Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy.

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Glycolysis regulation in tumor-associated macrophages: Its role in tumor development and cancer treatment.

Tumor-associated macrophages constitute the main cell population in the tumor microenvironment and play a crucial role in regulating the microenvironment composition. Emerging evidence has revealed that the metabolic profile determines the tumor-associated macrophage phenotype. Tumor-associated macrophage function is highly dependent on glucose metabolism, with glycolysis being the major metabolic pathway. Recent reports have demonstrated diversity in glucose flux of tumor-associated macrophages and complex substance communication with cancer cells. However, how the glucose flux in tumor-associated macrophages connects with glycolysis to influence tumor progression and the tumor microenvironment is still obscure. Moreover, while the development of single-cell sequencing technology allows a clearer and more accurate classification of tumor-associated macrophages, the metabolic profiles of tumor-associated macrophages from the perspective of single-cell omics has not been well summarized. Here, we review the current state of knowledge on glucose metabolism in tumor-associated macrophages and summarize the metabolic profiles of different tumor-associated macrophage subtypes from the perspective of single-cell omics. Additionally, we describe the current strategies targeting glycolysis in tumor-associated macrophages for cancer therapy.

Molecular Subtypes Based on PANoptosis Genes and Characteristics of Immune Infiltration in Cutaneous Melanoma.

Cutaneous melanoma (CM) remains the most life-threatening form of skin cancer. Further risk stratification and search for new prognostic targets for CM are of positive clinical significance. PANoptosis is defined as an inflammatory programmed cell death mediated by the PANoptosome complex and cannot be characterized by pyroptosis, apoptosis or necroptosis alone. Although PANoptosis is closely associated with many diseases including cancer, it has not been reported in CM. Combined with GTEx and TCGA database, we extracted 14 PANoptosis-related genes (PAGs). The molecular subtypes of CM were then analyzed by PAGs and their associations with the immune microenvironment and immunotherapy reactivity were analyzed. LASSO and univariate Cox analysis was performed on PAGs-related differentially expressed genes to establish PAGs characteristics that could effectively predict the prognosis of CM patients. Immune infiltration, tumor mutation burden analysis, immunotherapy response and drug sensitivity analysis were used to further analyze the causes of prognostic differences. Our study provides a new perspective on the role of PANoptosis in CM.

Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors.

Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.

SLC7A7 is a prognostic biomarker correlated with immune infiltrates in non-small cell lung cancer.

BACKGROUND: SLC7A7 (solute carrier family 7, amino acid transporter light chain, y + L system, member 7) is a critical gene in the regulation of cationic amino acid transport. However, the relationships between SLC7A7 and prognosis and tumor-infiltrating lymphocytes in different cancers remain unclear. METHODS: SLC7A7 expression was analyzed using the Oncomine database and Tumor Immune Estimation Resource (TIMER) site. The enrichment of the GO (Gene Oncology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways was conducted by DAVID. We evaluated the influence of SLC7A7 on clinical prognosis using the PrognoScan database. The functional state of SLC7A7 in various types of cancers was analyzed by CancerSEA. The relationships between SLC7A7 and cancer immune infiltrates was investigated by TIMER. Furthermore, correlations between SLC7A7 expression and gene marker sets of immune infiltrates were analyzed by TIMER and Gene Expression Profiling Interactive Analysis (GEPIA). The expression of SLC7A7 was verified by GEO database and immunohistochemistry. RESULTS: A lung cancer cohort study (GSE31210) showed that high SLC7A7 expression was associated with poor overall survival (OS) and relapse-free survival (RFS). In addition, SLC7A7 had a significant impact on the prognosis of diverse cancers. SLC7A7 expression was positively correlated with infiltrating levels of CD4 + and CD8 + T cells, macrophages, neutrophils and dendritic cells (DCs) in non-small cell lung cancer (NSCLC). SLC7A7 expression was also strongly correlated with various immune marker sets in NSCLC. CONCLUSIONS: These results indicated a role for SLC7A7 in infiltration of CD8 + T cells, CD4 + T cells, tumor-associated macrophages (TAMs), neutrophils and DCs in multiple cancers, and regulation of T cell exhaustion and Tregs in NSCLC. These findings suggest that SLC7A7 could be served as a biomarker for prognosis and immune infiltration in NSCLC.

