Prediction of chemotherapeutic response in unresectable non-small-cell lung cancer (NSCLC) patients by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2- (4-sulfophenyl)-2H-tetrazolium (MTS) assay

The involvement of homeobox-C 4 in predicting prognosis and unraveling immune landscape across multiple cancers via integrated analysis

Background: There has been growing evidence that the aberrantly expressed Homeobox-C 4 (HOXC4) plays crucial roles in the development of some cancer types. However, it remains unclear as far as its expression patterns and prognostic significance are concerned, as is tumor immunity.

Methods: To investigate the expression levels and prognostic implications of HOXC4, multiple data sources were used in conjunction with quantitative real-time polymerase chain reaction (qRT-PCR) verification. Afterward, diverse immunological-related analyses, along with anti-cancer drug sensitivity, were performed in a number of cancer types. A further exploration of the underlying mechanisms of HOXC4 in tumorigenesis and immunity was carried out using the Gene Set Enrichment Analysis (GSEA) and the Gene Set Variation Analysis (GSVA).

Results: Based on extensive database mining, HOXC4 was ubiquitously expressed across 21 tumor cell lines and significantly higher than that of normal tissues in 21 tumor types. The outcome of survival analysis including overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS) and progression-free interval (PFI) revealed that upregulation of HOXC4 expression in several cancers was associated with worse prognosis. Additionally, HOXC4 was observed to correlate closely with colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), rectum adenocarcinoma (READ), and thyroid carcinoma (THCA) in terms of tumor immune cells infiltration. As a result of our comprehensive pan-cancer study, we have identified a significant link between the expression of HOXC4 and the efficacy of immunotherapy-related treatments, together with anti-cancer drug sensitivity. As a final note, HOXC4 was found to modulate multiple signaling pathways involved in tumorigenesis and immunity.

Conclusion: HOXC4 has been implicated in our study for the first time as an oncogene in cancers with a poor prognosis, potentially laying the groundwork for promising clinical biomarkers and immunotherapy approaches.

A Hybrid Interpolation Weighted Collaborative Filtering Method for Anti-cancer Drug Response Prediction

Individualized therapies ask for the most effective regimen for each patient, while the patients' response may differ from each other. However, it is impossible to clinically evaluate each patient's response due to the large population. Human cell lines have harbored most of the same genetic changes found in patients' tumors, thus are widely used to help understand initial responses of drugs. Based on the more credible assumption that similar cell lines and similar drugs exhibit similar responses, we formulated drug response prediction as a recommender system problem, and then adopted a hybrid interpolation weighted collaborative filtering (HIWCF) method to predict anti-cancer drug responses of cell lines by incorporating cell line similarity and drug similarity shown from gene expression profiles, drug chemical structure as well as drug response similarity. Specifically, we estimated the baseline based on the available responses and shrunk the similarity score for each cell line pair as well as each drug pair. The similarity scores were then shrunk and weighted by the correlation coefficients drawn from the know response between each pair. Before used to find the K most similar neighbors for further prediction, they went through the case amplification strategy to emphasize high similarity and neglect low similarity. In the last step for prediction, cell line-oriented and drug-oriented collaborative filtering models were carried out, and the average of predicted values from both models was used as the final predicted sensitivity. Through 10-fold cross validation, this approach was shown to reach accurate and reproducible outcome for those missing drug sensitivities. We also found that the drug response similarity between cell lines or drugs may play important role in the prediction. Finally, we discussed the biological outcomes based on the newly predicted response values in GDSC dataset.