Use of Biomarkers in Screening for Cancer

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Multicancer Detection (MCD) Testing in Gastrointestinal Cancers: An Evolving Tool for Early Diagnosis

PURPOSE OF REVIEW

The current review aims to summarize the benefits and limitations of the novel multicancer detection tests (MCD) for diagnosing gastrointestinal (GI) malignancies.

RECENT FINDINGS

Traditional cancer screening methods can reduce deaths in malignancies involving the GI tract. For GI cancers, screening options vary by type and often involve invasive techniques with limited sensitivity. MCDs offer a promising, non-invasive (simple blood draw) alternative by analyzing biomarkers such as cell-free DNA and RNA using advanced techniques and machine learning to detect cancers across multiple organ sites. Large studies like the PATHFINDER trial and THUNDER study have demonstrated the feasibility and accuracy of MCD assays in identifying cancer signals, with high sensitivity and specificity in some GI organs that lack routine screening tests (e.g., liver, pancreas, and stomach). Despite these advancements, MCD testing faces challenges, including high costs, lack of FDA approval, false positives, and limited data on clinical utility in reducing cancer-specific mortality. MCD should not be a substitute for age-appropriate cancer screenings but may complement existing methods, particularly for cancers with no current screening tools, such as cholangiocarcinoma and pancreatic cancer. Clinicians need to discuss the limitations of MCDs, including the potential for overdiagnosis, patient anxiety, and financial burden due to insurance coverage gaps. MCD is a promising, non-invasive test that can augment traditional cancer screening. As the role of MCD in cancer detection evolves, further research is essential to establish how it will be integrated into clinical practice, ensuring informed, shared decision-making with patients.

Downregulation of solute carrier family 4 members 4 as a biomarker for colorectal cancer

Colorectal cancer (CRC) is one of the major cancer types associated with increased mortality worldwide. Hence, identifying reliable biomarkers make it very essential for early diagnosis and prognosis of CRC. Numerous studies have been conducted to decipher molecular mechanisms underlying CRC, however more deep insightful knowledge is the need of the hour. The purpose of this study was to identify promising key candidate genes in colorectal cancer (CRC) and assess their expression and clinical significance. To clarify and verify promising key biomarkers with signal transduction pathways in colorectal cancer, we integrated 11 microarray datasets from NCBI-GEO. This study utilized multiple bioinformatics tools and databases, including OncoDB, GEO2R, UALCAN, GEIPA, TIMER, and DAVID. The gene expression profiles of eleven datasets (GSE10714, GSE113513, GSE13471, GSE15960, GSE24514, GSE32323, GSE41258, GSE4183, GSE44076, GSE44861, GSE9348) were screened. In 11 gene expression profiles, 3 downregulated genes were identified and validated by databases such as OncoDB, UALCAN, GEIPA and TIMER. Downregulation of SLC4A4 with significant predictive value was validated by multi-omic data analysis and validated by Gene Expression Omnibus (GEO). GEIPA survival analysis showed that low SLC4A4 expression correlated with poorer overall survival among CRC patients. Based on this study, we identified SLC4A4 as a potential candidate biomarker for colorectal cancer (CRC), enabling early diagnosis and prognosis with molecular targeted therapy.

Sensitive Detection of Gynecological Cancer Recurrence Using Circulating Tumor DNA and Digital PCR: A Comparative Study with Serum Biochemical Markers

Early detection of recurrences in gynecological cancers is crucial for women's health. Circulating tumor DNA (ctDNA) analysis through liquid biopsy offers a promising approach for monitoring disease progression and identifying relapses. This study investigated the utility of digital Polymerase Chain Reaction (dPCR) for ctDNA detection in three gynecological cancer patients with clinically confirmed relapses during a two-year post-surgical follow-up. Patient-specific tumor mutations were identified through whole-exome sequencing (WES) and confirmed via Sanger sequencing. dPCR probes targeting these mutations were used to quantify the ctDNA levels in plasma samples collected throughout the follow-up period, and the findings were compared with standard serum biochemical markers. In two patients, persistent positive dPCR signals for the selected mutations were detected after tumor removal, with ctDNA levels progressively increasing even after post-surgical chemotherapy. Notably, dPCR identified elevated ctDNA levels before an increase in the cancer antigen 125 (CA125) biochemical marker was observed. In the third patient, no ctDNA signals from the two selected mutations were detected despite clinical evidence of recurrence, suggesting the emergence of new mutations. While this study highlights the promise of dPCR for early recurrence detection in gynecological cancers, it also underscores the critical need for comprehensive mutation panels to overcome the inherent challenges posed by tumor heterogeneity and the emergence of new mutations during disease progression.

Development of fecal microbial diagnostic marker sets of colorectal cancer using natural language processing method

BACKGROUND

Cancer screening and early detection greatly increase the chances of successful treatment. However, most cancer types lack effective early screening biomarkers. In recent years, natural language processing (NLP)-based text-mining methods have proven effective in searching the scientific literature and identifying promising associations between potential biomarkers and disease, but unfortunately few are widely used.

METHODS

In this study, we used an NLP-enabled text-mining system, MarkerGenie, to identify potential stool bacterial markers for early detection and screening of colorectal cancer. After filtering markers based on text-mining results, we validated bacterial markers using multiplex digital droplet polymerase chain reaction (ddPCR). Classifiers were built based on ddPCR results, and sensitivity, specificity, and area under the curve (AUC) were used to evaluate the performance.