Candidate prognostic biomarkers and prediction models for high-grade serous ovarian cancer from urinary proteomics.

High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.

Survival after neoadjuvant chemotherapy or chemoradiotherapy for resectable esophageal squamous cell carcinoma: A pooled analysis of randomized clinical trials.

BACKGROUND: The comparison of neoadjuvant chemoradiotherapy (nCRT) versus neoadjuvant chemotherapy (nCT) for locally advanced esophageal squamous cell carcinoma (ESCC) remains inconclusive, and the optimal regimen is still under investigation. METHODS: Prospective randomized clinical trials were systematically searched in electronic databases from inception to Oct 2023. A graphical reconstructive algorithm was employed to extract time-to-event outcomes from Kaplan-Meier curves presented in the original studies. Using reconstructed individual patient data, summary overall survival (OS) and disease progression-free survival (DFS) for nCRT versus nCT, primarily doublet chemotherapy were recalculated. Hazard Ratios (HRs) of OS and DFS reported were also pooled by the fixed-effects model. RESULTS: A total of 6 randomized clinical trials comprising 1162 patients were included in our analysis. In the individual patient data (IPD) pooled analysis, a significant OS benefit was found for nCRT in ESCC (HR=0.81, 95 %CI:0.67-0.98, p=0.029), compared with the treatment of nCT. The median overall survival time were 53 months (95 %CI:41.9-67.7 m) and 66 months(95 %CI:57.2-NA) respectively in the nCT and nCRT groups. Additionally, a significant improvement in PFS for nCRT compared to nCT in the IPD pooled analysis (HR=0.79,95 %CI:0.64-0.98; p=0.027). Consistent with above results, the pooled HRs of OS and DFS for nCRT versus nCT were 0.78 (95 % CI 0.65-0.92, p=0.004) and 0.79 (95 % CI: 0.65-0.97, p=0.02), respectively. Notably, no substantial heterogeneity across studies was observed. CONCLUSIONS: Our findings indicate that nCRT offers better survival outcomes for ESCC, at least when compared to neoadjuvant doublet chemotherapy.This evidence continues to support the clinical practice of employing nCRT in locally advanced resectable ESCC.

Proteomic landscape of epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

ncRNAs mediated RPS6KA2 inhibits ovarian cancer proliferation via p38/MAPK signaling pathway.

Background: Ovarian cancer is the most lethal gynecology malignancy in the world, therefore, research on the molecular biological mechanism of ovarian cancer tumorigenesis and progression has received widespread attention. Methods: We identified RPS6KA2 as the prognosis-related gene of ovarian cancer from TCGA, GSE26712 and GSE26193 database via bioinformatic analysis. qRT-PCR and western blot detected the differential expression of RPS6KA2 in normal ovaries and ovarian cancer tissues. The biological functions of RPS6KA2 were verified by in vitro and in vivo. GSEA analysis was used to select candidate signaling pathway of RPS6KA2 which was further verified by western blot. The possible binding sites of RPS6KA2 with miRNAs and circRNAs were predicted by bioinformatics analysis, and then a circRNA-miRNA-mRNA interaction network was constructed. Results: We found the expression of RPS6KA2 was down-regulated in ovarian cancer tissues. Overexpression of RPS6KA2 could suppress cell proliferation, whereas knockdown of RPS6KA2 had the opposite effects on proliferation. GSEA analysis showed that the MARK signaling pathway was closely associated with RPS6KA2. Bioinformatics analysis and dual-luciferase reporter assay showed that RPS6KA2 was regulated with miR-19a-3p, miR-106a-5p and miR-519d-3p. Further analysis showed that circFAM169A was the common ceRNA of miR-19a-3p, miR-106a-5p and miR-519d-3p. Dual-luciferase reporter assay showed the relationship of circFAM169A and miR-106a-5p and miR-519d-3p. After network analysis, one circRNA-miRNA-mRNA axis (circFAM169A/miR-106a-5p, miR-519d-3p/RPS6KA2) was identified. Conclusions: We demonstrated that circFAM169A/miR-106a-5p, miR-519d-3p mediated low expression of RPS6KA2 could affect the proliferation of ovarian cancer cells via p38/MAPK signaling pathway.

