Clinical data and RNA sequences from 11,069 samples across 33 cancer types were gathered from the TCGA database using UCSC Xena (https://xena.ucsc.edu/). To compare these cancer samples with normal tissues, gene profile data from GTEx (https://commonfund.nih.gov/GTEx) provided a vital reference.

A549, HBE, PC9, LLC, H1975, and H1299 cell lines were cultured in RPMI-1640 with 10% FBS and 1% penicillin-streptomycin at 37°C and 5% CO₂. Subsequently, the medium was replaced every 2–3 days to maintain optimal growth conditions and prevent nutrient depletion and metabolite accumulation. RNA was extracted using TRIzol Reagent, converted to cDNA via PrimeScript™ RT Reagent Kit, and analyzed by quantitative Real-time PCR (qPCR) with SYBR Premix Ex Taq II Reagent Kit. TUBA1B primers used were: forward 5′-CTC​AGT​TGA​TTA​TGG​CAA​GAA​GTC-3′ and reverse 5′-AGG​CGG​TTA​AGG​TTA​GTG​TAG​GT-3′. GAPDH was used as a reference. Relative expression was calculated by the 2^−ΔΔCT method, and analyses were performed in GraphPad Prism.

Cell proliferation was measured with an EdU incorporation assay, where cells were incubated with EdU, fixed, and detected using the Click-iT EdU Imaging Kit. DAPI counterstained nuclei, and images were captured by fluorescence microscopy.

Transwell assays were used to evaluate migration and invasion. For migration, transfected A549 cells were seeded in serum-free medium in the upper chamber, with medium containing 10% FBS in the lower chamber. After 24 h, migrated cells were fixed, stained, and counted. Invasion assays followed similar steps, but with Matrigel-coated inserts.

Cell viability was determined using the Cell Counting Kit-8 (CCK-8) assay. Cells were seeded into 96-well plates and incubated overnight at 37°C in a 5% CO₂ incubator to ensure cell attachment. The medium in each well was carefully aspirated and replaced with 100 μL of fresh medium containing 10% CCK-8 solution. The plates were then incubated for an additional 2 h at 37°C. Absorbance was measured at 450 nm using a microplate reader.

The Human Protein Atlas (http://www.proteinatlas.org/) provided immunohistochemical data on TUBA1B, allowing analysis of expression and localization patterns in tumors and normal tissues.

Using the TCGA database, we analyzed TUBA1B expression and cancer prognosis through survival indices: overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI). Analyses were conducted with R’s “survival” and “forestplot” packages, using univariate Cox analysis to assess TUBA1B’s impact on these outcomes.

Gene Set Enrichment Analysis (GSEA) assessed TUBA1B and associated genes in cancers using Pearson correlation and the R “clusterProfiler” package. Gene Set Variation Analysis (GSVA) was performed with “ssGSEA” and data from MSigDB.

We used the “ESTIMATE” R package to assess stromal, immune, and tumor purity scores in the TCGA cohort. Correlations between TUBA1B expression and TME-related pathways were explored, focusing on immune infiltration, immunomodulatory genes, MHC genes, and chemokines/receptors. Data from the Immune Cell Abundance Identifier (ImmuCellAI) and TIMER2 (http://timer.cistrome.org/) were visualized with the “ggplot2” package.

Differential TUBA1B expression between cancer and normal tissues was assessed with t-tests. Specifically, for the t-test analysis, we first verified the assumptions of normality and homogeneity of variances for the data. If the data met the assumptions of parametric tests, an independent samples t-test was employed to compare the means of TUBA1B expression levels between the cancerous and normal tissue groups. In cases where the assumptions were violated, non-parametric alternatives were considered. Kaplan-Meier survival analysis and log-rank tests evaluated patient outcomes. Correlations were tested using Spearman’s or Pearson’s test, as appropriate. All analyses were performed in R (version 4.1.0), with p-values <0.05 considered significant, ensuring reliability in deriving conclusions.

