Gene Expression Profiling Of Cancer Research Articles

Hepatitis B virus (HBV) codon adapts well to the gene expression profile of liver cancer: an evolutionary explanation for HBV's oncogenic role.

Due to the evolutionary arms race between hosts and viruses, viruses must adapt to host translation systems to rapidly synthesize viral proteins. Highly expressed genes in hosts have a codon bias related to tRNA abundance, the primary RNA translation rate determinant. We calculated the relative synonymous codon usage (RSCU) of three hepatitis viruses (HAV, HBV, and HCV), SARS-CoV-2, 30 human tissues, and hepatocellular carcinoma (HCC). After comparing RSCU between viruses and human tissues, we calculated the codon adaptation index (CAI) of viral and human genes. HBV and HCV showed the highest correlations with HCC and the normal liver, while SARS-CoV-2 had the strongest association with lungs. In addition, based on HCC RSCU, the CAI of HBV and HCV genes was the highest. HBV and HCV preferentially adapt to the tRNA pool in HCC, facilitating viral RNA translation. After an initial trigger, rapid HBV/HCV translation and replication may change normal liver cells into HCC cells. Our findings reveal a novel perspective on virus-mediated oncogenesis.

The evolution of gene expression profiling in breast cancer – A narrative review

Abstract Gene expression profile has revolutionised breast cancer treatment. It has paved the way for developing cancer-specific treatment and predicting the chances of recurrence. Genomic analysis has set the benchmark for personalised cancer therapy. The gene signature tests are mainly used in hormone-positive or luminal breast carcinoma with no or low lymph nodal metastatic carcinoma, and it helps in preventing unnecessary usage of chemotherapy in low-risk groups. The low and high gene expression profiles have made inroads into the new prognostic staging of breast cancers. There are several commercially available gene signature prognostic tests such as 21-Gene Recurrence Score (RS) assay (Oncotype DX), predictor analysis of microarray 50 risk of RS, Amsterdam 70-gene Profile (MammaPrint), Breast Cancer Index and EndoPredict. The gene signature assay can be useful in stratifying hormone-positive breast cancer patients into low and high genomic risk category. The low genomic risk patients can avoid unnecessary use of chemotherapy.

Construction of a co-expression network and prediction of metastasis markers in colorectal cancer patients with liver metastasis.

Colorectal cancer (CRC) is a common global malignancy associated with high invasiveness, high metastasis, and poor prognosis. CRC commonly metastasizes to the liver, where the treatment of metastasis is both difficult and an important topic in current CRC management. Microarrays data of human CRC with liver metastasis (CRCLM) were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database to identify potential key genes. Differentially expressed (DE) genes (DEGs) and DEmiRNAs of primary CRC tumor tissues and metastatic liver tissues were identified. Microenvironment Cell Populations (MCP)-counter was used to estimate the abundance of immune cells in the tumor micro-environment (TME), and weighted gene correlation network analysis (WGCNA) was used to construct the co-expression network analysis. Gene Ontology and Kyoto Encyclopaedia of Gene and Genome (KEGG) pathway enrichment analyses were conducted, and the protein-protein interaction (PPI) network for the DEGs were constructed and gene modules were screened. Thirty-five pairs of matched colorectal primary cancer and liver metastatic gene expression profiles were screened, and 610 DEGs (265 up-regulated and 345 down-regulated) and 284 DEmiRNAs were identified. The DEGs were mainly enriched in the complement and coagulation cascade pathways and renin secretion. Immune infiltrating cells including neutrophils, monocytic lineage, and cancer-associated fibroblasts (CAFs) differed significantly between primary tumor tissues and metastatic liver tissues. WGCN analysis obtained 12 modules and identified 62 genes with significant interactions which were mainly related to complement and coagulation cascade and the focal adhesion pathway. The best subset regression analysis and backward stepwise regression analysis were performed, and eight genes were determined, including F10, FGG, KNG1, MBL2, PROC, SERPINA1, CAV1, and SPP1. Further analysis showed four genes, including FGG, KNG1, CAV1, and SPP1 were significantly associated with CRCLM. Our study implies complement and coagulation cascade and the focal adhesion pathway play a significant role in the development and progression of CRCLM, and FGG, KNG1, CAV1, and SPP1 may be metastatic markers for its early diagnosis.

