Cancer Biomarkers最新文献

BackgroundDespite Non-small cell lung cancer (NSCLC) ranks among the most deadly cancers worldwide, and currently, apart from a low percentage, targetable molecules have not been identified in its etiopathogenesis. The relationship between the proteoglycans decorin and biglycan, which are present in the extracellular matrix of cells, and transforming growth factor Beta-1 (TGF-B1), has been shown in many cancers. We investigated the significance of these molecules in NSCLC.MethodsFasting serum levels of decorin, biglycan, and TGF-B1 were obtained from 48 newly diagnosed NSCLC patients and compared with those of 48 adult control subjects matched for age and demographics. Demographic data, baseline laboratory values, and ELISA results were compared between the groups.ResultsThe median age was 65(39-83) similar in both groups. There was no relation between demographic and clinical parameters and the levels of decorin, biglycan, and TGF-B1 in the NSCLC group. However, in comparison to the control group, NSCLC patients had significantly higher levels of biglycan (42.55 $\pm$ 27.40 vs. 24.38 $\pm$ 12.05 ng/mL, $p=$ 0.026) and TGF-B1 (15.55 $\pm$ 9.16 vs. 10.07 $\pm$ 7.8 pg/mL, $p=$ 0.001), while decorin levels were significantly lower (6.64 $\pm$ 1.92 vs. 10.28 $\pm$ 3.13 ng/mL, $p=$ 0.002). In the multivariate regression analysis; Decorin $<$ 8.13 ng/mL (OR, 10.96; 95% CI: 3.440-34.958), current smoking (OR, 3.81; 95% CI: 1.320-10.998), COPD (OR, 43.6; 95% CI: 2.082-913.081), and lower BMI (OR, 1.22; 95% CI: 1.070-1.405, $p=$ 0.003) were identified as independent predictive markers for NSCLC diagnosis.ConclusionThe decreased serum decorin level is an independent marker for NSCLC. Further studies are needed to investigate the prognostic significance of decorin on survival and its potential as a target in treatment.

BackgroundTo determine the serum levels of endoplasmic reticulum (ER) chaperones, glucose-regulated protein 78 (GRP78), glucose-regulated protein 94 (GRP94), calnexin, and calreticulin in patients with lung cancer and in the control group and to evaluate the relationship between chaperone levels and clinical data and patient survival.MethodsGRP78, GRP94, calnexin and calreticulin were measured in serum by ELISA. The serum chaperone levels of patients with lung cancer and the control group were compared. The relationship between serum chaperone levels and clinical data and patient prognosis was evaluated. The median survival time was calculated using the Kaplan-Meier method. Cox regression analysis was performed to determine the hazard ratio of ER chaperones considering prognostic factors.ResultsThe serum levels of all ER chaperones GRP78, GRP94, calnexin, and calreticulin were higher in patients with lung cancer than in the control group and correlated with each other. Serum calreticulin levels were not affected by demographic and clinical characteristics. Serum levels of GRP78, GRP94, and calnexin were not associated with survival. However, median survival ± SE (95%CI) was 16.00 ± 1.72 (12.62-19.38) months in patients with serum calreticulin levels of 250.52 ng/ml and above, while it was 8.00 ± 1.38 (5.29-10.71) months in patients with calreticulin levels below the cut-off value (log-rank = 6.919; p = 0.009). Calreticulin impacted survival, even after adjustment for sex, histologic subtype, stage, treatment, and response to chemotherapy, which impacted survival [HR (95%CI): 0.656 (0.433-0.995); p = 0.047].ConclusionCalreticulin is promising for delineating risk groups in lung cancer screening studies, guiding treatment and monitoring outcomes.

