patients with curative surgery plus chemoradiotherapy in stage II. A six-gene signature consisting of SHH, NF1, AP4B1, COMP, MATN3, and CCL8 was correlated with recurrence of patients with curative surgery plus chemoradiotherapy in stages III and IV. And a four-gene signature composing of ABCC2, AHNAK2, RNF43, and GSPT2 was highly related to progression of patients with unresectable metastatic GC. Taking into consideration TNM stage and gene signature reflecting recurrence or progression, the risk score models significantly improved the accuracy in predicting outcome of GC.

Profiles of differentially expressed genes and overexpression of NEBL indicates a positive prognosis in patients with colorectal cancer

The present study aimed to identify differentially expressed genes (DEGs) in colorectal cancer (CRC) and provide novel prognostic biomarkers for CRC. The microarray dataset GSE41258 was used to screen DEGs of CRC. Subsequently, a protein-protein interaction network of DEGs and Gene Ontology analysis were performed to identify hub genes and associated biological processes. Nebulette (NEBL) and complement C1q like 1 (C1QL1) were validated using reverse transcription-quantitative polymerase chain reaction in patients with CRC. Survival analysis was performed for the two hub genes. GSE41258 dataset included 182 CRC samples and 54 normal tissues. A total of 759 DEGs, including 279 upregulated and 480 downregulated were screened between both groups. NEBL and C1QL1 were identified as the two hub genes and upregulated genes involved in various biological processes, including ‘regulation of biological quality’ and ‘response to stimulus’, respectively. Additionally, the overexpression of NEBL and C1QL1 in experimental validation was consistent with the aforementioned bioinformatics analysis results. Survival analysis suggested that overexpressed NEBL in patients with CRC was associated with a positive prognosis for overall survival. In conclusion, CRC was associated with a large group of DEGs. From the upregulated genes, overexpressed NEBL in patients CRC indicated a positive prognosis for overall survival and may be used as a prognostic biomarker for patients with CRC.