Breast cancer biomarker measurements and standards

A sensitive sandwich-type electrochemical immunosensor using nitrogen-doped graphene/metal-organic framework-derived CuMnCoOx and Au/MXene for the detection of breast cancer biomarker

In terms of cancer-related deaths among women, breast cancer (BC) is the most common. Clinically, human epidermal growth receptor 2 (HER2) is one of the most commonly used diagnostic biomarkers for facilitating BC cell proliferation and malignant growth. In this study, a disposable gold electrode (DGE) modified with gold nanoparticle-decorated Ti3C2Tx (Au/MXene) was utilized as a sensing platform to immobilize the capturing antibody (Ab1/Au/MXene). Subsequently, nitrogen-doped graphene (NG) with a metal-organic framework (MOF)-derived copper-manganese-cobalt oxide, tagged as NG/CuMnCoOx, was used as a probe to label the detection antibody (Ab2). A sandwich-type immunosensor (NG/CuMnCoOx/Ab2/HER2-ECD /Ab1/Au/MXene/DGE) was developed to quantify HER2-ECD. NG/CuMnCoOx enhances the conductivity, electrocatalytic active sites, and surface area to immobilize Ab2. In addition, Au/MXene facilitates electron transport and captures more Ab1 on its surface. Under optimal conditions, the resultant immunosensor displayed an excellent linear range of 0.0001 to 50.0 ng. mL-1. The detection limit was 0.757 pg·mL-1 with excellent selectivity, appreciable reproducibility, and high stability. Moreover, the applicability for determining HER2-ECD in human serum samples indicates its ability to monitor tumor markers clinically.

Proteomic-based electrochemical non-invasive biosensor for early breast cancer diagnosis

Breast cancer is the second highest cause of cancer-related mortality in women worldwide and in the United States, accounting for around 571,000 deaths per year. Early detection of breast cancer increases treatment results and the possibility of a cure. While existing diagnostic modalities such as mammography, ultrasound, and biopsy exist, some are prohibitively expensive, uncomfortable, time-consuming, and have limited sensitivity, necessitating the development of a cost-effective, rapid, and highly sensitive approach such as an electrochemical biosensor. Our research focuses on detecting breast cancer patients using the ECM1 biomarker, which has higher expression in synthetic urine. Our study has two primary objectives: (i) Diverse ECM1 protein concentrations are measured using electrochemical impedance spectroscopy and ELISA. Establishing a standard curve for the electrochemical biosensor by calibrating ECM-1 protein levels using electrochemical impedance spectroscopy. (ii) Validation of the effectiveness of the electrochemical biosensor. This aim entails testing the unknown concentration of ECM1 in the synthetic urine to ensure the efficiency of the biosensor to detect the biomarker in the early stages. The results show that the synthetic urine solution's ECM-1 detection range ranges from 1 pg/ml to 500 ng/ml. This shows that by detecting changes in ECM-1 protein levels in patient urine, the electrochemical biosensor can consistently diagnose breast cancer in its early stages or during increasing recurrence. Our findings highlight the electrochemical biosensor's efficacy in detecting early-stage breast cancer biomarkers (ECM-1) in patient urine. Further studies will be conducted with patient samples and develop handheld hardware for patient usage.

Identification of cancer protein biomarker based on cell specific peptide and its potential role in predicting tumor metastasis

The identification of tumor related membrane protein is important for both cancer diagnosis and targeted therapy. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Targeting peptides are generated by library screening and can recognize their cognate targets with high specificity and affinity. In addition, these functional peptides have been considered to be a valuable molecule for both imaging detection and targeting therapy in oncology. The selected peptides can be used to identify cell-surface protein biomarkers of cancer cells. In our study, the peptide (VECYLIRDNLCIY) derived from the bacteria displaying library worked as a bait to capture its binding partner and aldolase A was identified as the candidate. The results indicated that aldolase A' expression level on the cell membrane was regulated by PI3K and aldolase A located on the membrane could inhibit the aggression of tumors through mediating cell metabolic pathway. Aldolase A could work as the joint for the metabolic and signal pathways related to tumor progression. In our work, we demonstrated a promising technology for selecting and identifying binding partners for cell-specific peptides that enables discovery of new tumor biomarkers, showing great scientific study values and clinical translation potencies. SIGNIFICANCE: MS-based cancer biomarker discovery provides promising target candidates for cancer diagnosis and therapy. However, the inevitable limits make it inconvenient in the process of sample preparation and data analysis. In this way, the small molecular probes show some advantages due to their readily availability and specific binding affinity such as the aptamers screened with SELEX technology and peptides derived from displaying libraries. In the present study, aldolase A was proved to be the membrane binding partner of a specific peptidic ligand towards ZR-75-1 tumor cell. It was discovered that membrane aldolase A was more sensitive and observable than other subcellular fractions in response to cellular metabolic state alteration or glucose availability. In addition, the reduced membrane-localized aldolase A expression indicated a more aggressive tumor phenotype and was accompanied by the upregulation of MMP-2/MMP-9. The non-glycolysis activity endowed it with potential utility as a tumor diagnostic marker and therapeutic target. This work demonstrates the practicability of screened peptide in cancer biomarker discovery, facilitating the development of diagnostic tools and therapeutic approaches to cancer, and markedly improves our understanding of cancer biology.

