Transcobalamin I: a novel prognostic biomarker of neoadjuvant chemotherapy in locally advanced hypopharyngeal squamous cell cancers

A machine learning model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using multiple parameters

OBJECTIVE

This study aimed to establish a model for predicting the three-year survival status of patients with hypopharyngeal squamous cell carcinoma using artificial intelligence algorithms.

METHOD

Data from 295 patients with hypopharyngeal squamous cell carcinoma were analysed retrospectively. Training sets comprised 70 per cent of the data and test sets the remaining 30 per cent. A total of 22 clinical parameters were included as training features. In total, 12 different types of machine learning algorithms were used for model construction. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and Cohen's kappa co-efficient were used to evaluate model performance.

RESULTS

The XGBoost algorithm achieved the best model performance. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve and kappa value of the model were 80.9 per cent, 92.6 per cent, 62.9 per cent, 77.7 per cent and 58.1 per cent, respectively.

CONCLUSION

This study successfully identified a machine learning model for predicting three-year survival status for patients with hypopharyngeal squamous cell carcinoma that can offer a new prognostic evaluation method for the clinical treatment of these patients.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Predictive Biomarkers for Response and Toxicity of Induction Chemotherapy in Head and Neck Cancers

This review focuses on the molecular biology of head and neck squamous cell carcinomas and presents current and emerging biomarkers of the response of patients to induction chemotherapy. The usefulness of genes, proteins, and parameters from diagnostic clinical imaging as well as other clinicopathological parameters is thoroughly discussed. The role of induction chemotherapy before radiotherapy or before chemo-radiotherapy is still debated, as the data on its efficacy are somehow confusing. Despite the constant improvement of treatment protocols and the introduction of new cytostatics, there is still no consensus regarding the use of induction chemotherapy in the treatment of head and neck cancer, with the possible exception of larynx preservation. Such difficulties indicate that potential future treatment strategies should be personalized. Personalized medicine, in which individual tumor genetics drive the selection of targeted therapies and treatment plans for each patient, has recently emerged as the next generation of cancer therapy. Early prediction of treatment outcome or its toxicity may be highly beneficial for those who are at risk of the development of severe toxicities or treatment failure—a different treatment strategy may be applied to these patients, sparing them unnecessary pain. The literature search was carried out in the PubMed and ScienceDirect databases as well as in the selected conference proceedings repositories. Of the 265 articles and abstracts found, only 30 met the following inclusion criteria: human studies, analyzing prediction of induction chemotherapy outcome or toxicity based on the pretreatment (or after the first cycle, if more cycles of induction were administered) data, published after the year 2015. The studies regarding metastatic and recurrent cancers as well as the prognosis of overall survival or the outcome of consecutive treatment were not taken into consideration. As revealed from the systematic inspection of the papers, there are over 100 independent parameters analyzed for their suitability as prognostic markers in HNSCC patients undergoing induction chemotherapy. Some of them are promising, but usually they lack important features such as high specificity and sensitivity, low cost, high positive predictive value, clinical relevance, short turnaround time, etc. Subsequent studies are necessary to confirm the usability of the biomarkers for personal medicine.

Diagnostic and Therapeutic Perspectives Associated to Cobalamin-Dependent Metabolism and Transcobalamins’ Synthesis in Solid Cancers

Cobalamin or vitamin B12 (B12) is a cofactor for methionine synthase and methylmalonyl-CoA mutase, two enzymes implicated in key pathways for cell proliferation: methylation, purine synthesis, succinylation and ATP production. Ensuring these functions in cancer cells therefore requires important cobalamin needs and its uptake through the transcobalamin II receptor (TCII-R). Thus, both the TCII-R and the cobalamin-dependent metabolic pathways constitute promising therapeutic targets to inhibit cancer development. However, the link between cobalamin and solid cancers is not limited to cellular metabolism, as it also involves the circulating transcobalamins I and II (TCI or haptocorrin and TCII) carrier proteins, encoded by TCN1 and TCN2, respectively. In this respect, elevations of B12, TCI and TCII concentrations in plasma are associated with cancer onset and relapse, and with the presence of metastases and worse prognosis. In addition, TCN1 and TCN2 overexpressions are associated with chemoresistance and a proliferative phenotype, respectively. Here we review the involvement of cobalamin and transcobalamins in cancer diagnosis and prognosis, and as potential therapeutic targets. We further detail the relationship between cobalamin-dependent metabolic pathways in cancer cells and the transcobalamins’ abundancies in plasma and tumors, to ultimately hypothesize screening and therapeutic strategies linking these aspects.

