Background
The prognostic significance of insulin‐like growth factor binding protein 2 (IGFBP2) expression has been explored in plenty of studies in human cancers. Because of the controversial ...results, the meta‐analysis was carried out to evaluate the relevance of IGFBP2 expression with the prognosis in various tumors.
Methods
The data searched from four databases (Pubmed, Embase, Cochrane library, and Web of science) was used to calculate pooled hazard ratios (HRs) in this meta‐analysis. Subgroup analyses were stratified by ethnicity, cancer type, publication year, Newcastle–Ottawa Scale score, treatments, and populations.
Results
Twenty‐one studies containing 5560 patients finally met inclusion criteria. IGFBP2 expression was associated with lower overall survival (HR = 1.57, 95% CI = 1.31–1.88) and progression‐free survival (HR = 1.18, 95% CI = 1.04–1.34) in cancer patients, but not with disease‐free survival (HR = 1.50, 95% CI = 0.91–2.46) or recurrence‐free survival (HR = 1.50, 95% CI = 0.93–2.40). The subgroup analyses indicated IGFBP2 overexpression was significantly correlated with overall survival in Asian patients (HR = 1.42, 95% CI = 1.18–1.72), Caucasian patients (HR = 2.20, 95% CI = 1.31–3.70), glioma (HR = 1.36, 95% CI = 1.03–1.79), and colorectal cancer (HR = 2.52, 95% CI = 1.43–4.44) and surgery subgroups (HR = 1.97, 95% CI = 1.50–2.58).
Conclusion
The meta‐analysis showed that IGFBP2 expression was associated with worse prognosis in several tumors, and may serve as a potential prognostic biomarker in cancer patients.
The prognostic significance of Insulin‐like growth factor binding protein 2 (IGFBP2) expression has been explored in plenty of studies in human cancers. However, the results remain controversial. Here, we conducted a systematic review and meta‐analysis to assess the relevance of IGFBP2 expression with the prognosis in various tumors.
In pattern recognition and data mining, clustering is a classical technique to group matters of interest and has been widely employed to numerous applications. Among various clustering algorithms, ...K-means (KM) clustering is most popular for its simplicity and efficiency. However, with the rapid development of the social network, high-dimensional data are frequently generated, which poses a considerable challenge to the traditional KM clustering as the curse of dimensionality. In such scenarios, it is difficult to directly cluster such high-dimensional data that always contain redundant features and noises. Although the existing approaches try to solve this problem using joint subspace learning and KM clustering, there are still the following limitations: 1) the discriminative information in low-dimensional subspace is not well captured; 2) the intrinsic geometric information is seldom considered; and 3) the optimizing procedure of a discrete cluster indicator matrix is vulnerable to noises. In this paper, we propose a novel clustering model to cope with the above-mentioned challenges. Within the proposed model, discriminative information is adaptively explored by unifying local adaptive subspace learning and KM clustering. We extend the proposed model using a robust <inline-formula> <tex-math notation="LaTeX">l_{2,1} </tex-math></inline-formula>-norm loss function, where the robust cluster centroid can be calculated in a weighted iterative procedure. We also explore and discuss the relationships between the proposed algorithm and several related studies. Extensive experiments on kinds of benchmark data sets demonstrate the advantage of the proposed model compared with the state-of-the-art clustering approaches.
Glyceraldehyde-3-phosphate dehydrogenase （GAPDH）, initially identified as a glycolytic enzyme and considered as a housekeeping gene, is widely used as an internal control in experiments on proteins, ...mRNA, and DNA. However, emerging evidence indicates that GAPDH is implicated in diverse functions independent of its role in energy metabolism; the expression status of GAPDH is also deregulated in various cancer cells. One of the most common effects of GAPDH is its inconsistent role in the determination of cancer cell fate. Furthermore, studies have described GAPDH as a regulator of cell death; other studies have suggested that GAPDH participates in tumor progression and serves as a new therapeutic target. However, related regulatory mechanisms of its numerous cellular functions and deregulated expression levels remain unclear. GAPDH is tightly regulated at transcriptional and pnsttranscriptional levels, which are involved in the regulation of diverse GAPDH functions. Several cancer-related factors, such as insulin, hypoxia inducible factor-1 （HIF-1）, p53, nitric oxide （NO）, and acetylated histone, not only modulate GAPDH gene expression but also affect protein functions via common pathways. Moreover, posttranslational modifications （PTMs） occurring in GAPDH in cancer cells result in new activities unrelated to the original glycnlytic function of GAPDH. In this review, recent findings related to GAPDH transcriptional regulation and PTMs are summarized. Mechanisms and pathways involved in GAPDH regulation and its different roles in cancer cells are also described.
