Abstract 2057: Pan-cancer analysis of ion channel genes reveals patterns of large-scale transcriptional dysregulation and novel oncogenes with functions in cellular connectivity
Bibliographic record
Abstract
Abstract Ion channels (IC) are one of the largest families of highly druggable proteins targeted in therapies for many human diseases; however, their role in cancer remains understudied. IC drug repurposing for cancer therapies is an appealing avenue; however, prioritizing ICs for pre-clinical research, mechanistic elucidation, and target development remains a challenge. Here we performed a systematic, pan-cancer analysis to prioritize IC genes as putative targets for therapeutic and biomarker development. Using machine learning, we studied ~10,000 human transcriptomes from 32 cancer types of The Cancer Genome Atlas (TCGA) to identify ICs whose expression significantly associated with patient survival. IC genes showed dysregulation in all cancer types: elevated IC expression was characteristic of a subset of tumors often associated with poor patient prognosis. Excessive IC expression was unexpectedly frequent and high-magnitude compared to control sets of protein-coding genes and genes of major druggable families. We focused on ICs in glioblastoma (GBM), the most prevalent and lethal form of brain cancer in adults, prioritizing GJB2 (gap junction protein beta 2) and SCN9A (sodium voltage-gated channel alpha subunit 9) as potential GBM oncogenes. High expression of these ICs associated with poor GBM prognosis in TCGA and independent transcriptomics datasets. GJB2 and SCN9A expression was higher in GBMs than normal brain tissues and associated with GBM subtypes, histological features, and immune cell infiltration. At the single-cell level, GJB2 and SCN9A were predominantly expressed in malignant cell types in GBMs. We validated GJB2 and SCN9A with shRNA-mediated knockdowns (KDs) in patient-derived GBM cell lines and mouse xenograft models. First, GJB2 and SCN9A KDs significantly impaired cell viability and spheroid formation. We then validated survival associations, showing that GJB2 and SCN9A KDs prolonged survival of mice with GBM xenografts. Finally, we showed GBJ2 KD impaired tunneling nanotube length in GBM cells. Transcriptome-wide profiling of IC KD cells revealed hundreds of differentially expressed genes. Integrative pathway enrichment across IC KDs in GBM cells and IC-stratified TCGA samples identified dysregulated pathways with functional themes of neuronal development, cancer hallmarks, and regulation of glial cells, including tumor cell connectivity pathways in GBM, corroborating the phenotypes observed in our functional validations. Our pan-cancer analysis prioritized ICs for further pre-clinical development and mechanistic study. The functional validation experiments of GJB2 and SCN9A highlight their possible roles in GBM pathology and lends confidence to our analysis. This study demonstrates the utility of investigating ICs as avenues for drug repurposing and the development of novel cancer therapeutics. Citation Format: Alexander Bahcheli, Hyun Min, Xi Huang, Jüri Reimand. Pan-cancer analysis of ion channel genes reveals patterns of large-scale transcriptional dysregulation and novel oncogenes with functions in cellular connectivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2057.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".