Molecularly Imprinted Polymers for the Determination of Cancer Biomarkers

Biomarkers can provide critical information about cancer and many other diseases; therefore, developing analytical systems for recognising biomarkers is an essential direction in bioanalytical chemistry. Recently molecularly imprinted polymers (MIPs) have been applied in analytical systems to determine biomarkers. This article aims to an overview of MIPs used for the detection of cancer biomarkers, namely: prostate cancer (PSA), breast cancer (CA15-3, HER-2), epithelial ovarian cancer (CA-125), hepatocellular carcinoma (AFP), and small molecule cancer biomarkers (5-HIAA and neopterin). These cancer biomarkers may be found in tumours, blood, urine, faeces, or other body fluids or tissues. The determination of low concentrations of biomarkers in these complex matrices is technically challenging. The overviewed studies used MIP-based biosensors to assess natural or artificial samples such as blood, serum, plasma, or urine. Molecular imprinting technology and MIP-based sensor creation principles are outlined. Analytical signal determination methods and the nature and chemical structure of the imprinted polymers are discussed. Based on the reviewed biosensors, the results are compared, and the most suitable materials for each biomarker are discussed.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

Iterative principal component analysis method for improvised classification of breast cancer disease using blood sample analysis

Breast cancer is the most common cancer in women occurring worldwide. Some of the procedures used to diagnose breast cancer are mammogram, breast ultrasound, biopsy, breast magnetic resonance imaging, and blood tests such as complete blood count. Detecting breast cancer at an early stage plays an important role in diagnostic and curative procedures. This paper aims to develop a predictive model for detecting the breast cancer using blood samples data containing age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin, and chemokine monocyte chemoattractant protein 1 (MCP-1).The two main challenges encountered in this process are identification of biomarkers and the precision of disease prediction accuracy. The proposed methodology employs principal component analysis in a peculiar approach followed by random forest tree prediction model to discriminate between healthy and breast cancer patients. This approach extracts high communalities, a linear combination of input attributes in a systematic procedure as principal axis elements. The iteratively extracted principal axis elements combined with minimum number of input attributes are able to predict the disease with higher accuracy of classification with increased sensitivity and specificity score. The results proved that the proposed approach generates a higher predictor performance than the previous reported results by opting relevant extracted principal axis elements and attributes that commend the classifier with increased performance measures.

The Key Characteristics of Carcinogens: Relationship to the Hallmarks of Cancer, Relevant Biomarkers, and Assays to Measure Them

The key characteristics (KC) of human carcinogens provide a uniform approach to evaluating mechanistic evidence in cancer hazard identification. Refinements to the approach were requested by organizations and individuals applying the KCs. We assembled an expert committee with knowledge of carcinogenesis and experience in applying the KCs in cancer hazard identification. We leveraged this expertise and examined the literature to more clearly describe each KC, identify current and emerging assays and in vivo biomarkers that can be used to measure them, and make recommendations for future assay development. We found that the KCs are clearly distinct from the Hallmarks of Cancer, that interrelationships among the KCs can be leveraged to strengthen the KC approach (and an understanding of environmental carcinogenesis), and that the KC approach is applicable to the systematic evaluation of a broad range of potential cancer hazards in vivo and in vitro We identified gaps in coverage of the KCs by current assays. Future efforts should expand the breadth, specificity, and sensitivity of validated assays and biomarkers that can measure the 10 KCs. Refinement of the KC approach will enhance and accelerate carcinogen identification, a first step in cancer prevention.See all articles in this CEBP Focus section, "Environmental Carcinogenesis: Pathways to Prevention."