RESULTS

A total of 7 of the 14 bacterial markers showed significantly increased abundance in the stools of colorectal cancer patients. A five-bacteria classifier for colorectal cancer diagnosis was built, and achieved an AUC of 0.852, with a sensitivity of 0.692 and specificity of 0.935. When combined with the fecal immunochemical test (FIT), our classifier achieved an AUC of 0.959 and increased the sensitivity of FIT (0.929 vs. 0.872) at a specificity of 0.900.

CONCLUSIONS

Our study provides a valuable case example of the use of NLP-based marker mining for biomarker identification.

Anti-tumor target screening of sea cucumber saponin Frondoside A: a bioinformatics and molecular docking analysis

Cancer remains the leading cause of death worldwide. In spite of significant advances in targeted and immunotherapeutic approaches, clinical outcomes for cancer remain poor. The aim of the present study was to investigate the potential mechanisms and therapeutic targets of Frondoside A for the treatment of liver, pancreatic, and bladder cancers. The data presented in our study demonstrated that Frondoside A reduced the viability and migration of HepG2, Panc02, and UM-UC-3 cancer cell in vitro. Moreover, we utilized the GEO database to screen and identify for differentially expressed genes (DEGs) in liver, pancreatic, and bladder cancers, which resulted in the identification of 714, 357, and 101 DEGs, respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were performed using the Metascape database for DEGs that were significantly associated with cancer development. The protein-protein interaction (PPI) networks of the identified DEGs in liver, pancreatic, and bladder cancers were analyzed using Cytoscape 3.9.0 software, and subsequently identified potential key genes that were associated with these networks. Subsequently, their prognostic values were assessed by gene expression level analysis and Kaplan-Meier survival analysis (GEPIA). Furthermore, we utilized TIMER 2.0 to investigate the correlation between the expression of the identified key gene and cancer immune infiltration. Finally, molecular docking simulations were performed to assess the affinity of Frondoside A and key genes. Our results showed a significant correlation between these DEGs and cancer progression. Combined, these analyses revealed that Frondoside A involves in the regulation of multiple pathways, such as drug metabolism, cell cycle in liver cancer by inhibiting the expression of CDK1, TOP2A, CDC20, and KIF20A, and regulates protein digestion and absorption, receptor interaction in pancreatic cancer by down-regulation of ASPM, TOP2A, DLGAP5, TPX2, KIF23, MELK, LAMA3, and ANLN. While in bladder cancer, Frondoside A regulates muscle contraction, complement and coagulation cascade by increase FLNC expression. In conclusion, the present study offers valuable insights into the molecular mechanism underlying the anticancer effects of Frondoside A, and suggests that Frondoside A can be used as a functional food supplement or further developed as a natural anti-cancer drug.

Early Detection of Ovarian Cancer

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

A guide to mass spectrometric analysis of extracellular vesicle proteins for biomarker discovery

Exosomes (small extracellular vesicles) in living organisms play an important role in processes such as cell proliferation or intercellular communication. Recently, exosomes have been extensively investigated for biomarker discoveries for various diseases. An important aspect of exosome analysis involves the development of enrichment methods that have been introduced for successful isolation of exosomes. These methods include ultracentrifugation, size exclusion chromatography, polyethylene glycol-based precipitation, immunoaffinity-based enrichment, ultrafiltration, and asymmetric flow field-flow fractionation among others. To confirm the presence of exosomes, various characterization methods have been utilized such as Western blot analysis, atomic force microscopy, electron microscopy, optical methods, zeta potential, visual inspection, and mass spectrometry. Recent advances in high-resolution separations, high-performance mass spectrometry and comprehensive proteome databases have all contributed to the successful analysis of exosomes from patient samples. Herein we review various exosome enrichment methods, characterization methods, and recent trends of exosome investigations using mass spectrometry-based approaches for biomarker discovery.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

Prognostic significance of VEGF signaling system components and matrix metalloproteinases in blood serum of gastric cancer patients

Analysis of long-term treatment results of 77 primary gastric cancer patients at stage I-IV of the tumor process followed during 1 - 41 months (median - 6.4 months) from the onset of specific treatment are presented depending on the basal levels of VEGF, soluble forms of its receptors (sVEGFR1, sVEGFR2) and matrix metalloproteinases (MMP-2, 7, 9) in blood serum. Overall survival assessed by Kaplan-Meyer analysis and with the help of Cox multiparametric regression model was applied as the criterion of prognostic value. It was found that at high (≥ 420 pg/ml) serum VEGF, the overall survival of patients with gastric cancer was statistically significantly lower than at the marker's levels below 420 pg/ml (p<0.011): 3-year's survival comprised 46,3±12,5% and 88,2±7,8% respectively. Median survival of patients with high VEGF level comprised 21.7 months, of those with low VEGF was not achieved during the whole follow-up period. Serum sVEGFR1, sVEGFR2, MMP-2, 7 and 9 levels were not significantly associated with the overall survival of patients included in this study. Only index M of TNM system and serum VEGF level demonstrated an independent prognostic value in multiparametric model (p=0.036). Thus, it was confirmed that VEGF signaling pathway plays an important role in gastric cancer, and its components - in the first place, VEGF A - are substantial factors of disease prognosis, and can also be useful for monitoring of treatment efficiency.

Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis

BACKGROUND

Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored.

METHODS

To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape.

RESULTS

Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood.

CONCLUSION

In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.

The functional landscape of Golgi membrane protein 1 (GOLM1) phosphoproteome reveal GOLM1 regulating P53 that promotes malignancy

Golgi membrane protein 1 (GOLM1) was implicated in carcinogenesis of multiple types of cancer. However, Phosphoproteome landscapes of GOLM1 overexpression in lung cancer remain largely unknown. In this study, using data from the Cancer Genome Atlas (TCGA) and phosphoproteome, we systematically evaluated the feature of GOLM1 and studied its prognostic value in non-small cell lung cancer (NSCLC). The proliferation, migration, and invasion capacities in PC9 cell with GOLM1 overexpression were determined using Trans-well system assay. Tumor engrafts was visualized in mice models and confirmed by ex vivo. An increased expression of GOLM1 had shorter overall survival (OS) in patients with NSCLC in TCGA database. GOLM1 in single gene set enrichment analysis (GSEA) related to adherent's junction, cell cycle, and pathway in cancer. Overexpression of GOLM1 in GOLM1OE PC9 cells promoted cell proliferation, migration, and invasion. Decreased migration and invasion potential were also observed in knockdown of GOLM1 in GOLM1KD PC9 cells in migration assay. An increased expression of GOLM1 could significantly increase the growth of tumor in xenograft mice models. phosphoproteome analysis showed 239 upregulated and 331 downregulated Phosphorylated proteins in GOLM1OE PC9 cells. Overexpression of GOLM1 in GSEA was significantly related to P53 in MAPK signaling pathway. Overexpression of GOLM1enhanced the phosphorylation of P53 protein at site S315 but inhibited the formation of P53 tetramers. These results indicate that overexpression GOLM1 enhances non-small-cell carcinoma aggressiveness through inhibited the formation of P53 tetramer.

Isothymusin, a Potential Inhibitor of Cancer Cell Proliferation: An In Silico and In Vitro Investigation

BACKGROUND

Since centuries plant-based compounds are known for the treatment of cancer in both traditional and contemporary medicine. The problems like target non-specificity and toxicity are well-known regarding anticancer drugs. Therefore, target specific search of novel entities is constant. Isothymusin is a dimethoxy, trihydroxy flavone present in plants like Ocimum sanctum, and Limnophilla geoffrayi. There are limited reports available on the anticancer potential of isothymusin.

OBJECTIVES

The effects of isothymusin on redox status, cell cytotoxicity, and targets involved in the promotion and progression of the cancer cells have been investigated.

METHODS

Antiproliferative efficacy was evaluated by MTT, Neutral Red Uptake, and Sulforhodamine-B assays. The spectrophotometric methods were adopted to study the effect against selected targets. Redox activity was assessed by in vitro antioxidant assays and the interaction study, ADMET profiling, and toxicity assessments were done in silico.

RESULTS

Isothymusin scavenges the radicals, i.e., DPPH and nitric oxide with moderate ferric reducing potential. It affected the proliferation of leukemia, colon, skin, and breast cancer cell lines by more than 50% but moderately affected prostate, kidney, lung, hepatic, and breast adenocarcinoma (up to 48%). Isothymusin inhibited the enzymes associated with the promotion stage of cancer, including cycloxygenase- 2 and lipoxygenase-5. Additionally, it also inhibited the activity of proliferation markers like cathepsin- D, dihydrofolate reductase, hyaluronidase, and ornithine-decarboxylase. Besides, in silico studies supported the in vitro enzyme inhibition assays outcome. Toxicity studies showed promising results of chemical descriptors and non-skin-irritant, moderate ocular-irritancy, and in vitro Ames test confirmed non-mutagenic nature.

CONCLUSION

Isothymusin showed radical scavenging and anti-proliferative activities, which may be taken up as a phytochemical lead for the synthesis of analogues possessing enhanced anticancer potential.

Diagnostic role of neutrophil-to-lymphocyte ratio and monocyte-to-lymphocyte ratio in patients with enchondroma and low-grade chondrosarcoma

Objectives

This study aims to evaluate the role of elevated neutrophil-to-lymphocyte ratio (NLR) and monocyte-to- lymphocyte ratio (MLR) in differential diagnosis of enchondroma and low-grade chondrosarcoma.

Patients and methods

One-hundred-and-one patients (44 males, 57 females; mean age 53.6±11.5 years; range, 21 to 85 years) diagnosed with enchondroma and low-grade chondrosarcoma in Ankara Oncology Training and Research Hospital between January 2010 and December 2019 were included in this retrospective study. Patients’ age, gender, location and type of tumor, and pre-treatment complete blood count results were acquired. One-hundred patients (48 males, 52 females; mean age 50.9±13.6 years; range, 19 to 76 years) with complete blood count results admitted to the same center for reasons other than fracture, infection or tumors with similar age and gender to the aforementioned study group were included as healthy controls.