TCN1 is a potential prognostic biomarker and correlates with immune infiltrates in lung adenocarcinoma

Background

Around the world, lung cancer is the leading cause of cancer-related death. Lung adenocarcinomas are among the most common diagnosed forms of lung cancer, whose overall survival has not improved significantly, which makes finding an effective therapeutic target vital. Transcobalamin (TCN1) is a vitamin B12-binding protein which regulates cobalamin homeostasis. In tumor tissues, TCN1 is expressed highly, and its expression is correlated with cancer aggressiveness and poor prognosis according to recent studies and bioinformatic analyses. However, its effect on lung adenocarcinoma (LUAD) is unknown.

Methods

We evaluated whether TCN1 shows diagnostic and prognostic value in LUAD using bioinformatic analysis. In particular, various databases and analysis tools were used to determine TCN1’s relationship with LUAD, including TCGA, GTEx, GEO, STRING, and TISIDB.

Results

As compared to normal lung tissue, the level of TCN1 expression in LUAD tissues was significantly higher (P < 0.001). TCN1 also had a good ability to distinguish lung adenocarcinoma from non-lung adenocarcinoma samples [area under the curve (AUC) = 0.788]. According to univariate Cox statistics, high expression levels of TCN1 correlate with poor overall survival (OS) in LUAD (P < 0.001). Moreover, based on a multivariate Cox analysis, TCN1 expression was independently correlated with OS (P = 0.011). GO/KEGG and GSEA indicated enrichment in epidermal cell differentiation (P < 0.0005), keratinocyte differentiation (P < 0.0005), neuroactive ligand–receptor interaction (P < 0.0005), epithelial–mesenchymal transition (P = 0.029, FDR = 0.023) and TNFA signaling via NFKB (P = 0.029, FDR = 0.023). Furthermore, TCN1 is associated with immune infiltration based on an analysis of immune cell infiltration.

Conclusions

In summary, TCN1 could be used as a prognostic and diagnostic biomarker and provide deeper perspectives for the development of therapies and prognostic markers in LUAD.

High expression of TCN1 is a negative prognostic biomarker and can predict neoadjuvant chemosensitivity of colon cancer

Transcobalamin (TCN1) is a vitamin B12 (cobalamin)-binding protein that regulates cobalamin homeostasis. Recent studies and bioinformatic analyses have found that TCN1 is highly expressed in cancer tissues and is associated with tumour aggressiveness and poor prognosis. The present study aimed to detect TCN1 as a novel biomarker for prognosis and chemosensitivity of colon cancer. Next-generation sequencing showed that TCN1 was one of several upregulated mRNAs in colon cancer, which was verified by further bioinformatics analyses. Western blotting (n = 9) and quantitative real time polymerase chain reaction (qRT-PCR, n = 30) revealed that TCN1 was highly expressed in colon cancer tissues at both the protein and mRNA level. A total of 194 cases of colon cancer were examined by immunohistochemistry and revealed that TCN1 expression level was related to advanced stages (P < 0.005). Kaplan-Meier analysis verified that patients with lower TCN1 expression usually had longer overall survival (P = 0.008). In addition, TCN1 was highly expressed in pulmonary metastatic tumour tissues (n = 37, P = 0.025) and exhibited higher levels in right-sided colon cancer than in left-sided colon cancer (P = 0.029). TCN1 expression in specimens that had received neoadjuvant chemotherapy decreased compared with that in colonoscopy biopsy tissues (n = 42, P = 0.009). Further bioinformatics analyses verified that apoptosis pathways might have a role in high TCN1 expression. All the studies revealed that TCN1 expression in colon cancer was significantly associated with malignant biological behaviour. Therefore, TCN1 could be used as a novel biomarker for colon cancer aggressiveness and prognosis and might also be a potential biomarker for predicting neoadjuvant chemosensitivity.