Comparative Proteome Analysis of Breast Cancer Tissues Highlights the Importance of Glycerol-3-phosphate Dehydrogenase 1 and Monoacylglycerol Lipase in Breast Cancer Metabolism

BACKGROUND/AIM

Breast cancer (BC) incidence and mortality rates have been increasing due to the lack of appropriate diagnostic tools for early detection. Proteomics-based studies may provide novel targets for early diagnosis and efficient treatment. The aim of this study was to investigate the global changes occurring in protein profiles in breast cancer tissues to discover potential diagnostic or prognostic biomarkers.

MATERIALS AND METHODS

BC tissues and their corresponding healthy counterparts were collected, subtyped, and subjected to comparative proteomics analyses using two-dimensional gel electrophoresis (2-DE) and two-dimensional electrophoresis fluorescence difference gel (DIGE) coupled to matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF/TOF) to explore BC metabolism at the proteome level. Western blot analysis was used to verify changes occurring at the protein levels.

RESULTS

Bioinformatics analyses performed with differentially regulated proteins highlighted the changes occurring in triacylglyceride (TAG) metabolism, and directed our attention to TAG metabolism-associated proteins, namely glycerol-3-phosphate dehydrogenase 1 (GPD1) and monoacylglycerol lipase (MAGL). These proteins were down-regulated in tumor groups in comparison to controls.

CONCLUSION

GPD1 and MAGL might be promising tissue-based protein biomarkers with a predictive potential for BC.

Systematical Identification of Breast Cancer-Related Circular RNA Modules for Deciphering circRNA Functions Based on the Non-Negative Matrix Factorization Algorithm

Circular RNA (circRNA), a kind of special endogenous RNA, has been shown to be implicated in crucial biological processes of multiple cancers as a gene regulator. However, the functional roles of circRNAs in breast cancer (BC) remain to be poorly explored, and relatively incomplete knowledge of circRNAs handles the identification and prediction of BC-related circRNAs. Towards this end, we developed a systematic approach to identify circRNA modules in the BC context through integrating circRNA, mRNA, miRNA, and pathway data based on a non-negative matrix factorization (NMF) algorithm. Thirteen circRNA modules were uncovered by our approach, containing 4164 nodes (80 circRNAs, 2703 genes, 63 miRNAs and 1318 pathways) and 67,959 edges in total. GO (Gene Ontology) function screening identified nine circRNA functional modules with 44 circRNAs. Within them, 31 circRNAs in eight modules having direct relationships with known BC-related genes, miRNAs or disease-related pathways were selected as BC candidate circRNAs. Functional enrichment results showed that they were closely related with BC-associated pathways, such as ‘KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAYS IN CANCER’, ‘REACTOME IMMUNE SYSTEM’ and ‘KEGG MAPK SIGNALING PATHWAY’, ‘KEGG P53 SIGNALING PATHWAY’ or ‘KEGG WNT SIGNALING PATHWAY’, and could sever as potential circRNA biomarkers in BC. Comparison results showed that our approach could identify more BC-related functional circRNA modules in performance. In summary, we proposed a novel systematic approach dependent on the known disease information of mRNA, miRNA and pathway to identify BC-related circRNA modules, which could help identify BC-related circRNAs and benefits treatment and prognosis for BC patients.

Apoptosis-linked gene 2 promotes breast cancer growth and metastasis by regulating the cytoskeleton

Breast cancer is the most prevalent cancer in women. Although it begins as local disease, breast cancer frequently metastasizes to the lymph nodes and distant organs. Therefore, novel therapeutic targets are needed for the management of this disease. Apoptosis-linked gene 2 (ALG-2) is a calcium-binding protein crucial for diverse physiological processes and has recently been implicated in cancer development. However, it remains unclear whether this protein is involved in the pathogenesis of breast cancer. Here, we demonstrate that the expression of ALG-2 is significantly upregulated in breast cancer tissues and is correlated with clinicopathological characteristics indicative of tumor malignancy. Our data further show that ALG-2 stimulates breast cancer growth and metastasis in mice. ALG-2 also promotes breast cancer cell proliferation, survival, and motility in vitro. Mechanistic data reveal that ALG-2 disrupts the localization of centrosome proteins, resulting in spindle multipolarity and chromosome missegregation. In addition, ALG-2 drives the polarization and migration of breast cancer cells by facilitating the rearrangement of microtubules and microfilaments. These findings reveal a critical role for ALG-2 in the pathogenesis of breast cancer and have important implications for its diagnosis and therapy.