BackgroundHeat shock factor 1 (HSF1), the principal transcriptional regulator of cellular stress responses, has been exhibited to play a role in the progression of various human cancer types. However, the function of HSF1 in endometrial cancer (EC) has not yet been evaluated.ObjectiveThis study examined the expression and role of HSF1 in EC.MethodsImmunohistochemistry was performed to explore HSF1 level in 135 endometrial tissue specimens. The relationship between HSF1 level and EC patients' clinicopathological characteristics was analyzed. Quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) and Western blotting were employed to explore HSF1 expression level in tissues in vitro. Small interfering RNA (siRNA) was employed to suppress HSF1 expression level. The invasion and migration capacities were evaluated using transwell and wound healing assays. Cell cycle arrest and apoptosis were assessed by flow cytometric analysis.ResultsEC tissues exhibited higher HSF1 expression level compared with normal endometrial and atypical endometrial hyperplasia tissues. High HSF1 expression level was associated with histological grade, muscular invasion, lymph node metastasis, and estrogen receptor (ER) expression level in EC tissues and cells. Kaplan-Meier analysis indicated that EC patients with elevated HSF1 expression level had poorer overall survival. Knockdown of HSF1 in EC cells resulted in cell cycle arrest, increased apoptosis, and inhibited EC cell proliferation, invasion, and migration.ConclusionThe results demonstrated that HSF1 could function as an oncogene in EC. HSF1 could play a notable role in EC progression. HSF1 may be a potential molecular target for both the treatment and prognosis of patients with EC.

Non-small cell lung cancer (NSCLC) remains the most common cause for cancer-related mortality despite advances in treatment. Early detection is crucial for improving patient outcomes, yet current diagnostic and prognostic molecular biomarkers lack the sensitivity and specificity necessary to become clinically useful. Recent studies revealed that the lower airway microbiome play a role in NSCLC and that microbial signatures are associated with NSCLC development, progression, and prognosis, suggesting the potential for microbiome-based biomarkers for early diagnosis and risk stratification. Here we review recent advances in the role of the local and systemic microbiome in early-stage NSCLC. Primarily, several studies have identified specific microbial taxa associated with lung cancer suggesting novel insights into disease pathogenesis and progression. Integration of microbiome data with other 'omics' platforms, such as host transcriptomics and metabolomics, has the potential to enhance our understanding of microbial-host interactions and may provide more comprehensive biomarker signatures. While promising, challenges remain to the development of microbiome-based biomarkers such as those related to differences in samples utilized, sequencing methods, and data analysis. Here, we discuss such challenges as well as future directions for research needed to fulfil the promise of microbiome-based biomarkers for changing early detection and management strategies in NSCLC.

ObjectiveIn NSCLC, the main approach to differentiate between intrapulmonary metastases (IPM) and multiple primary lung cancer (MPLC) is to integrate histopathological and genomic information. Here, we identified viable biomarkers that can distinguish IPM from MPLC by integrating comprehensive genomic profiling (CGP) and targeted RNA sequencing.MethodsWe retrospectively collected tissues from at least two lesions in 34 patients. 29 and 5 out of 34 patients determined as pathologic MPLC (pMPLC) and pathologic IPM, respectively, according to Martini-Melamed criteria (M-M criteria). A comprehensive investigation at genomic and transcriptomic level was conducted.ResultsNine of the 29 pMPLCs shared trunk mutations in their lesions and were consequently reclassified as IPM. Survival analyses revealed that classification integrated M-M criteria and mutational profiling could distinguish IPM/MPLC more accurately. Further exploration at the transcriptomic level revealed elevated expression levels of genes related to epithelial-mesenchymal transition and immunomodulatory pathways in IPM. Notably, the expression of CXCL1 and CXCL8 was significantly upregulated in IPM.ConclusionsWe found that the expression of CXCL1 and CXCL8 in any tumor lesion within a patient could reliably indicate IPM. Additionally, assessing the transcriptional levels of CXCL1 and CXCL8 also provide a dependable and practical approach to identify IPM from MPLC.