Results

Neutrophil-to-lymphocyte ratio and MLR of the study group were found to be significantly higher than the control group (p<0.001). Neutrophil-to-lymphocyte ratio and MLR held diagnostic importance with statistically significant cut-off values. Statistically significant cut-offs for NLR and MLR were ≥2.0 (sensitivity=73.3%, specificity=67%) and ≥0.2 (sensitivity=76.2%, specificity=63%), respectively. Multivariate logistic regression analysis was performed adjusting for age and gender and NLR ≥2 [odds ratio (OR)=3.1] or MLR ≥0.2 (OR=2.9) were found to be associated with approximately three-fold risk for diagnosis of enchondroma or low-grade chondrosarcoma.

Conclusion

The NLR and MLR have diagnostic value in cartilaginous tumors such as enchondroma and low-grade chondrosarcoma. However, our results do not support utilization of NLR and MLR as diagnostic value for differentiation of enchondroma and low-grade chondrosarcoma.

The Utility of Liquid Biopsies in Radiation Oncology

The use of therapeutic radiation is primarily guided by clinicopathologic factors and medical imaging, whereas molecular biomarkers currently play a comparatively minor role in most settings. Liquid biopsies provide a rich source of noninvasive tumor-specific biomarkers and are amenable to repeated and noninvasive assessment. Here, we review the current status of liquid biopsies and their potential impact on the field of radiation oncology. We focus on established and emerging approaches to analyze circulating tumor DNA and circulating tumor cells from peripheral blood. These promising classes of biomarkers could have an outsized impact on cancer management by meaningfully stratifying patients into risk groups, tracking radiation therapy efficacy during and after treatment, and identifying patients with radiosensitive or radioresistant disease. Finally, we highlight opportunities for future investigation including the need for prospective interventional studies employing liquid biopsies to guide the management of radiation therapy-treated patients.

Down-regulated Solute Carrier Family 4 Member 4 Predicts Poor Progression in Colorectal Cancer

Aim: To identify potential key candidate genes, whose expression and clinical significance was further assessed in colorectal cancer (CRC). Methods: Three original microarray datasets (GSE41328, GSE22598, and GSE23878) from NCBI-GEO were used to analyze differentially expressed genes (DEGs) in CRC. Online database analyses through Oncomine and GEIPA were performed to evaluate SLC4A4 expression and explore the prognostic merit of SLC4A4 expression, which was further confirmed by analyses from QPCR based cDNA array and IHC based tissue microarray (TMA). STRING website was used to explore the interaction between SLC4A4 with other DEGs based on the protein-protein interaction (PPI) networks. Results: Analysis of three original microarray datasets from GEO identified 82 shared, differentially expressed genes (28 upregulated and 54 down-regulated) in CRC tissues. Online analyses from Oncomine and GEIPA revealed lower SLC4A4 mRNA expression in CRC tissues compared to adjacent normal tissues, which were further confirmed by QPCR based cDNA array and IHC based TMA analyses on both mRNA and protein levels. Survival analyses through GEIPA and from TMA demonstrated that low SLC4A4 expression is correlated with worse overall survival among patients with CRC. Survival analysis from Kaplan-meier plotter demonstrated that low SLC4A4 expression is significantly associated with poor progression (including relapse-free survival, overall survival, distant metastasis-free survival, post-progression survival) of patients with breast cancer, lung cancer, gastric cancer, and ovarian cancer. PPI analysis found that SLC4A4 is highly correlated with various genes, including SLC9A3, SLC26A6, ENSG00000214921, SLC26A4, SLC9A3R1, and SLC9A1. Conclusion: The mRNA and protein levels of SLC4A4 were decreased in CRC tissues, and low expression of SLC4A4 significantly correlated with shorter survival of CRC patients and poorer progression of patients with breast cancer, lung cancer, gastric cancer and ovarian cancer, suggesting potential role of SLC4A4 on tumor suppression and prognostic prediction in multiple malignancies including CRC.

Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer

Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.

Blood Chromogranin A Is Not Effective as a Biomarker for Diagnosis or Management of Bronchopulmonary Neuroendocrine Tumors/Neoplasms

BACKGROUND

Identification of circulating tumor markers for clinical management in bronchopulmonary (BP) neuroendocrine tumors/neoplasms (NET/NEN) is of considerable clinical interest. Chromogranin A (CgA), a "universal" NET biomarker, is considered controversial as a circulating biomarker of BPNEN.

AIM

Assess utility of CgA in the diagnosis and management of BPNEN in a multicentric study.

MATERIAL AND METHODS

CgA diagnostic metrics were assessed in lung NET/NENs (n = 200) and controls (n = 140), randomly assigned to a Training and Test set (100 BPC and 70 controls in each). Assay specificity was evaluated in neoplastic lung disease (n = 137) and nonneoplastic lung disease (n = 77). CgA efficacy in predicting clinical status was evaluated in the combined set of 200 NET/NENs. CgA levels in bronchopulmonary neuroendocrine tumor (BPNET) subtypes (atypical [AC] vs. typical [TC]) and grade was examined. The clinical utility of an alteration of CgA levels (±25%) was evaluated in a subset of 49 BPNET over 12 months. CgA measurement was by NEOLISATM kit (EuroDiagnostica).