Comprehensive analysis of liquid-liquid phase separation-related genes in prediction of breast cancer prognosis.

Objective: Liquid-liquid phase separation (LLPS) is a functional unit formed by specific molecules. It lacks a membrane and has been reported to play a crucial role in tumor drug resistance and growth by regulating gene expression and drug distribution. However, whether LLPS could be used to predict cancer prognosis was not clear. This study aimed to construct a prognostic model for breast cancer based on LLPS-correlated genes (LCGs). Methods: LCGs were identified using the PhaSepDB, gene expression profile and clinical characteristics of breast cancer were obtained from TCGA and cBioportal. The PanCancer Atlas (TCGA) cohort was used as the training cohort to construct the prognostic model, while the Nature 2012 and Nat Commun 2016 (TCGA) cohort and GEO data were used as test cohort to perform external verification. Data analysis was performed with R4.2.0 and SPSS20.0. Results: We identified 140 prognosis-related LCGs (pLCGs) (p< 0.01) in all cohorts, 240 pLCGs (p< 0.01) in the luminal cohort, and 28 pLCGs (p< 0.05) in the triple-negative breast cancer (TNBC) cohort. Twelve genes in all cohorts (training cohort: 5/10-year ROC values were 0.76 and 0.77; verification cohort: 5/10-year ROC values were 0.61 and 0.58), eight genes in the luminal cohort (training cohort: 5/10-year ROC values were 0.79 and 0.75; verification cohort: 5/10-year ROC values were 0.62 and 0.62), and four genes in the TNBC cohort (training cohort: 5/10-year ROC values were 0.73 and 0.79; verification cohort: 5/10-year ROC values were 0.55 and 0.54) were screened out to construct the prognostic prediction model. The 17-gene risk-score was constructed in all cohorts (1/3/5-year ROC values were 0.88, 0.83, and 0.81), and the 11-gene risk-score was constructed in the luminal cohort (1/3/5-year ROC values were 0.67, 0.85 and 0.84), and the six-gene risk-score was constructed in the TNBC cohort (1/3/5-year ROC value were 0.87, 0.88 and 0.81). Finally, the risk-score and clinical factors were applied to construct nomograms in all cohorts (1/3/5-year ROC values were 0.89, 0.79 and 0.75, C-index = 0.784), in the luminal cohort (1/3/5-year ROC values were 0.84, 0.83 and 0.85, C-index = 0.803), and in the TNBC cohort (1/3/5-year ROC values were 0.95, 0.84 and 0.77, C-index = 0.847). Discussion: This study explored the roles of LCGs in the prediction of breast cancer prognosis.

Copper metabolism patterns and tumor microenvironment characterization in colon adenocarcinoma.

Copper participates in biological processes such as oxygen metabolism and iron uptake, and is a key factor in immune regulation. Based on the transcription data, mutation data and clinical data of colon adenocarcinoma (COAD) patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Profiling Interactive Analysis (GEPIA2) database, the expression and mutation of copper metabolization-related genes in COAD patients and their correlation with tumor immune microenvironment were analyzed. Copper metabolization-related genes (CMRGs) were used to construct COAD subtypes and prognostic risk models for COAD patients. Furthermore, Kaplan-Meier (K-M) curve and receiver operating characteristic (ROC) curve were used to analyze the clinical value of COAD subtypes and genotyping models in distinguishing clinical characteristics of patients, and the immune infiltration of patients with different genotypes was analyzed. Finally, the clinical tissue samples from COAD patients were used to analyze the mRNA expression of genes in risk model between tumor and normal tissues by the method of Polymerase Chain Reaction (PCR). Of the 479 CMRGs, 68 genes were differentially expressed in normal and tumor tissues of COAD patients in TCGA and GEPIA2. Two subtypes with different clinical and immunological characteristics were identified by using 482 genes related to copper metabolism. Finally, a prognostic risk model consisting of five CMRGs was constructed, which could not only predict the prognosis of patients, but also correlated with COAD subtypes. In addition, some genes (glutathione S-transferase mu 1, cyclin D1and cytochrome P450 family 2 subfamily S member 1) in risk model was show significant difference between normal and tumor tissues. The COAD subtypes identified by CMRGs can help clinically distinguish patients with different prognosis and tumor progression, and the risk score can assist in clinical evaluation of patient prognosis, serving as a valuable biomarker for COAD immunotherapy.