Using Resistin, glucose, age and BMI to predict the presence of breast cancer

Background

The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis.

Methods

For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models.

Results

Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91].

Conclusions

These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.

Electronic supplementary material

The online version of this article (10.1186/s12885-017-3877-1) contains supplementary material, which is available to authorized users.

Functionalized vertical GaN micro pillar arrays with high signal-to-background ratio for detection and analysis of proteins secreted from breast tumor cells

The detection of cancer biomarkers has recently attracted significant attention as a means of determining the correct course of treatment with targeted therapeutics. However, because the concentration of these biomarkers in blood is usually relatively low, highly sensitive biosensors for fluorescence imaging and precise detection are needed. In this study, we have successfully developed vertical GaN micropillar (MP) based biosensors for fluorescence sensing and quantitative measurement of CA15-3 antigens. The highly ordered vertical GaN MP arrays result in the successful immobilization of CA15-3 antigens on each feature of the arrays, thereby allowing the detection of an individual fluorescence signal from the top surface of the arrays owing to the high regularity of fluorophore-tagged MP spots and relatively low background signal. Therefore, our fluorescence-labeled and CA15-3 functionalized vertical GaN-MP-based biosensor is suitable for the selective quantitative analysis of secreted CA15-3 antigens from MCF-7 cell lines, and helps in the early diagnosis and prognosis of serious diseases as well as the monitoring of the therapeutic response of breast cancer patients.

TIMP-3 mRNA expression levels positively correlates with levels of miR-21 in in situ BC and negatively in PR positive invasive BC

BACKGROUND

Breast carcinomas (BC) belong to a heterogeneous group of malignant diseases. Correct categorization of BC based on molecular biomarkers has a very important role in deciding the proper course of therapy for each patient. It has been already shown that the decrease of TIMP metalloproteinase inhibitor 3 (TIMP-3) together with overexpression of microRNA-21 (miR-21) might be involved in the process of BC invasion. This is the first study that examined relationship among miR-21, TIMP-3 mRNA and TIPM-3 protein levels in BC groups formed according to invasiveness.

METHODS

In this study, we used 46 breast cancer samples. Estrogen and progesterone receptor (ER, PR) protein levels were evaluated by immunohistochemistry (IHC) method. TIMP-3 mRNA expression was examined by two-step real-time quantitative PCR (qRT-PCR). Western blot analysis was performed for 16 samples.

RESULTS

Statistically significant differences in TIMP-3 expression levels between invasive groups were discovered in ER positive (ER+) (p=0.015), Her-2 negative (p=0.026) subgroups, and patients without lymph-node metastasis (p=0.039). Interestingly, significant positive correlation was detected between miR-21 and TIMP-3 mRNA levels (P<0.001, ρ=0.949) in the group of in situ tumors. TIMP-3 mRNA expression levels highly negatively correlated with levels of miR-21 in PR+ invasive BCs (p=0.007, ρ=-0.641). TIMP-3 protein levels negatively correlated with miR-21 levels in pure invasive BCs.

CONCLUSION

These data suggest that signaling pathways involved in formation and progression of BCs in groups formed according to invasiveness might be different. Our findings propose that TIMP-3 mRNA expression levels could be significant prognostic parameter, but within specific BC subtypes.

In Situ Hybridization of Breast Cancer Markers

In situ hybridization is an important technique in breast cancer research, which is widely applied in detection of specific nucleic acid sequences. Here, we describe the detailed protocol of fluorescence in situ hybridization and chromogenic in situ hybridization in detection of gene HER2/neu amplification in breast cancer tissues.

Development of NIST standard reference material 2373: Genomic DNA standards for HER2 measurements

NIST standard reference material (SRM) 2373 was developed to improve the measurements of the HER2 gene amplification in DNA samples. SRM 2373 consists of genomic DNA extracted from five breast cancer cell lines with different amounts of amplification of the HER2 gene. The five components are derived from the human cell lines SK-BR-3, MDA-MB-231, MDA-MB-361, MDA-MB-453, and BT-474. The certified values are the ratios of the HER2 gene copy numbers to the copy numbers of selected reference genes DCK, EIF5B, RPS27A, and PMM1. The ratios were measured using quantitative polymerase chain reaction and digital PCR, methods that gave similar ratios. The five components of SRM 2373 have certified HER2 amplification ratios that range from 1.3 to 17.7. The stability and homogeneity of the reference materials were shown by repeated measurements over a period of several years. SRM 2373 is a well characterized genomic DNA reference material that can be used to improve the confidence of the measurements of HER2 gene copy number.