RESULTS

Sensitivity and specificity in the training set were 41/98%, respectively. Test set data were 42/87%. Training set area under receiver operator characteristic analysis differentiated BPC from control area under the curve (AUC) 0.61 ± 0.05 p = 0.015. Test set the data were AUC 0.58 ± 0.05, p = 0.076. In the combined set (n = 200), 67% BPNET/NEN (n = 134) had normal CgA levels. CgA levels did not distinguish histological subtypes (TC vs. AC, AUC 0.56 ± 0.04, p = 0.21), grade (p = 0.45-0.72), or progressive from stable disease (AUC 0.53 ± 0.05 p = 0.47). There was no correlation of CgA with Ki-67 index (Pearson r = 0.143, p = 0.14). For nonneoplastic diseases (chronic obstructive pulmonary disorder and idiopathic pulmonary fibrosis), CgA was elevated in 26-37%. For neoplastic disease (NSCLC, squamous cell carcinoma), CgA was elevated in 11-16%. The neuroendocrine SCLC also exhibited elevated CgA (50%). Elevated CgA was not useful for differentiating BPNET/NEN from these other pathologies. Monitoring BPNET/NEN over a 12-month period identified neither CgA levels per se nor changes in CgA were reflective of somatostatin analog treatment outcome/efficacy or the natural history of the disease (progression).

CONCLUSIONS

Blood CgA levels are not clinically useful as a biomarker for lung BPNET/NEN. The low specificity and elevations in both nonneoplastic as well as other common neoplastic lung diseases identified limited clinical utility for this biomarker.

Screening and surveillance of multiple solid tumours using plasma placental-like chondroitin sulfate A (pl-CSA)

Rationale: Placental-like chondroitin sulfate A (pl-CSA) is known to be exclusively synthesized in multiple cancer tissues and associated with disease severity. Here, we aimed to assess whether pl-CSA is released into bio-fluids and can serve as a cancer biomarker. Methods: A novel ELISA was developed to analyse pl-CSA content in bio-fluids using pl-CSA binding protein and an anti-pl-CSA antibody. Immunohistochemical staining of tissue chips was used as the gold standard control. Results: The developed ELISA method was specific and sensitive (1.22 μg/ml). The pl-CSA content was significantly higher in lysates and supernatants of cancer cell lines than in those of normal cell lines, in plasma from mouse cancer models than in that from control mice, and in plasma from patients with oesophageal, cervical, ovarian, or lung cancer than in that from healthy controls. Similar to the tissue chip analysis, which showed a significant difference in pl-CSA positivity between cancer tissues and normal adjacent tissues, the plasma pl-CSA analysis had 100% sensitivity and specificity for differentiating oesophageal and lung cancer patients from healthy controls. Importantly, in oesophageal and lung cancer patients, the pl-CSA content was significantly higher in late-stage disease than in early-stage disease, and it dramatically decreased after surgical resection of the tumour. Conclusion: These data indicate a direct link between plasma pl-CSA content and tumour presence, indicating that plasma pl-CSA may be a non-invasive biomarker with clinical applicability for the screening and surveillance of patients with multiple types of solid tumours.

Evaluation of core serous epithelial ovarian cancer genes as potential prognostic markers and indicators of the underlying molecular mechanisms using an integrated bioinformatics analysis

Ovarian cancer is a major cause of mortality in women. However, the molecular events underlying the pathogenesis of the disease are yet to be fully elucidated. In the present study, an integrated bioinformatics analysis was performed to identify core genes involved in serous epithelial ovarian cancer. A total of three expression datasets were downloaded from the Gene Expression Omnibus database, and included 46 serous epithelial ovarian cancer and 30 ovarian surface epithelium samples. The three datasets were merged, and batch normalization was performed. The normalized merged data were subsequently analyzed for differentially expressed genes (DEGs). In total, 2,212 DEGs were identified, including 1,300 upregulated and 912 downregulated genes. Gene Ontology analysis revealed that these DEGs were primarily involved in ‘regulation of cell cycle’, ‘mitosis’, ‘DNA packaging’ and ‘nucleosome assembly’. The main cellular components included ‘extracellular region part’, ‘chromosome’, ‘extracellular matrix’ and ‘condensed chromosome kinetochore’, whereas the molecular functions included ‘Calcium ion binding’, ‘polysaccharide binding’, ‘enzyme inhibitor activity’, ‘growth factor activity’, ‘cyclin-dependent protein kinase regulator activity’, ‘microtubule motor activity’ and ‘Wnt receptor activity’. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that these DEGs were predominantly involved in ‘Wnt signaling pathway’, ‘pathways in cancer’, ‘PI3K-Akt signaling pathway’, ‘cell cycle’, ‘ECM-receptor interaction’, ‘p53 signaling pathway’ and ‘focal adhesion’. The 20 most significant DEGs were identified from the protein-protein interaction network, and Oncomine analysis of these core genes revealed that 13 were upregulated and two were downregulated in serous epithelial ovarian cancer. Survival analysis revealed that cyclin B1, polo like kinase 1, G protein subunit γ transducin 1 and G protein subunit γ 12 are key molecules that may be involved in the prognosis of serous epithelial ovarian cancer. These core genes may provide novel treatment targets, although their roles in the carcinogenesis and prognosis of serous epithelial ovarian cancer require further study.