223P Gene expression profiles in endocrine-resistant breast cancer

Around 75% of breast cancer (BC) patients' tumors express the treatment-predictive biomarker estrogen receptor alpha (ER/ESR1) and are accordingly offered endocrine therapy. One-third of patients develop endocrine resistance, a majority with tumors that retain ER expression. For most patients, the resistance mechanism is still unknown but factors such as alterations in ER downstream signaling have been investigated. This study aims to examine the gene expression programs of endocrine-resistant tumors. A cohort of verified endocrine-resistant BC patients diagnosed in 2005-2006 and 2008-2012 was retrospectively collected at the Karolinska University Hospital in Stockholm, Sweden. Cases (N=56) were defined as patients with an ER+ and human epidermal growth factor receptor 2 (HER2)-negative primary tumor who developed an ER+/HER2- relapse during endocrine therapy. Patients with an ER+/HER2- tumor without progression or relapse at 10 years of follow-up were defined as controls (N=56). RNA was extracted from formalin-fixed paraffin-embedded tissue and analyzed using Affymetrix Clariom D microarrays. Transcriptome Analysis Console (TAC) Software, Gene Set Enrichment (GSEA) application, and R were used for gene expression data analysis. The initial analysis generated differentially expressed genes, where in general more genes were upregulated in primary tumors of cases who eventually relapse versus controls and downregulated in relapse versus primary tumors of cases. Subsequent investigation of gene sets showed upregulations in epithelial-mesenchymal transition, androgen response, MYC targets, and reactive oxygen species in primary tumors of cases versus controls. We found upregulation in metabolism-associated gene sets and downregulation of estrogen response, epithelial-mesenchymal transition, androgen response, and ESR1 regulation in relapses of cases compared to their primary tumors. This study of a unique cohort of endocrine-resistant tumors suggests that specific signaling pathways are associated with the development of endocrine resistance in BC. These gene expression signatures may guide further studies on the resistance mechanisms and develop future diagnostic tools for therapy response prediction.

19P Gene expression profile (GEP) of extensive small-cell lung cancer (eSCLC) patients (pts) receiving first-line platinum-etoposide plus atezolizumab (PEA)

19P Gene expression profile (GEP) of extensive small-cell lung cancer (eSCLC) patients (pts) receiving first-line platinum-etoposide plus atezolizumab (PEA)

Hospital Rurality and Gene Expression Profiling for Early-Stage Breast Cancer among Iowa Residents (2010-2018).

Objective Given the challenges rural cancer patients face in accessing cancer care as well as the slower diffusion and adoption of new medical technologies among rural providers, the aim of our study was to examine trends in gene expression profiling (GEP) testing and evaluate the association between hospital rurality and receipt of GEP testing. Methods Data from the Iowa Cancer Registry (ICR) were used to identify women with newly diagnosed, histologically confirmed breast cancer from 2010 through 2018 who met eligibility criteria for GEP testing. Patients were allocated to the hospitals where their most definitive surgical treatment was received, and Rural-Urban Commuting Area codes were used to categorize hospitals into urban (N = 43), large rural (N = 16), and small rural (N = 48). Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression to evaluate the association between hospital rurality and GEP test use, adjusting for demographic and clinical characteristics. The association between test result and treatment received was assessed among patients who received Oncotype DX (ODX) testing. Results Of 6,726 patients eligible for GEP test use, 46% (N = 3,069) underwent testing with 95% receiving ODX. While overall GEP testing rates increased over time from 42% between 2010 and 2012 to 51% between 2016 and 2018 (Ptrend < 0.0001), use continued to be the lowest among patients treated at hospitals in small rural areas. The odds of GEP testing remained significantly lower among patients treated at hospitals located in small rural areas (aOR 0.55; 95% CI 0.43–0.71), after adjusting for demographic and clinical characteristics. ODX recurrence scores were highly correlated with chemotherapy use across all strata of hospital rurality. Conclusions GEP testing continues to be underutilized, especially among those treated at small rural hospitals. Targeted interventions aimed at increasing rates of GEP testing to ensure the appropriate use of adjuvant chemotherapy may improve health outcomes and lower treatment-related costs.

