Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer

ANXA5 predicts prognosis and immune response and mediates proliferation and migration in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy that often develops unnoticed. Typically, these tumors are identified at advanced stages, resulting in a relatively low chance of successful treatment. Anoikis serves as a natural defense against the spread of tumor cells, meaning circumventing anoikis can effectively inhibit tumor metastasis. Nonetheless, studies focusing on anoikis in the context of HNSCC remain scarce.

METHODS

Anoikis-related genes (ARGs) were identified by using the GeneCards and Harmonizome databases. Expression data of these genes and relevant clinical features were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A LASSO regression and a prognostic risk score model were developed to determine their prognostic significance. The analysis included the use of the CIBERSORT algorithm to quantify immune and stromal cell presence. Furthermore, in vitro and in vivo, we confirmed the expression and functional roles of proteins and mRNA of genes independently predictive of prognosis.

RESULTS

The study identified eight genes linked to prognosis (ANXA5, BAK1, CDKN2A, PPARG, CCR7, MAPK11, CRYAB, CRYBA1) and developed a prognostic model that effectively forecasts the survival outcomes for patients with HNSCC. A higher survival likelihood is associated with lower risk scores. In addition, a significant relationship was found between immune and risk score, and ANXA5 deletion promoted the killing of HNSCC cells by activated CD8+ T cells. During the screening process, 65 different chemotherapeutic drugs were found to have significant differences in IC50 values when comparing high- and low-risk categories. ANXA5 emerged as a gene with independent prognostic significance, exhibiting notably elevated protein and mRNA levels in HNSCC tissue compared to non-tumorous tissue. The suppression of ANXA5 gene activity resulted in a substantial decrease in both the growth and mobility of HNSCC cells. Animal model experiments demonstrated that inhibiting ANXA5 suppressed HNSCC growth and migration in vivo.

CONCLUSION

Through bioinformatics, a prognostic risk model of high precision was developed, offering valuable insights into the survival rates and immune responses in patients with HNSCC. ANXA5 is highlighted as a significant prognostic factor among the identified genes, indicating its promise as a potential therapeutic target for those with HNSCC.

Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics

Objective

This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics.

Methods

Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the "CancerSubtypes" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics.

Results

Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm.

Conclusion

Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

The Risk Function of Breast and Ovarian Cancers in the Avrami-Dobrzyński Cellular Phase-Transition Model

Specifying the role of genetic mutations in cancer development is crucial for effective screening or targeted treatments for people with hereditary cancer predispositions. Our goal here is to find the relationship between a number of cancerogenic mutations and the probability of cancer induction over the lifetime of cancer patients. We believe that the Avrami-Dobrzyński biophysical model can be used to describe this mechanism. Therefore, clinical data from breast and ovarian cancer patients were used to validate this model of cancer induction, which is based on a purely physical concept of the phase-transition process with an analogy to the neoplastic transformation. The obtained values of model parameters established using clinical data confirm the hypothesis that the carcinogenic process strongly follows fractal dynamics. We found that the model's theoretical prediction and population clinical data slightly differed for patients with the age below 30 years old, and that might point to the existence of an ancillary protection mechanism against cancer development. Additionally, we reveal that the existing clinical data predict breast or ovarian cancers onset two years earlier for patients with BRCA1/2 mutations.

A comparative study of clustering methods on gene expression data for lung cancer prognosis

Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two lung adenocarcinoma datasets. For each method, we clustered patients into two clusters and assessed the difference in patient survival time between clusters. Unsupervised methods were found to have large logrank p-values and no significant results in most cases. Semi-supervised and supervised methods had improved performance over unsupervised methods and very significant p-values. These results indicate that unsupervised methods are not capable of identifying clusters with significant differences in survival prognosis in most cases, while supervised and semi-supervised methods can better cluster patients into clinically useful subtypes.