Five extracellular matrix-associated genes upregulated in oral tongue squamous cell carcinoma: An integrated bioinformatics analysis

Despite advancements in treatment regimens, the mortality rate of patients with oral tongue squamous cell carcinoma (OTSCC) is high. In addition, the signaling pathways and oncoproteins involved in OTSCC progression remain largely unknown. Therefore, the aim of the present study was to identify specific prognostic marker for patients at a high risk of developing OTSCC. The present study used four original microarray datasets to identify the key candidate genes involved in OTSCC pathogenesis. Expression profiles of 93 OTSCC tissues and 76 normal tissues from GSE9844, GSE13601, GSE31056 and GSE75538 datasets were investigated. Differentially expressed genes (DEGs) were determined, and gene ontology enrichment and gene interactions were analyzed. The four GSE datasets reported five upregulated and six downregulated DEGs. Five upregulated genes (matrix metalloproteinase 1, 3, 10 and 12 and laminin subunit gamma 2) were localized in the extracellular region of cells and were associated with extracellular matrix disassembly. Furthermore, analysis for The Cancer Genome Atlas database revealed that the aforementioned five upregulated genes were also highly expressed in OTSCC and head and neck squamous cell carcinoma tissues. These results demonstrated that the five upregulated genes may be considered as potential prognostic biomarkers of OTSCC and may serve at understanding OTSCC progression. Upregulated DEGs may therefore represent valuable therapeutic targets to prevent or control OTSCC pathogenesis.

Serum anti-EIF3A autoantibody as a potential diagnostic marker for hepatocellular carcinoma

Tumor-associated autoantibodies are promising diagnostic biomarkers for early detection of tumors. We have screened a novel tumor-associated autoantibody in hepatocellular carcinoma (HCC) model mice. Its target antigen was identified as eukaryotic translation initiation factor 3 subunit A (EIF3A) by proteomic analysis, and the elevated expression of EIF3A in HCC tissues of tumor model mice as well as human patients was shown. Also, its existence in tumor-derived exosomes was revealed, which seem to be the cause of tumor-associated autoantibody production. To use serum anti-EIF3A autoantibody as biomarker, ELISA detecting anti-EIF3A autoantibody in human serum was performed using autoantibody-specific epitope. For the sensitive detection of serum autoantibodies its specific conformational epitopes were screened from the random cyclic peptide library, and a streptavidin antigen displaying anti-EIF3A autoantibody-specific epitope, XC90p2(-CPVRSGFPC-), was used as capture antigen. It distinguished patients with HCC (n = 102) from healthy controls (n = 0285) with a sensitivity of 79.4% and specificity of 83.5% (AUC = 0.87). Also, by simultaneously detecting with other HCC biomarkers, including alpha-fetoprotein, HCC diagnostic sensitivity improved from 79.4% to 85%. Collectively, we suggest that serum anti-EIF3A autoantibody is a useful biomarker for the diagnosis of HCC and the combinational detection of related biomarkers can enhance the accuracy of the cancer diagnosis.

MicroRNA-related transcription factor regulatory networks in human colorectal cancer

OBJECTIVE

Colorectal cancer (CRC) is an extremely common gastrointestinal malignancy. The present study aimed to identify microRNAs (miRNAs) and transcription factors (TFs) associated with tumor development.

METHODS

Three miRNA profile datasets were integrated and analyzed to elucidate the potential key candidate miRNAs in CRC. The starBase database was used to identify the potential targets of common differentially expressed miRNAs (DEMs). Transcriptional Regulatory Element Database and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text databases were used to identify cancer-related TFs and the TF-regulated target genes. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integration Discovery (DAVID) database, and the miRNA-TF-gene networks were constructed by Cytoscape. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression of genes and miRNAs.

RESULTS

In total, 14 DEMs were found in CRC. By bioinformatics analysis, 5 DEMs (miR-145, miR-497, miR-30a, miR-31, and miR-20a) and 8 TFs (ELK4 (ETS-family transcription factor), myeloblastosis proto-oncogene like (MYBL)1, MYBL2, CEBPA, PPARA, PPARD, PPARG, and endothelial PAS domain protein (EPAS1)) appeared to be associated with CRC and were therefore used to construct miRNA-TF-gene networks. From the networks, we found that miR-20a might play the most important role as an miRNA in the networks. By qRT-PCR, we demonstrated that miR-20a was significantly upregulated in CRC tissues. We also performed qRT-PCR to identify the expression of miR-20a-related TFs (PPARA, PPARD, PPARG, EPAS1). Three of them, PPARA, PPARG, and EPAS1, were downregulated in CRC tissues, with statistically significant differences, while the downregulation of PPARD in CRC tissues was not significantly different. Pathway enrichment analyses indicated that the phosphoinositide 3-kinase (PI3K)-Akt signaling pathway was the most significantly enriched pathway. Two main elements of the PI3K-Akt signaling pathway, phosphatase and tensin homolog deleted on chromosome 10 and B-cell lymphoma 2-associated agonist of cell death, were demonstrated to be downregulated in CRC.

CONCLUSION

The present study identified hub miRNAs and miRNA-related TF regulatory networks in CRC, which might be potential targets for the diagnosis and treatment of CRC.