Gene expression profile of human colorectal cancer identified NKTR as a biomarker for liver metastasis.

Objective: Liver metastasis is one of the prognostic factors of colorectal cancer (CRC). The aim of this study is to identify biomarkers that facilitate easier detection of liver metastasis.Methods: Significance Analysis of Microarrays (SAM) and Array Data Analyzer (ADA) were applied used for the analysis of differentially differently expressed mRNAs. mRNA expression was verified by quantitative real-timer reverse transcriptiontase polymerase chain reaction (qRT-PCR). Immunohistochemistry were was used to show natural killer-tumor recognition (NKTR) expression in CRC. NKTR-knockdown CRC cells were constructed obtained by using short hairpin RNA (shRNA). Followed by CCK-8 assay, plate colony formation test, and transwell assay were used to evaluate the influence of NKTR on cell proliferation, migration, and invasion in vitro.Results: SAM yielded showed 256 up-regulated and 224 down-regulated differentially differently expressed genes. Seven genes were identified by using ADA, tools and four genes were verified by using qRT-PCR. Three genes (metastasis associated lung adenocarcinoma transcript 1 (MALAT1), nuclear factor I/B (NKTR), and nuclear factor I/B (NFIB)) showed a statistically significant considerabley difference between CRC with and liver metastasis and CRC without liver metastasis. Immunohistochemical analysis showed that NKTR expression was much lower in primary CRC with liver metastasis than that in primary CRC without liver metastasis. The NKTR protein plays a role in the lytic function of natural killer (NK) cells and it has been rarely studied in the CRC. The down-regulation of NKTR by shRNA interference in CRC cells increased cell proliferation, migration, and invasion in vitro.

MiR-489 Confines Uncontrolled Estrogen Signaling through a Negative Feedback Mechanism and Regulates Tamoxifen Resistance in Breast Cancer

Approximately 75% of diagnosed breast cancer tumors are estrogen-receptor-positive tumors and are associated with a better prognosis due to response to hormonal therapies. However, around 40% of patients relapse after hormonal therapies. Genomic analysis of gene expression profiles in primary breast cancers and tamoxifen-resistant cell lines suggested the potential role of miR-489 in the regulation of estrogen signaling and development of tamoxifen resistance. Our in vitro analysis showed that loss of miR-489 expression promoted tamoxifen resistance, while overexpression of miR-489 in tamoxifen-resistant cells restored tamoxifen sensitivity. Mechanistically, we found that miR-489 is an estrogen-regulated miRNA that negatively regulates estrogen receptor signaling by using at least the following two mechanisms: (i) modulation of the ER phosphorylation status by inhibiting MAPK and AKT kinase activities; (ii) regulation of nuclear-to-cytosol translocation of estrogen receptor α (ERα) by decreasing p38 expression and consequently ER phosphorylation. In addition, miR-489 can break the positive feed-forward loop between the estrogen-Erα axis and p38 MAPK in breast cancer cells, which is necessary for its function as a transcription factor. Overall, our study unveiled the underlying molecular mechanism by which miR-489 regulates an estrogen signaling pathway through a negative feedback loop and uncovered its role in both the development of and overcoming of tamoxifen resistance in breast cancers.

CFDP1 promotes hepatocellular carcinoma progression through activating NEDD4/PTEN/PI3K/AKT signaling pathway.