ObjectiveThis study aimed to develop and validate a radiomics nomogram based on 40 KeV images and iodine density maps derived from dual-layer spectral detector CT (DLSDCT) for predicting cervical lymph node (LN) metastasis in patients with papillary thyroid carcinoma (PTC).MethodsA total of 214 LNs from 143 patients with histopathologically confirmed PTC in our hospital were included in the study. The LNs were randomly divided into a training group (n = 150) and a validation group (n = 64) in a 7:3 ratio. Radiomics features were extracted from non-enhanced, arterial phase, and venous phase 40 KeV images, as well as arterial phase and venous phase iodine density maps. Recursive feature elimination (RFE) and logistic regression (LR) were used for feature selection and radiomics score construction. A multivariate logistic regression model was established, incorporating the radiomics score and CT image features. The receiver operating characteristic (ROC) curve was used to evaluate the model's performance. The Hosmer-Lemeshow test and calibration curve were used to assess the model's goodness of fit, while decision curve analysis (DCA) evaluated its clinical applicability.ResultsThe radiomics features consisted of 11 LN-related features that exhibited a good predictive effect. The radiomics nomogram, which included radiomics features, lymphatic hilum status, and significant enhancement in the arterial phase, demonstrated excellent calibration and discrimination in both the training set (AUC = 0.955; 95% confidence interval [CI]: 0.924-0.985) and the validation set (AUC = 0.928; 95% CI: 0.861-0.994). The decision curve analysis confirmed the clinical validity of our nomogram. The DeLong test comparing the radiomics-clinical nomogram with the clinical model yielded a p-value of <0.001.ConclusionsThe radiomics nomogram, incorporating radiomics features and CT image features, serves as a non-invasive preoperative prediction tool with high accuracy in predicting cervical lymph node metastasis in patients with PTC.

BackgroundGiven the differences between malignancies arising from different segments of the colon, specific theranostic biomarkers can be linked to either Right-sided (RCC) or Left-sided colon cancer (LCC).ObjectiveAnalysis of 65 serum proteins to identify panels of theranostic biomarkers for LCC and RCC.MethodsSerum levels of 65 immunomodulators were measured in CC, LCC, and RCC patients, as well as healthy controls with the ProcartaPlex Human Immune Monitoring 65-Plex Panel.ResultsIL-27 may be used for early detection in LCC. CD-30 was up-regulated in metastatic CC, BLC was up-regulated in metastatic LCC and CD-40L was down-regulated in metastatic RCC. MDC and MMP-1 were positively associated, while IL-9 and VEGF-A were negatively associated with lymph nodes invasion in CC. Up-regulation of IL-12p70 and MMP-1 in LCC with lymph nodes invasion contrasted with down-regulation of IL-9 and MIP-1beta. IL-23, I-TAC, and SDF-1α were negatively associated with resistant CC to Folfox chemotherapy, and I-TAC was down-regulated in resistant LCC. IL-2 and FGF-2 were down-regulated, while APRIL was up-regulated in resistant RCC.ConclusionsOur study revealed significant differences in serum protein levels between LCC and RCC emphasizing the importance to explore novel theranostic biomarkers for CC, associated with resistance or sensitivity to chemotherapy.

BackgroundNasopharyngeal carcinoma (NPC) is a neoplasm that arises from the mucosal lining of the nasopharynx. Recent investigations have underscored that reprogramming of lipid metabolism is a salient metabolic alteration in neoplastic cells. Consequently, identifying lipid metabolism-associated biomarkers in NPC is of paramount importance.MethodsUtilizing transcriptomic datasets, differentially expressed genes (DEGs) were identified from GSE12452, contrasting NPC specimens with normal controls. The Weighted Gene Co-expression Network Analysis (WGCNA) was employed to discern key module genes pertinent to NPC. Lipid metabolism-related differentially expressed genes (LMR-DEGs) were ascertained by intersecting DEGs, key module genes linked to NPC, and lipid metabolism-related genes (LMRGs) using a Venn diagram approach. Subsequently, the MCODE algorithm was applied within the protein-protein interaction (PPI) framework to pinpoint lipid metabolism-centric biomarkers for NPC. The diagnostic potential of these biomarkers was assessed through ROC analysis. In the concluding phase, a 'TF-mRNA-miRNA' interaction network was delineated using Cytoscape.ResultsIn our analysis, a total of 5026 DEGs were discerned when contrasting NPC specimens with normal controls. From this pool, 1835 genes were pinpointed as key module genes pertinent to NPC. Through a Venn diagram approach, 64 LMR-DEGs were isolated. Further analysis led to the identification of six lipid metabolism-centric biomarkers for NPC, namely GALC, SPTLC2, SMPD2, DEGS2, DEGS1, and SMPD3. Notably, these biomarkers demonstrated robust diagnostic efficacy. We found that DEGS1 was negatively correlated with SMPD2 and DEGS2. A comparative expression analysis revealed diminished expression levels of GALC, SPTLC2, SMPD2, DEGS2, and SMPD3 in the NPC cohort relative to the control group. In the terminal phase of our study, the 'TF-mRNA-miRNA' regulatory network was delineated, comprising 309 nodes and 360 interaction pairs.ConclusionIn summary, our investigation identified six lipid metabolism-associated biomarkers (GALC, SPTLC2, SMPD2, DEGS2, DEGS1, and SMPD3) linked to NPC, providing a foundational framework for potential therapeutic interventions for NPC.