Development of anoikis-related long non-coding RNA signature associated with prognosis and immune landscape in cutaneous melanoma patients.

BACKGROUND: Anoikis is involved in many critical biological processes in tumors; however, function in CM is still unknown. In this study, the relevance between Anoikis-related lncRNAs (ARLs) and the clinicopathological characteristics of patients with CM was comprehensively assessed. METHODS: Through analysis of TCGA dataset, ARLs were identified by using TCGA dataset. Based on the ARLs, a risk model was established to anticipate the prognosis of patients with CM, besides, the prediction accuracy of the model was evaluated. The immune infiltration landscape of patients with CM was assessed comprehensively, and the correlation between ARLs and immunity was elucidated. Immunotherapy and drug sensitivity analyses were applied to analyze the treatment response in patients with CM with diverse risk scores. Different subgroups were distinguished among the patients using consensus cluster analysis. RESULTS: A risk model based on six ARLs was set up to obtain an accurate prediction of the prognosis of patients with CM. There were distinctions in the immune landscape among CM patients with diverse risk scores and subgroups. Six prognosis-related ARLs were highly correlated with the number of immune cells. Patients with CM with different risk scores have various sensitivities to immunotherapy and antitumor drug treatments. CONCLUSION: Our newly risk model associated with ARLs has considerable prognostic value for patients with CM. Not only has the risk model high prediction accuracy but it also indicates the immune status of CM patients, which will provide a new direction for the individualized therapy of patients with CM.

Prognostic Value of Liver Kinase B1 (LKB1) in Gastric Cancer-Associated Tumor Microenvironment Immunity.

Liver kinase B1 (LKB1) is a tumor suppressor gene, the inactivation of which occurs frequently in different tumor types. However, whether LKB1 is associated with the clinical features of gastric cancer (GC) and regulating tumor immunity is unknown. In this study, we showed that LKB1 is highly expressed in the serum of healthy individuals (n = 176) compared to GC patients (n = 416) and is also associated with clinical outcomes and good survival rates in GC patients. Furthermore, genes associated with immune checkpoints and T cell activation, such as PD-1, PD-L1, CD8A, CD8B, CD28, and GZMM, were shown to be highly expressed in GC subgroups with high LKB1 expression. Compared with fresh gastric cancerous tissues, LKB1 was highly expressed in CD3+CD8+ and CD3+CD8+CD28+ T cells in fresh adjacent non-cancerous tissues. CD3+CD8+ T cells produced an IFN-γ anti-cancer immune response. Furthermore, the proportion of CD3+CD8+ T cells that expressed LKB had a positive correlation with IFN-γ expression. Moreover, GC patients with low LKB1 expression had a poor objective response rate, and worse progression-free survival and overall survival when treated with pembrolizumab. In conclusion, LKB1 may be a potential immune checkpoint in GC patients.

Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer.

The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis­platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SIGNIFICANCE: Platinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis­platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.

Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library.

High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.

Prognostic and clinical significance of Solute Carrier Family 7 Member 1 in ovarian cancer.

BACKGROUND: Members of the solute carrier (SLC)7 family are known to play important roles in tumorigenesis and development. However, the prognostic significance of the SLC7 family in ovarian cancer (OC) remains unknown. METHODS: Expression patterns of SLC7 family members in OC were analyzed using gene expression profiling interactive analysis (GEPIA). The Kaplan-Meier plotter was applied to evaluate associations of the SLC7 gene family with prognosis of OC. SLC7A1 expression was additionally analyzed via immunohistochemical staining. χ2, Kaplan-Meier and Cox regression analysis were used to evaluate the relationship between SLC7A1 expression and clinicopathological features, platinum resistance and prognosis in patients with high-grade serous ovarian cancer (HGSOC). RESULTS: The GEPIA dataset revealed the abundant expression of SLC7A1, SLC7A4, and SLC7A7, and conversely, the low expression of SLC7A2 and SLC7A8 in OC relative to normal tissue samples. Kaplan-Meier survival analysis further indicated that high SLC7A1 and low SLC7A2 mRNA levels were significantly associated with overall survival (P<0.05). Positive SLC7A1 expression was detected in 65 (58.1%) HGSOC tissue samples, but not in all normal ovarian tissue samples (100%), indicating that the expression of SLC7A1 in HGSOC tissues was significantly higher than that in normal ovarian tissues (P<0.001). Additionally, expression of SLC7A1 was negatively associated with relapse-free survival (RFS; P<0.05). CONCLUSIONS: SLC7A1 is a potential prognostic biomarker of OC.