It is being increasingly reported that the Cranio Facial Development Protein 1 (CFDP1) plays a significant role in the onset and progression of tumors. Nonetheless, the underlying mechanisms associated with CFDP1 that contribute to hepatocellular carcinoma (HCC) and the specific biological role of CFDP1 remain vague. The Gene Expression Omnibus (GEO) database was analyzed to obtain the gene expression profiles as well as the matching clinical data of HCC patients. The gene co-expression network was developed by means of weighted gene co-expression network analysis (WGCNA) to screen for possible biomarkers that could be used for the purpose of predicting prognosis. The Cancer Genome Atlas (TCGA) and Gene Expression Profile Interaction Analysis (GEPIA) databases were used to assess the relationship between survival and expression. In addition, we identified the underlying mechanism associated with CFDP1 by analyzing the KEGG pathway database, applying the GSEA and GeneCards analysis method. We performed a sequence of experiments (in vivo and in vitro) for the purpose of investigating the specific function of CFDP1 in liver cancer. The obtained results revealed high expression of CFDP1 in HCC tissues and cell lines. A positive correlation between the overexpression of CFDP1 and the adverse clinicopathological features was observed. Moreover, we observed that the low recurrence-free survival and overall survival were associated with CFDP1 overexpression. In addition, GeneCards and GSEA analysis showed that CFDP1 may interact with NEDD4 and participate in PTEN regulation. Meanwhile, CFDP1 can promote the malignant development of liver cancer in vivo and in vitro. The western blotting technique was also employed so as to examine the samples, and the findings demonstrated that CFDP1 enhanced the malignancy of HCC via the NEDD4-mediated PTEN/PI3K/AKT pathway. We highlighted that CFDP1 played an oncogenic role in HCC and was identified as a possible clinical prognostic factor and a potential novel therapeutic target for HCC.

M6Acancer-Net: Identification of m6A-mediated cancer driver genes from gene-site heterogeneous network.

N6-methyladenosine (m6A) is the most abundant eukaryotic modification internal mRNA, which plays the crucial roles in the occurrence and development of cancer. However, current knowledge about m6A-mediated functional circuit and key genes targeted by m6A methylation in cancer is mostly elusive. Thus, here we proposed a novel network-based approach (called m6Acancer-Net) to identify m6A-mediated driver genes and their associated network in specific type of cancer, such as acute myeloid leukemia. m6A-mediated cancer driver genes are defined as genes mediated by m6A methylation, significantly mutated, and functionally interacted in cancer. m6Acancer-Net identified the m6A-mediated cancer driver genes by combining gene functional interaction network with RNA methylation, gene expression and mutation information. A cancer-specific gene-site heterogeneous network was firstly constructed by connecting the m6A site co-methylation network with the functional interaction pruned gene co-expression network generated from large scale gene expression profile of specific cancer. Then, the functional m6A-mediated genes were identified by selecting the m6A regulators as seed genes to perform the random walk with restart algorithm on the gene-site heterogeneous network. Finally, m6A-mediated cancer driver gene subnetworks were constructed by performing the heat diffusion of mutation frequency for functional m6A-mediated genes in protein-protein interaction networks. The experimental results of m6Acancer-Net on the acute myeloid leukemia (AML) and glioblastoma multiforme (GBM) data from TCGA project show that the m6A-mediated caner driver genes identified by m6Acancer-Net are targeted by m6A regulators, and mediate significant cancer-related pathways. They play crucial roles in development and prognostic stratification of cancer. Moreover, 15 m6A-mediated cancer driver genes identified in AML are validated by literatures to mediate AML progress, and 14 m6A-mediated cancer driver genes identified in GBM are validated by literatures to participate in development of GBM. m6Acancer-Net is reliable to identify the functionally significant m6A-mediated driver genes in specific cancer, and it can effectively facilitate the understanding of regulatory and therapeutic mechanism of cancer driver genes in epitranscriptome layer.