Integrative analysis of gene expression profiles reveals distinct molecular characteristics in oral tongue squamous cell carcinoma

Oral tongue squamous cell carcinoma (OTSCC) is the most common type of oral cancer. Despite advances in knowledge regarding the genome-scale gene expression pattern of oral cancer, the molecular portrait of OTSCC biology has remained unclear over the last few decades. Furthermore, studies concerning OTSCC gene-expression profiles are limited or inconsistent owing to tissue heterogeneity in single-cohort studies. Consequently, the present study integrated the profile datasets of three cohorts in order to screen for differentially expressed genes (DEGs), and subsequently identified the potential candidate genes and pathways in OTSCC through gene enrichment analysis and protein-protein interaction (PPI) network construction. Using the selected Gene Expression Omnibus datasets GSE13601, GSE31056 and GSE78060, 206 DEGs (125 upregulated and 81 downregulated) were identified in OTSCC, principally associated with extracellular matrix (ECM) organization and the phosphoinositide 3-kinase/protein kinase B signaling pathway. Furthermore, 146/206 DEGs were filtered into the PPI network and 20 hub genes were sorted. Further results indicated that the two most significant modules filtered from the PPI network were associated with ECM organization and human papillomavirus infection, which are important factors affecting OTSCC pathology. Overall, a set of OTSCC-associated DEGs has been identified, including certain key candidate genes that may be of vital importance for diagnosis, therapy and prevention of this disease.

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.

Identifying the Best Marker Combination in CEA, CA125, CY211, NSE, and SCC for Lung Cancer Screening by Combining ROC Curve and Logistic Regression Analyses: Is It Feasible?

The detection of serum biomarkers can aid in the diagnosis of lung cancer. In recent years, an increasing number of lung cancer markers have been identified, and these markers have been reported to have varying diagnostic values. A method to compare the diagnostic value of different combinations of biomarkers needs to be established to identify the best combination. In this study, automatic chemiluminescence analyzers were employed to detect the serum concentrations of carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), cytokeratin 19 fragment (CY211), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC) in 780 healthy subjects, 650 patients with pneumonia, and 633 patients with lung cancer. Receiver operating characteristic (ROC) curve and logistic regression analyses were also used to evaluate the diagnostic value of single and multiple markers of lung cancer. The sensitivities of the five markers alone were lower than 65% for lung cancer screening in healthy subjects and pneumonia patients. SCC was of little value in screening lung cancer. After combining two or more markers, the areas under the curves (AUCs) did not increase with the increase in the number of markers. For healthy subjects, the best marker for lung cancer screening was the combination CEA + CA125, and the positive cutoff range was 0.577 CEA + 0.035 CA125 > 2.084. Additionally, for patients with pneumonia, the best screening markers displayed differences in terms of sex but not age. The best screening marker for male patients with pneumonia was the combination CEA + CY211 with a positive cutoff range of 0.008 CEA + 0.068 CY211 > 0.237, while that for female patients with pneumonia was CEA > 2.73 ng/mL, which could be regarded as positive. These results showed that a two-marker combination is more suitable than a multimarker combination for the serological screening of tumors. Combined ROC curve and logistic regression analyses are effective for identifying the best markers for lung cancer screening.

Lectin-based biosensors as analytical tools for clinical oncology

The review focus on the use of lectin-based biosensors in the oncology field, and ponders the potentialities of using these devices as analytical tools to monitor the levels of cancer glycobiomarkers in biological fluids, helping in the diagnosis, prognosis and treatment assessment. Several examples of lectin-based biosensors directed for cancer biomarkers are described and discussed, and their potential application in the clinic is considered, taking into account their analytical features, advantages and performance in sample analysis. Technical and practical aspects in the construction process, which are specific for lectin biosensors, are debated, as well as the requirements in sample collection and processing, and biosensor validation. Today's challenges for real implementation of these devices in the clinic are presented, along with the future trends in the field.

Identification of anti-SF3B1 autoantibody as a diagnostic marker in patients with hepatocellular carcinoma

BACKGROUND

Tumor-associated (TA) autoantibodies, which are generated by the immune system upon the recognition of abnormal TA antigens, are promising biomarkers for the early detection of tumors. In order to detect autoantibody biomarkers effectively, antibody-specific epitopes in the diagnostic test should maintain the specific conformations that are as close as possible to those presenting in the body. However, when using patients' serum as a source of TA autoantibodies the characterization of the autoantibody-specific epitope is not easy due to the limited amount of patient-derived serum.

METHODS

To overcome these limits, we constructed a B cell hybridoma pool derived from a hepatocellular carcinoma (HCC) model HBx-transgenic mouse and characterized autoantibodies derived from them as tumor biomarkers. Their target antigens were identified by mass spectrometry and the correlations with HCC were examined. With the assumption that TA autoantibodies generated in the tumor mouse model are induced in human cancer patients, the enzyme-linked immunosorbent assays (ELISA) based on the characteristics of mouse TA autoantibodies were developed for the detection of autoantibody biomarkers in human serum. To mimic natural antigenic structures, the specific epitopes against autoantibodies were screened from the phage display cyclic random heptapeptide library, and the streptavidin antigens fused with the specific epitopes were used as coating antigens.