E2F1/2/4 mRNA is associated with immune infiltration and are potential biomarkers for the prognosis of human gastric carcinoma.

BACKGROUND: E2Fs are genes that regulate DNA synthesis and the cell cycle by encoding a family of transcription factors. Increasing experimental evidence has revealed that E2Fs play key roles in tumor progression in various types of cancer. METHODS: We investigated the survival, expression and transcriptional data of E2F1/2/4 in gastric cancer (GC) patients using the immunohistochemistry assay, Kaplan-Meier Plotter, cBioPortal, String, and GEPIA databases. The plasma of GC patients was analyzed using the real-time reverse transcription polymerase chain reaction (RT-PCR) assay. The correlation between E2F1/2/4 expression and clinical features was analyzed using the quartile method. As well, the correlation between E2F1/2/4 and GC immune infiltration was also investigated using the TIMER database. Database of Immune Cell Expression (DICE) was also used to analyze correlations between SOX4 and immune responses. RESULTS: RT-PCR and tissue immunohistochemistry confirmed that E2F1/2/4 was highly expressed in serum and GC tissue samples of GC patients, the expression of which was not affected by patient age and gender. Also, the survival analysis revealed that low levels of E2F1/2/4 expression were significantly associated with a longer overall survival (OS) in GC patients. E2F1/2/4 was correlated with patient prognosis and immune cell infiltration, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs in GC. Our findings indicated that E2F1/2/4 could be used as a prognostic biomarker and indicator of immune infiltration in GC. CONCLUSIONS: This study revealed that E2F1/2/4 could be a promising indicator for tumor-associated immune infiltration and prognosis in GC patients.

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors.

BACKGROUND: Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types. METHODS: In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis. RESULTS: In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively. CONCLUSION: The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

Expression and clinical significance of methyl-CpG binding domain protein 2 in high-grade serous ovarian cancer.

Platinum resistance is an important cause of clinical recurrence and mortality of patients with high-grade serous ovarian cancer (HGSOC). Methyl-CpG binding domain protein 2 (MBD2) serves an important role in tumor progression; however, its role in HGSOC remains unclear. The aim of the present study was to investigate the expression of MBD2 in HGSOC and its role in drug resistance and prognosis of HGSOC. MBD2 expression was analyzed by immunohistochemical staining and western blotting. The associations between MBD2 expression and clinical pathological features, platinum resistance and patient prognosis were analyzed using a χ2 test, Kaplan-Meier analysis and Cox regression analysis. Positive MBD2 expression was detected in 73 (63.5%) of the HGSOC tissue samples, whereas it was undetectable in all 16 normal tissue samples (100%) analyzed, indicating a significantly higher expression level in tumor tissues compared with normal tissues (P<0.001). Additionally, MBD2 expression was significantly higher in platinum-resistant cases compared with that in platinum-sensitive cases (P<0.05). In addition, high expression of MBD2 was negatively associated with relapse-free survival (P<0.05). In conclusion, MBD2 was demonstrated to be a potential drug target and a biomarker for poor prognosis in HGSOC.

ZC3H12D is a prognostic biomarker associated with immune cell infiltration in lung adenocarcinoma.