Study on the effect and mechanisms of SIGLEC-15 on laryngeal squamous cell carcinoma

Objective: To investigate the effect of sialic acid combined with immunoglobulin-like lectin 15 (SIGLEC-15) on laryngeal squamous cell carcinoma (LSCC) and underlying mechanisms. Methods: The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE) and Gene Expression Profiling Interactive Analysis (GEPIA2) databases were used for bioinformatics analysis. Cell Counting Kit-8 (CCK8), flow cytometry, and Transwell method were used respectively to detect proliferation, apoptosis, cell cycle, metastasis and invasion behaviors of the cells. Gene chip method was used for detecting up-regulated and down-regulated genes and performing enrichment analysis of differential genes. Western Blotting (WB) and Quantitative Real-time PCR (qPCR) were used to detect the expressions of proteins. Tumor formation experiments in nude mice were used to detect the effect of SIGLEC-15 on the growth of transplanted tumors. Wilcoxon rank sum test, t-test and Log-rank test were used for statistical analysis. Results: SIGLEC-15 was highly expressed in laryngeal squamous cell carcinoma and closely related to life in being. TU686SIGLEC-15-with low expression of SIGLEC-15 was constructed. Compared to TU686SIGLEC-15+, TU686SIGLEC-15-showed significantly reduced activities of proliferation (48 h: 1.32±0.23 vs. 2.56±0.37, t=6.59, P<0.05), migration (1 036.52±51.22 vs. 1 819.62±180.24, t=7.22, P<0.05) and invasion (469.21±112.25 vs. 961.45±102.03, t=7.85, P<0.05); early increased apoptosis ((23.27±1.12)% vs. (5.64±1.61)%, t=11.32, P<0.05); blocked cell cycle at G0/G1 ((59.32±3.65)% vs. (35.46±3.57)%, t=9.85, P<0.05); the knockdown of SIGLEC-15 resulted in up-regulation of 864 genes, down-regulation of 357 genes, with significant changes in molecules of cell cycle, apoptosis and JAK/STAT signal pathways, and the expressions of p-JAK2, p-STAT3, Caspase-3, Bad, Bcl-2, and Cyclin d1 proteins. Tumor formation experiments in nude mice showed that at 8 weeks after the tumors were implanted, the growth transplanted tumors of TU686 SIGLEC-15-cell group was slower than that of TU686 SIGLEC-15+cell group, with significant difference in the mean tumor weights between two groups ((0.382±0.054) g vs. (1.277±0.126) g, t=8.44, P<0.05), while the expression of SIGLEC-15 was lower in the transplanted tumors of SIGLEC-15 knockdown group compared to control group, with significant difference(11.29±2.17 vs. 36.25±7.56, t=9.28, P<0.05). Conclusion: SIGLEC-15 is highly expressed in LSCC and can promote the progression of LSCC through the JAK2-STAT3 pathway.

Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB.

Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors.

Gene expression profiling for breast cancer in the Philippines: Real-world data on feasibility, possible disparities, and clinical implications for LMICs.

e12569 Background: Racial, biological and socioeconomic disparities have most recently been shown to impact on specific molecular subtypes of breast tumorsand on its clinical implications including the decision to omit adjuvant chemotherapy. Despite considerable logistical and cost constraints, we provide the first-ever documented and tangible profile of gene expression of breast cancers in the Philippines for a better understanding of its role in facilitating future testing and health policy making in an LMIC setting. Methods: This is a case series review of the 70-gene (MammaPrint) and 80-gene (BluePrint) signature done among Filipino patients from Dec 2013 to Nov 2021. Data on demographics, tumor characteristics, adjuvant therapy received and clinical risk assessment in the MINDACT study to c-low or c-high were gathered and analyzed. Results: Thirty-six females aged 36-75 years with invasive breast carcinoma Stage I to IIB had the following profile: Low Risk Luminal A (19, 52.8%), High Risk Luminal B (7, 19.4%), High Risk Her-2 (3, 8.3%), High Risk Basal (7, 19.4%). All low-risk patients underwent adjuvant endocrine therapy alone thus foregoing otherwise clinically recommended chemotherapy. No significant trend (in age, laterality, tumor grade and stage) can be discerned within all stratification groups except in the following areas: Histology: low risk profile in lobular carcinoma (n=2) Tumor Size: high risk profile in all tumors measuring 4.1 – 5 cms (n=4) Nodal Status: low risk profile in 6 of 8 patients with node+ disease LVI among node- patients: low risk profile in all 6 LVI+ tumors HR status: high risk profile in all 7 tumors with HR- status Her-2 neu status: good IHC test correlation with genomic subtype HR and Her-2 neu status: HR+her2- status in all 19 low risk tumors while high risk basal subtype in all 5 HR-her2- or triple-negative (TNBC) tumors Clinical risk in MINDACT: low genomic risk in 11 of 25 (44%) c-high patients. Conclusions: Gene profiling of breast cancer in a resource-limited setting is feasible and imperative to avoid chemotherapy and/or overtreatment. Clinically relevant disparities may exist which in turn can affect outcomes, decisions and health policies in adjuvant therapy and overall cancer care.[Table: see text]