Prognostic significance of Claudin 12 in estrogen receptor-negative breast cancer

AIMS

Altered expression of the Claudin (CLDN) superfamily of tight junction proteins has been reported in breast cancer. The aim of this study was to examine the immunohistochemical expression of CLDN 12 and its prognostic significance in breast cancer tissues.

METHODS

Immunohistochemical expression of CLDN 12 was performed on tissue microarrays consisting of 232 cases of breast carcinoma and correlated with clinicopathological features as well as survival of the patients with breast cancer.

RESULTS

For the estrogen receptor (ER)-negative subgroup of patients with breast cancer, CLDN 12 expression was shown to be an independent predictor of poor overall survival (HR=2.345; p=0.020) and disease-free survival (HR=2.177; p=0.026) but not for the ER-positive tumours.

CONCLUSIONS

The findings suggest that CLDN 12 expression could be clinically useful for predicting the survival of the ER-negative subgroup of patients with breast cancer.

Elevated level of serum glycoprotein bifucosylation and prognostic value in Chinese breast cancer

Aberrant glycosylation is highly associated with cancer progression. The aim of this study was to compare bifucosylated N-glycans in sera obtained from healthy controls and breast cancer patients, with the goal of identifying a potential indicator for monitoring the recurrence and metastasis of breast cancer. A unique structural pattern of bifucosylated N-glycan, with both core and antennary fucosylation, was identified in breast cancer patients. The spectrum of antennary fucosylation was a composite of the standard spectra of Lewis X and H2, indicating a mixture of the two epitopes. Permethylated N-glycans of the glycoproteins extracted from 91 breast cancer patients and 43 healthy controls were detected using linear ion-trap quadrupole-electrospray ionization mass spectrometry, which appeared to be a highly sensitive and useful approach in the detection and identification of N-glycans. To evaluate MS profile data, several statistical tools were applied, including Student'st-test, partial least squares discriminant analysis and receiver-operating characteristic curve. The results showed that the measurement of bifucosylation degree and CEA levels had an improved diagnostic performance compared with that of CEA alone. We compared the potential of bifucosylated N-glycan as an indicator of breast cancer recurrence with the current clinical biomarkers, i.e., CEA, CA 15-3 and CA125. The result revealed that, compared with CEA, CA 15-3 and CA125, the bifucosylation degree of N-glycans could be a more reliable indicator of breast cancer recurrence.

Antibody-based proteomics and biomarker research - current status and limitations

Antibody-based proteomics play a very important role in biomarker discovery and validation, facilitating the high-throughput evaluation of candidate markers. Most proteomics-driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis-free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody-based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody-based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.

Mathematical modeling for novel cancer drug discovery and development

INTRODUCTION

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.

AREAS COVERED

This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.

EXPERT OPINION

Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Gene expression correlation for cancer diagnosis: a pilot study

Poor prognosis for late-stage, high-grade, and recurrent cancers has been motivating cancer researchers to search for more efficient biomarkers to identify the onset of cancer. Recent advances in constructing and dynamically analyzing biomolecular networks for different types of cancer have provided a promising novel strategy to detect tumorigenesis and metastasis. The observation of different biomolecular networks associated with normal and cancerous states led us to hypothesize that correlations for gene expressions could serve as valid indicators of early cancer development. In this pilot study, we tested our hypothesis by examining whether the mRNA expressions of three randomly selected cancer-related genes PIK3C3, PIM3, and PTEN were correlated during cancer progression and the correlation coefficients could be used for cancer diagnosis. Strong correlations (0.68 ≤ r ≤ 1.0) were observed between PIK3C3 and PIM3 in breast cancer, between PIK3C3 and PTEN in breast and ovary cancers, and between PIM3 and PTEN in breast, kidney, liver, and thyroid cancers during disease progression, implicating that the correlations for cancer network gene expressions could serve as a supplement to current clinical biomarkers, such as cancer antigens, for early cancer diagnosis.