BACKGROUND: ZC3H12 family members have an important role in tumorigenesis and development. However, the relationship between ZC3H12 family members and the prognosis of lung adenocarcinoma (LUAD) and tumor infiltrating lymphocytes is not clear. METHODS: The expression of ZC3H12 family members in LUAD was analyzed by UALCAN. UALCAN, Kaplan-Meier Plotter, and GEPIA were used to evaluate the effect of ZC3H12 family members on the prognosis of LUAD. The relationship between prognostic ZC3H12 family members and 14 functional states of LUAD was studied by CancerSEA. The correlation between ZC3H12 and immune cell infiltration was studied by TIMMER. In addition, the correlation between ZC3H12D expression and an immune infiltration gene marker set was analyzed by TISIDB and GEPIA. Finally, the expression of ZC3H12D in LUAD was further verified by the GEO database and immunohistochemical staining. RESULTS: The combined prognostic analysis of UALCAN, Kaplan-Meier Plotter, and GEPIA showed that the up-regulated expression of ZC3H12D mRNA was closely related to an improvement in overall survival rate (OS) in patients with LUAD. There was no significant correlation between ZC3H12D and 14 functional states of LUAD. Further analysis showed that the expression of ZC3H12D was positively correlated with the infiltration of B cells and CD4+T cells in LUAD. The expression of ZC3H12D was also positively correlated with immune markers in LUAD, including B cell-derived TNF and LTA cytokines, CXCL13, and its receptor CXCR5. Immunohistochemical staining showed that the expression of ZC3H12D in LUAD tissue samples was higher than normal lung tissues. CONCLUSIONS: These findings suggest that multiple ZC3H12 family members are associated with the prognosis of patients with LUAD tumors. The increased expression of ZC3H12D was correlated with improved prognosis. ZC3H12D was shown to be associated with the level of immune cell infiltration, including B cells and CD4+T cells. Thus, ZC3H12D can be used as a biomarker to judge the prognosis and immune infiltration of LUAD.

2019-nCoV may create complications in colon cancer patients with ACE2 expression.

Since December 2019, a novel coronavirus (2019-nCoV) has emerged from Wuhan, China, causing symptoms in humans. Much remains unknown about 2019-nCoV, especially the additional risks that 2019-nCoV infection may pose for colon cancer patients. Many reports show that angiotensin converting enzyme II (ACE2) is the cell receptor through which 2019-nCoV enters host cells, and this is similar to the cell entry mechanism of SARS coronavirus. Previous studies show that ACE2 is highly expressed in the gastrointestinal tract, especially in the colon. In patients with colon cancer, ACE2 expression is significantly increased in tumor tissues compared to tissues from patients with other types of cancer. One of the known regulators of endocytosis is the serine protease (TMPRSS2) and AP2-associated protein kinase 1 (AAK1), which also facilitates the passage of viruses into cells. Furthermore, the Database of Gene expression profiling interactive analysis suggests that expression levels for ACE2, TMPRSS2, and AAK1 are positively correlated in colon cells. Therefore, our findings predict that 2019-nCoV will create increased complications for patients with colon cancer.

Cyclin-dependent kinase 6 (CDK6) is a candidate diagnostic biomarker for early non-small cell lung cancer.

BACKGROUND: Cyclin-dependent kinase 6 (CDK6) is an important regulatory protein of the cell cycle and plays an important role in tumor progression. The aim of this study was to investigate the expression of CDK6 in T1 stage non-small cell lung cancer (NSCLC) and to explore the association of CDK6 with the clinicopathological features of the disease. METHODS: CDK6 expression was analyzed by real-time PCR (RT-PCR) and immunohistochemistry (IHC) in tumor tissue samples and the distal normal tissue samples from 56 T1 stage NSCLC patients. The correlation between CDK6 expression and clinicopathological features was analyzed using the independent samples t-test and nonparametric tests. RESULTS: We found CDK6 had a tendency to increase in tumor tissues compared to normal tissues at the transcriptional level (P=0.073). Moreover, the expression of CDK6 protein in NSCLC tissues was also significantly higher than in normal lung tissues (P=0.003). With an increase of smoking quantity, the expression of CDK6 mRNA was increased (P=0.009). Remarkably, CDK6 expression was increased in squamous cell carcinoma (SCC) tissues but decreased in adenocarcinoma (AD) tissues at both the transcription and protein levels (P<0.001). After stratification based on pathological type, CDK6 gene expression was not associated with any clinicopathological features in SCC, while it was negatively associated with tumor diameter in AD (P=0.049). CONCLUSIONS: Taken together, these results indicated that abnormal expression of the CDK6 gene in NSCLC might be associated with pathological type, which may serve as a diagnostic biomarker for NSCLC.