Lifetime ovulatory years and ovarian cancer gene expression profiles

BackgroundGreater ovulatory years is associated with increased ovarian cancer risk. Although ovulation leads to an acute pro-inflammatory local environment, how long-term exposure to ovulation impacts ovarian carcinogenesis is not fully understood. Thus, we examined the association between gene expression profiles of ovarian tumors and lifetime ovulatory years to enhance understanding of associated biological pathways.MethodsRNA sequencing data was generated on 234 invasive ovarian cancer tumors that were high-grade serous, poorly differentiated, or high-grade endometrioid from the Nurses’ Health Study (NHS), NHSII, and the New England Case Control Study. We used linear regression to identify differentially expressed genes by estimated ovulatory years, adjusted for birth decade and cohort, overall and stratified by menopausal status at diagnosis. We used false discovery rates (FDR) to account for multiple testing. Gene set enrichment analysis (GSEA) with Cancer Hallmarks, KEGG, and Reactome databases was used to identify biological pathways associated with ovulatory years.ResultsNo individual genes were significantly differentially expressed by ovulatory years (FDR > 0.19). However, GSEA identified several pathways that were significantly associated with ovulatory years, including downregulation of pathways related to inflammation and proliferation (FDR < 1.0 × 10–5). Greater ovulatory years were more strongly associated with downregulation of genes related to proliferation (e.g., E2F targets, FDR = 1.53 × 10–24; G2M checkpoints, FDR = 3.50 × 10–22) among premenopausal versus postmenopausal women at diagnosis. The association of greater ovulatory years with downregulation of genes involved in inflammatory response such as interferon gamma response pathways (FDR = 7.81 × 10–17) was stronger in postmenopausal women.ConclusionsOur results provide novel insight into the biological pathways that link ovulatory years to ovarian carcinogenesis, which may lead to development of targeted prevention strategies for ovarian cancer.

LINC00922 promotes deterioration of gastric cancer.

Several studies have demonstrated the association of lncRNAs with a variety of cancers. Here, we explored the role of LINC00922 in gastric cancer (GC) using bioinformatics approaches and in vitro experiments. We examined the expression of LINC00922 and the prognosis of GC patients based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA). LINC00922-related genes were identified by the Multi Experiment Matrix (MEM) database and The Atlas of Noncoding RNAs in Cancer (TANRIC), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction analysis. The significance of LINC00922 in cell proliferation, apoptosis, invasion and migration was assessed by MTT assay, flow cytometry, Transwell assay and wound-healing assay. The expression of LINC00922 was increased in GC tissues compared with adjacent non-tumor tissues, and increased LINC00922 expression was correlated with poor overall survival and disease-free survival. In addition, 336 overlapping genes were identified by the MEM database and TANRIC and found to be involved in GC-related biological processes, such as cell adhesion and migration, as well as TGF-β signaling. In the protein-protein interaction network, hub genes, such as FSTL3 and LAMC1, were identified. LINC00922 overexpression significantly promoted cell proliferation and invasion in vitro, whereas LINC00922 knockdown exerted opposite effects. In summary, our findings indicate that LINC00922 is overexpressed in GC tissues, suggesting that it might play a role in the development and progression of GC, and thus, it might serve as a prognostic indicator of GC.

A novel risk prediction model of pyroptosis-related genes for the prognosis and immunotherapy response of endometrial cancer.