RESULTS

In this study, one of HCC-associated autoantibodies derived from HBx-transgenic mouse, XC24, was characterized. Its target antigen was identified as splicing factor 3b subunit 1 (SF3B1) and the high expression of SF3B1 was confirmed in HCC tissues. The specific peptide epitopes against XC24 were selected and, among them, XC24p11 cyclic peptide (-CDATPPRLC-) was used as an epitope of anti-SF3B1 autoantibody ELISA. With this epitope, we could effectively distinguish between serum samples from HCC patients (n = 102) and healthy subjects (n = 85) with 73.53% sensitivity and 91.76% specificity (AUC = 0.8731). Moreover, the simultaneous detection of anti-XC24p11 epitope autoantibody and AFP enhanced the efficiency of HCC diagnosis with 87.25% sensitivity and 90.59% specificity (AUC = 0.9081).

CONCLUSIONS

ELISA using XC24p11 peptide epitope that reacts against anti-SF3B1 autoantibody can be used as a novel test to enhance the diagnostic efficiency of HCC.

Identification of potential crucial genes associated with carcinogenesis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common genitourinary malignancy with high mortality. However, the molecular pathogenesis of ccRCC remains unclear and effective biomarkers for daily practice are still limited. Thus, we aimed to identify the potential crucial genes and pathways associated with carcinogenesis of ccRCC and further analyze the molecular mechanisms implicated in tumorigenesis. In the present study, expression profiles GSE 66270, GSE 53757, GSE 36895, and GSE 76351 were downloaded from GEO database, including 244 matched primary and adjacent normal tissues, furthermore, the level 3 RNAseq dataset (RNAseqV2 RSEM) of KIRC was also downloaded from The Cancer Genome Atlas (TCGA), which consist of 529 ccRCC tumors and 72 normal tissues. Then, differentially expressed genes (DEGs) and pathway enrichment were analyzed by using R software. A total of 129 up- and 123 down-regulated genes were identified, which were aberrantly expressed both in GEO and TCGA data. Second, Gene ontology (GO) analyses revealed that most of the DEGs were significantly enriched in integral component of membrane, extracellular exosome, plasma membrane, cell adhesion, and receptor binding. Signaling pathway analyses indicated that DEGs had common pathways in signal transduction, metabolism, and immune system. Third, hub genes were identified with protein-protein interaction (PPI) network, including PTPRC, TGFB1, EGF, MYC, ITGB2, CTSS, FN1, CCL5, KNG1, and CD86. Additionally, sub-networks analyse was also performed by using MCODE plugin. In conclusion, the novel DEGs and pathways in ccRCC identified in this study may provide new insight into the underlying molecular mechanisms that facilitates RCC carcinogenesis.

Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

Background

The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.

Results

The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein–protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network.

Conclusion

This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

Baseline and On-Treatment Characteristics of Serum Tumor Markers in Stage IV Oncogene-Addicted Adenocarcinoma of the Lung

INTRODUCTION

The role of serum tumor markers in the modern management of advanced NSCLC remains poorly described.

METHODS

A single-center retrospective analysis of available carcinoembryonic antigen, CA125, CA19.9, and CA27.29 levels at baseline and during treatment of stage IV lung adenocarcinoma by oncogenic driver was conducted.

RESULTS

A total of 142 patients were analyzed (60 with anaplastic lymphoma kinase gene [ALK] rearrangement, 50 with EGFR mutation, four with ROS1 rearrangement, and 29 with KRAS mutation). Of these, 82% had at least one marker (95% if all four markers were measured), with CA27.29 being the most commonly increased and CA19.9 the rarest. Only CA27.29 differed significantly by oncogene (it was less common in KRAS) (p = 0.016). The median times to nadir during tyrosine kinase inhibitor (TKI) therapy in EGFR and ALK cases were 16.4 and 20 weeks, respectively. Of the 41 patients with EGFR mutation or ALK or ROS1 rearrangement, 24 (59%) demonstrated an initial increase within the first 4 weeks of TKI therapy, 58% of whom then had their levels fall below baseline. An increase in marker level of 10% or more from nadir occurred in 53% of systemic and 22% of central nervous system-only progression.

CONCLUSIONS

Serum tumor markers are frequently increased in lung adenocarcinoma regardless of driver oncogene. Changes within the first 4 weeks of therapy may be misleading. Progression is associated with marker increases, especially in sites other than the central nervous system.

Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis

Colorectal cancer (CRC) is one of the most common malignant diseases worldwide, but the involved signaling pathways and driven-genes are largely unclear. This study integrated four cohorts profile datasets to elucidate the potential key candidate genes and pathways in CRC. Expression profiles GSE28000, GSE21815, GSE44076 and GSE75970, including 319 CRC and 103 normal mucosa, were integrated and deeply analyzed. Differentially expressed genes (DEGs) were sorted and candidate genes and pathways enrichment were analyzed. DEGs-associated protein–protein interaction network (PPI) was performed. Firstly, 292 shared DEGs (165 up-regulated and 127 down-regulated) were identified from the four GSE datasets. Secondly, the DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Thirdly, 180 nodes/DEGs were identified from DEGs PPI network complex. Lastly, the most significant 2 modules were filtered from PPI, 31 central node genes were identified and most of the corresponding genes are involved in cell cycle process, chemokines and G protein-coupled receptor signaling pathways. Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in CRC, which could improve our understanding of the cause and underlying molecular events, and these candidate genes and pathways could be therapeutic targets for CRC.