Previous studies have shown that ALDH2 and ADH1B genes may be associated with alcohol metabolism and the risk of esophageal squamous cell carcinoma (ESCC), with inconsistent results. This ...meta-analysis aimed at comprehensively assessing the associations between ALDH2 and ADH1B polymorphisms and the risk of ESCC to synthesize and clarify the evidence.
We calculated summary estimates of the associations between four genetic variants (rs671 and rs674 in ALDH2, and rs1229984 and rs1042026 in ADH1B) and the ESCC risk across 23 publications in the additive model and allelic model. Venice criteria, Bayesian false discovery probability (BFDP), and false-positive reporting probability (FPRP) were used to assess the strength of epidemiological evidence. Heterogeneity among studies was evaluated by using the Higgin's I
statistic, and publication bias was assessed by using funnel plots and Begg's test. A Mendelian randomization (MR) analysis was performed to determine the causal association between alcohol intake and esophageal cancer risk. Data from the HaploReg v4.1 and PolyPhen-2 were analyzed for functional annotations.
Of the four genetic variants, rs671 of ALDH2 was associated with a significantly reduced risk of ESCC (OR: 0.60, 95% CI: 0.50-0.73), whereas rs1229984 of ADH1B was associated with a significantly increased risk (2.50, 95% CI: 1.70-3.69) in the additive model. In the allelic model, the variant rs1229984 of ADH1B also increased the risk of ESCC (OR: 1.50; 95% CI: 1.21-1.87). The result for the variant rs671 was considered as strong epidemiological evidence. Functional annotations identified that the four variants were related to the enhancer histone marks and motif changes. The other two variants were not associated with the ESCC risk (rs674 of ALDH2 OR: 1.22, 95% CI: 0.71-2.12; rs1042026 of ADH1B OR: 1.28, 95% CI: 0.52-3.14) in the additive model. The MR analysis did not find a causal effect of alcohol on the esophageal cancer risk.
The results showed that ADH1B rs1229984 was significantly associated with an increased the risk of ESCC.
In many real-world applications, data are represented by high-dimensional features. Despite the simplicity, existing K-means subspace clustering algorithms often employ eigenvalue decomposition to ...generate an approximate solution, which makes the model less efficiency. Besides, their loss functions are either sensitive to outliers or small loss errors. In this paper, we propose a fast adaptive K-means (FAKM) type subspace clustering model, where an adaptive loss function is designed to provide a flexible cluster indicator calculation mechanism, thereby suitable for datasets under different distributions. To find the optimal feature subset, FAKM performs clustering and feature selection simultaneously without the eigenvalue decomposition, therefore efficient for real-world applications. We exploit an efficient alternative optimization algorithm to solve the proposed model, together with theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on several benchmark datasets demonstrate the advantages of FAKM compared to state-of-the-art clustering algorithms.
Insulin-like growth factor binding protein-3 (IGFBP3) has been reported to be related to the risk of some cancers. Here we focussed on serum IGFBP3 as a possible biomarker of diagnosis and prognosis ...for oesophageal squamous carcinoma (ESCC).
Enzyme-linked immunosorbent assay (ELISA) was used to measure the serum IGFBP3 level in the training cohort including 136 ESCC patients and 119 normal controls and the validation cohort with 55 ESCC patients and 42 normal controls. The receiver operating characteristics curve (ROC) was used to assess the diagnosis value. Cox proportional hazards model was applied to select factors for survival nomogram construction.