This study aimstodevelopariskpredictionmodel of pyroptosis-related genes based on its impact on immunotherapy sensitivityof uterine corpus endometrial carcinoma (UCEC), one of the most common and threatening gynecological malignancies. Through multiple bioinformatics analysis, we obtained raw counts of RNA-sequencing data and corresponding clinical information related to UCEC from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) to investigate the potential mechanisms of differentially expressed pyroptosis-related genes (DEPRGs), including the correlation between DEPRGs and prognosis, tumor immune microenvironment and the immunotherapy sensitivity of UCEC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis were used to figure out the functional differences. Furthermore, a mRNA-miRNA-lncRNA network was constructed to identify potentialimpact of pyroptosis ontumorprogression. In this study, we achieved six DEPRGs (CASP3, GPX4, GSDMD, NOD2, PYCARD and TIRAP) and constructed a 6-gene signature which classified UCEC patients in the TCGA cohort into a low-risk group or a high-risk group. Patients in the low-risk group showed significantly longer survival time (p=0.000373). Theriskscorewasalso confirmedasanindependentprognosticfactorcombining with the clinical characteristics. GO and KEGG functional analysis revealed the possible molecular mechanisms by which six DEPRGs influence anti-tumor immunity in UCEC patients. In addition, we found that two DEPRGs (GPX4, TIRAP) were not only significantly associated with tumor mutational burden (TMB) or microsatellite Instability (MSI), but also involved in regulating the number and function of CD8+ cells. Upon comprehensive bioinformatics analysis, it was concluded that pyroptosis-related genes (PRGs) could predict the prognosis of EC patients and be affected in modulating theanti-tumorimmune responsesforpatientswithEC.

Microarray analysis of breast cancer gene expression profiling in response to 2-deoxyglucose, metformin, and glucose starvation

BackgroundBreast cancer (BC) is the most frequently diagnosed cancer in women. Altering glucose metabolism and its effects on cancer progression and treatment resistance is an emerging interest in BC research. For instance, combining chemotherapy with glucose-lowering drugs (2-deoxyglucose (2-DG), metformin (MET)) or glucose starvation (GS) has shown better outcomes than with chemotherapy alone. However, the genes and molecular mechanisms that govern the action of these glucose deprivation conditions have not been fully elucidated. Here, we investigated the differentially expressed genes in MCF-7 and MDA-MB-231 BC cell lines upon treatment with glucose-lowering drugs (2-DG, MET) and GS using microarray analysis to study the difference in biological functions between the glucose challenges and their effect on the vulnerability of BC cells.MethodsMDA-MB-231 and MCF-7 cells were treated with 20 mM MET or 4 mM 2-DG for 48 h. GS was performed by gradually decreasing the glucose concentration in the culture medium to 0 g/L, in which the cells remained with fetal bovine serum for one week. Expression profiling was carried out using Affymetrix Human Clariom S microarrays. Differentially expressed genes were obtained from the Transcriptome Analysis Console and enriched using DAVID and R packages.ResultsOur results showed that MDA-MB-231 cells were more responsive to glucose deprivation than MCF-7 cells. Endoplasmic reticulum stress response and cell cycle inhibition were detected after all three glucose deprivations in MDA-MB-231 cells and only under the metformin and GS conditions in MCF-7 cells. Induction of apoptosis and inhibition of DNA replication were observed with all three treatments in MDA-MB-231 cells and metformin-treated MCF-7 cells. Upregulation of cellular response to reactive oxygen species and inhibition of DNA repair mechanisms resulted after metformin and GS administration in MDA-MB-231 cell lines and metformin-treated MCF-7 cells. Autophagy was induced after 2-DG treatment in MDA-MB-231 cells and after metformin in MCF-7 cells. Finally, inhibition of DNA methylation were observed only with GS in MDA-MB-231 cells.ConclusionThe procedure used to process cancer cells and analyze their expression data distinguishes our study from others. GS had the greatest effect on breast cancer cells compared to 2-DG and MET. Combining MET and GS could restrain both cell lines, making them more vulnerable to conventional chemotherapy.

Protein Structure Readouts of Cancer Drivers for Precision Medicine.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of cancer or a transformed cell, the initiation and progression are driven by only a few mutational events, viz., driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions, thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient's cancer cell gained enormous attention recently for designing better targeting therapies and paving the way for precision cancer medicine. With rolling advancements in high-throughput omic technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures and their potential application in developing precision cancer therapies.