BackgroundPathologically, clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma, with high heterogeneity and poor prognosis. There is increasing evidence that alternative splicing (AS) is involved in tumor evolution and tumor immune microenvironment (TIME). However, studies on the exploration of AS events and TIME in ccRCC are still few but needed.MethodsThe transcriptional data and clinicopathological information of patients with ccRCC in The Cancer Genome Atlas (TCGA) database were extracted completely. Patients were grouped according to the ESTIMATE algorithm and differentially expressed AS events (DEASs) were identified. The relationship between AS events and features of TIME were investigated by functional enrichment analysis and unsupervised consensus analysis. Finally, hub splicing factors (SFs) was identified by the regulatory network of survival-related AS events and intersection SFs, and its biological function was further verified in vitro.ResultsIn total, the data of 515 patients with ccRCC were extracted and analyzed. Patients with low immune-score presented longer overall survival (OS) than high immune-score. 861 AS events were identified as DEASs, and they were enriched in immune-related pathways. 3 AS-based clusters were identified and found to have different prognoses and unique immune features. Finally, MBNL1 was identified as a hub SF, and it was shown to inhibit proliferation and metastasis, promote apoptosis, and block cells in G2/M phase in 786O and A498 cells. Mechanistically, MBNL1 regulates QKI expression through AS.ConclusionThe prognostic risk model constructed base on immune-related AS events has good predictive ability for ccRCC. The hub SF MBNL1 identied in the present study could inhibit the progression of ccRCC. This effect is likely due to the regulation of QKI expression through AS.

BackgroundWe previously utilized pretreatment tumor markers Carcinoembryonic Antigen (CEA) and Cancer Antigen 125 (CA125) for predicting lymph node metastasis (LNM) in endometrioid endometrial cancer (EC).ObjectiveThe aim of this study was to externally validate a nomogram developed in our previous single-center retrospective study.MethodsA multi-center validation study was conducted to recruit endometrioid EC patients from four branches of Chang Gung Memorial Hospital between 2009 and 2021, with patients participating in the original research being excluded. The previously established nomogram was applied with optimal cut-off values for CEA 1.4 ng/ml and CA125 40 U/mL identified through receiver operating characteristic (ROC) curves. The concordance index (C-index) was calculated to assess discrimination, and comparative negative predictive value (NPV) and negative likelihood ratio (NLR) were determined. Decision curve analysis (DCA) was plotted to evaluate our predictive model's clinical utility and net benefit.ResultsOverall, 1271 patients were included in this external validation study. The results demonstrated a C-index of 0.727, indicating moderate discrimination ability of the nomogram in predicting LNM in this independent cohort. Comparative analysis of NPV 97.2% and NLR 0.36 revealed performance metrics consistent with the original study, reinforcing the nomogram's potential clinical utility in ruling out the possibility of LNM if both pretreatment CEA and CA125 were less than 1.4 ng/ml and 40 U/mL, respectively. The DCA indicated that the nomogram provided clinical utility.ConclusionThe reproducible performance metrics in the independent large sample cohort underscore the robustness and generalizability of utilizing CEA and CA125 as predictors of LNM in endometrioid EC, suggesting its potential as a simple tool for clinicians in preoperative decision-making regarding lymphadenectomy.