Serum IGFBP3 levels were significantly lower in early-stage ESCC or ESCC patients than those in normal controls (p < .05). The specificity and sensitivity of serum IGFBP3 for the diagnosis of ESCC were 95.80% and 50.00%, respectively, with the area under the ROC curve (AUC) of 0.788 in the training cohort. Similar results were observed in the validation cohort (88.10%, 38.18%, and 0.710). Importantly, serum IGFBP3 could also differentiate early-stage ESCC from controls (95.80%, 52.54%, 0.777 and 88.10%, 36.36%, 0.695 in training and validation cohorts, respectively). Furthermore, Cox multivariate analysis revealed that serum IGFBP3 was an independent prognostic risk factor (HR = 2.599, p = .002). Lower serum IGFBP3 level was correlated with reduced overall survival (p < .05). Nomogram based on serum IGFBP3, TNM stage, and tumour size improved the prognostic prediction of ESCC with a concordance index of 0.715.
We demonstrated that serum IGFBP3 was a potential biomarker of diagnosis and prognosis for ESCC. Meanwhile, the nomogram might help predict the prognosis of ESCC.
Key Message
Serum IGFBP3 showed early diagnostic value in oesophageal squamous cell carcinoma with independent cohort validation. Moreover, serum IGFBP3 was identified as an independent prognostic risk factor, which was used to construct a nomogram with improved prognosis ability in oesophageal squamous cell carcinoma.
Oral tongue squamous cell carcinoma (OTSCC) is one of the most aggressive oral tumors. The aim of this study was to establish a nomogram to predict overall survival (OS) of TSCC patients after ...surgery. 169 TSCC patients who underwent surgical treatments in the Cancer Hospital of Shantou University Medical College were included. A nomogram based on Cox regression analysis results was established and internally validated using bootstrap resampling method. pTNM stage, age and total protein, immunoglobulin G, factor B and red blood cell count were identified as independent prognostic factors to create the nomogram. The Akaike Information Criterion and Bayesian Information Criterion of the nomogram were lower than those of pTNM stage, indicating a better goodness-of-fit of the nomogram for predicting OS. The bootstrap-corrected concordance index of nomogram was higher than that of pTNM stage (0.794 vs. 0.665, p = 0.0008). The nomogram also had a good calibration and improved overall net benefit. Based on the cutoff value obtained from the nomogram, the proposed high-risk group had poorer OS than low-risk group (p < 0.0001). The nomogram based on nutritional and immune-related indicators represents a promising tool for outcome prediction of surgical OTSCC.
Background
We previously found that autoantibodies against a panel of six tumor-associated antigens (p53, NY-ESO-1, MMP-7, Hsp70, PRDX6 and Bmi-1) may aid in early detection of esophageal squamous ...cell carcinoma. Here we aimed to evaluate the diagnostic value of this autoantibody panel in esophagogastric junction adenocarcinoma (EJA) patients.
Methods
Serum autoantibody levels were measured by enzyme-linked immunosorbent assay in a training cohort and a validation cohort. We used receiver-operating characteristics (ROC) to calculate diagnostic accuracy.
Results
We recruited 169 normal controls and 122 EJA patients to the training cohort, and 80 normal controls and 70 EJA patients to the validation cohort. Detection of the autoantibody panel demonstrated an area under the curve (AUC) of 0.818, sensitivity 59.0% and specificity 90.5% in training cohort, and AUC 0.815, sensitivity 61.4% and specificity 90.0% in validation cohort in the diagnosis of EJA. Measurement of the autoantibody panel could distinguish early stage EJA patients from normal controls (AUC 0.786 and 0.786, sensitivity 50.0% and 56.0%, and specificity 90.5% and 90.0%, for training and validation cohorts, respectively). Moreover, a restricted panel consisting of autoantibodies against p53, NY-ESO-1 and Bmi-1 exhibited similar diagnostic performance for EJA (AUC 0.814 and 0.823, sensitivity 53.5% and 60.0%, and specificity 90.5% and 93.7%, for training and validation cohorts, respectively) and early stage EJA (AUC 0.744 and 0.773, sensitivity 55.6% and 52.0%, and specificity 90.5% and 93.7%, for training and validation cohorts, respectively).
Conclusions
Autoantibodies against an optimized TAA panel as serum biomarkers appear to help identify the present of early stage EJA.
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