A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

LncRNA CCAT1 knockdown suppresses tongue squamous cell carcinoma progression by inhibiting the ubiquitination of PHLPP2

Tongue squamous cell carcinoma (TSCC) is prevailing malignancy in the oral and maxillofacial region, characterized by its high frequency. LncRNA CCAT1 can promote tumorigenesis and progression in many cancers. Here, we investigated the regulatory mechanism by which CCAT1 influences growth and metastasis of TSCC. Levels of CCAT1, WTAP, TRIM46, PHLPP2, AKT, p-AKT, and Ki67 in TSCC tissues and cells were assessed utilizing qRT-PCR, Western blot and IHC. Cell proliferation, migration, and invasion were evaluated utilizing CCK8, colony formation, wound healing and transwell assays. Subcellular localization of CCAT1 was detected utilizing FISH assay. m6A level of CCAT1 was assessed using MeRIP. RNA immunoprecipitation (RIP), Co-immunoprecipitation (Co-IP) and RNA pull down elucidated binding relationship between molecules. Nude mouse tumorigenesis experiments were used to verify the TSCC regulatory function of CCAT1 in vivo. Metastatic pulmonary nodules were observed utilizing hematoxylin and eosin (HE) staining. CCAT1 silencing repressed TSCC cell proliferation, migration and invasion. Expression of CCAT1 was enhanced through N6-methyladenosine (m6A) modification of its RNA, facilitated by WTAP. Moreover, IGF2BP1 up-regulated CCAT1 expression by stabilizing its RNA transcript. CCAT1 bond to PHLPP2, inducing its ubiquitination and activating AKT signaling. CCAT1 mediated the ubiquitination and degradation of PHLPP2 by TRIM46, thereby promoting TSCC growth and metastasis. CCAT1/TRIM46/PHLPP2 axis regulated proliferation and invasion of TSCC cells, implying that CCAT1 would be a novel therapeutic target for TSCC patients.

[Expression of interleukin-34 in tongue squamous cell carcinoma and its clinical implications]

OBJECTIVE

To investigate the expression of interleukin- 34 (IL-34) in tongue squamous cell carcinoma (TSCC) and its clinical implications.

METHODS

Serum IL-34 level was detected in 36 patients with TSCC and 36 healthy individuals using enzymelinked immunosorbent assay (ELISA). The expressions of IL-34 mRNA and protein levels in TSCC and adjacent tissues were examined in 41 patients using real-time fluorescence quantitative PCR (qRT-PCR) and immunohistochemistry (IHC), and their correlation with the clinicopathological features of the patients was further analyzed. Informatic analysis of the differentially expressed genes related with IL-34 in TSCC was carried out based on String database, LinkedOmics database and GEO database, and GO functional analysis and KEGG signaling pathway enrichment analysis were performed using Webgestalt database.

RESULTS

The serum level of IL-34 was significantly lower in TSCC patients than in the healthy individuals (P < 0.001), and its expression level was also significantly lower in the tumor tissues than in the adjacent tissues (P < 0.001). The expression level of IL-34 in TSCC tissues was related with lymph node metastasis and TNM staging (P < 0.05), but not with age, gender, smoking, drinking, or tumor size (P > 0.05). Informatic analysis suggested that IL-34 had the strongest correlation with CSF1R and PTPRJ. IL-34 and its related genes in TSCC were enriched mainly in bone marrow cell differentiation, collagen-containing extracellular matrix, and cytokine binding and signal receptor activator activity. KEGG signaling pathway enrichment showed that IL-34 and the related differentially expressed genes were involved mainly in osteoclast differentiation, protein polysaccharide in cancer, and the MAPK signaling pathway.

CONCLUSION

IL-34 is lowly expressed in TSCC and participates in the occurrence and progression of TSCC, and can be potentially used as a new diagnostic biomarker and therapeutic target for TSCC.

A new prognostic model of esophageal squamous cell carcinoma based on Cloud-least squares support vector machine

Background

In view of the low accuracy of the prognosis model of esophageal squamous cell carcinoma (ESCC), this study aimed to optimize the least squares support vector machine (LSSVM) algorithm to determine the uncertain prognostic factors using a Cloud model, and consequently, to establish a new high-precision prognosis model of ESCC.

Methods

We studied 4,771 ESCC patients(training samples) from the Surveillance, Epidemiology, and End Results (SEER) database and 635 ESCC patients(validation samples) from the Henan Provincial Center for Disease Control and Prevention (HCDC) database, with the same exclusion criteria and inclusion criteria for both databases, and obtained permission to obtain a research data file in the SEER database from the National Cancer Institute. The independent risk factors were analyzed using the log-rank method, survival curves, univariate and multivariate Cox analysis. Finally, the independent prognostic factors were used to construct the nomogram, random forest and Cloud-LSSVM prognostic models were utilized for validation.

Results

The overall median survival time of the SEER database was 14 months (HCDC samples was 46 months), the mean survival time was 26.5 months (HCDC samples was 36.8 months), and the 3-year survival rate was 65.8%. This is because most of the patients with Henan samples are early ESCC, and most of the Seer patients are T3 and T4 people. The multivariate Cox analysis showed that age at diagnosis (P<0.001), sex (P=0.001), race (P=0.002), differentiation grade (P<0.001), pathologic T category (P<0.001), and pathologic M category (P<0.001) were the factors affecting the prognosis of ESCC patients. The SEER data and HCDC database results showed that the accuracy of the Cloud-LSSVM (C-index =0.71, 0.689) model is higher than the differentiation grade (C-index =0.548, 0.506), random forest (C-index =0.649, 0.498), and nomogram (C-index =0.659, 0.563). This new model can realize the unity of the randomness and fuzziness of the Cloud model and utilize the powerful learning and non-linear mapping abilities of LSSVM.

Conclusions

Due to the difference of clans between training samples and test samples, the accuracy of prediction is generally not high, but the accuracy of Cloud-LSSVM model is much higher than other models. The new model provides a clear prognostic superiority over the random forest, nomogram, and other models.

Support Tools in the Differential Diagnosis of Salivary Gland Tumors through Inflammatory Biomarkers and Radiomics Metrics: A Preliminary Study

BACKGROUND

The purpose of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR), and radiomic metrics (quantitative descriptors of image content) extracted from MRI sequences by machine learning increase the efficacy of proper presurgical differentiation between benign and malignant salivary gland tumors.

METHODS

A retrospective study of 117 patients with salivary gland tumors was conducted between January 2015 and November 2022. Univariate analyses with nonparametric tests and multivariate analyses with machine learning approaches were used.

RESULTS

Inflammatory biomarkers showed statistically significant differences (p < 0.05) in the Kruskal-Wallis test based on median values in discriminating Warthin tumors from pleomorphic adenoma and malignancies. The accuracy of NLR, PLR, SII, and SIRI was 0.88, 0.74, 0.76, and 0.83, respectively. Analysis of radiomic metrics to discriminate Warthin tumors from pleomorphic adenoma and malignancies showed statistically significant differences (p < 0.05) in nine radiomic features. The best multivariate analysis result was obtained from an SVM model with 86% accuracy, 68% sensitivity, and 91% specificity for six features.

CONCLUSIONS

Inflammatory biomarkers and radiomic features can comparably support a pre-surgical differential diagnosis.

Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling

Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.