To comprehensively examine TUBA1B expression patterns in various human cancers, an extensive analysis was conducted using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. This investigation aimed to elucidate the TUBA1B mRNA expression levels across different cancers and compare them between normal and tumor tissues across multiple cancer types. The analysis revealed significant upregulation of TUBA1B in 28 out of the 33 cancers studied. Notably, cancers with increased TUBA1B expression included adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colonic adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal cancer (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), renal chromophobe cell carcinoma (KICH), renal clear cell carcinoma (KIRC), kidney papillary cell carcinoma (KIRP), low-grade glioma (LGG), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian plasmacytoid cystic adenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), rectal adenocarcinoma (READ), cutaneous melanoma (SKCM), gastric adenocarcinoma (STAD), testicular germ cell tumor (TGCT), thyroid adenocarcinoma (THCA), thymoma (THYM), endometrial cancer (UCEC), and uterine carcinosarcoma (UCS) (Figure 1A; Supplementary Table S1). Interestingly, acute myelogenous leukemia (LAML) exhibited lower TUBA1B expression, distinguishing it from other cancer types. Further comparative analysis of TUBA1B expression levels across tumor tissues showed that glioblastoma multiforme (GBM) had the highest expression, while hepatocellular carcinoma (LIHC) exhibited the lowest (Figure 1B). In normal tissues, TUBA1B expression was highest in bone marrow and lowest in the pancreas (Figure 1C). These expression patterns were validated through immunohistochemical data from the Human Protein Atlas (HPA), which showed strong TUBA1B staining in tumor tissues, whereas normal tissues exhibited weaker staining (Figure 2). These findings suggest TUBA1B’s potential as a biomarker for diagnostic and therapeutic applications. Further investigation into TUBA1B’s mechanisms and functions in these cancer types could pave the way for targeted interventions and personalized treatments, enhancing patient outcomes.

To understand TUBA1B’s role in cancer prognosis, an in-depth analysis was performed using univariate Cox regression to examine risk ratios for overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) across various cancers. This analysis revealed that high TUBA1B expression is a significant risk factor for OS in several cancers, including LGG, KICH, LIHC, LUAD, MESO, BLCA, ACC, BRCA, KIRP, HNSC, THYM, PRAD, and OV (Figure 3A, P < 0.05), suggesting that elevated TUBA1B expression correlates with poorer survival outcomes in these cancers. Similarly, TUBA1B was a risk factor for DSS in LGG, KICH, LUAD, MESO, KIRP, PRAD, LIHC, ACC, BLCA, HNSC, PAAD, OV, and BRCA (Figure 3B), underscoring its prognostic relevance. High TUBA1B expression was also associated with shorter disease-free intervals in patients with PAAD, UCS, CESC, and CHOL (Figure 3C), indicating a potential role in disease recurrence and progression. Cox regression analysis for PFI revealed that TUBA1B was an unfavorable factor in patients with KICH, ACC, LGG, BLCA, LUAD, LIHC, PRAD, OV, UVM, and MESO (Figure 3D), further supporting its use as a prognostic marker. Further analysis revealed that TUBA1B upregulation consistently correlated with advanced stages in various cancers (Figure 4), suggesting its role in tumor progression and aggressiveness and its potential as a marker for disease stage. These findings highlight TUBA1B as a critical prognostic indicator in cancer.

Tumorigenesis and cancer progression are closely associated with genetic changes, including gene mutations, copy number variants (CNV), and DNA methylation. To fully understand the genetic mutations affecting TUBA1B, we conducted an analysis of tumor samples from 10,953 patients using the cBioPortal platform. The three-dimensional structure of TUBA1B is shown in Figure 5A. Our findings reveal a high prevalence of TUBA1B amplifications, with diffuse large B-cell lymphoma showing the highest frequency (Figure 5B). Specific mutation sites and types in the TUBA1B gene are detailed in Figure 5C, highlighting its diverse mutation profile. In addition, a significant positive correlation between TUBA1B expression and CNV was identified across various cancers (Figure 5D), suggesting that CNV may drive TUBA1B dysregulation, influencing tumor progression. To further investigate, we analyzed DNA methylation’s role in TUBA1B expression, given its known impact on immune cell regulation and tumor immunosurveillance. This analysis across multiple cancers revealed significant correlations between TUBA1B expression and promoter methylation in nine tumor types, with ovarian cancer (OV) showing the highest positive and lower-grade gliomas (LGG) the highest negative correlation (Figure 5E).

To explore TUBA1B’s biological roles across cancers, we conducted a Gene Set Enrichment Analysis (GSEA) using the “clusterprofiler” package. This analysis revealed that TUBA1B is involved in cell cycle pathways, including “Cell Cycle,” “Signaling by Rho GTPases, Miro GTPases, and RHOBTB3,” and “Vesicle-mediated transport,” all found across nine cancer types (Figure 6). These pathways underscore TUBA1B’s regulatory role in fundamental cellular processes within cancer. Additionally, TUBA1B is engaged in immunomodulation pathways linked to adaptive and innate immunity and cytokine signaling, pointing to its dual involvement in tumor growth and immune system responses.

Further, a comprehensive Gene Set Variation Analysis (GSVA) was conducted across 50 hallmark pathways (Figure 7), revealing a strong association between TUBA1B expression and pathways that drive cancer progression, such as hypoxia, KRAS, P53, and MYC signaling. This suggests TUBA1B’s influence on core molecular processes in tumor development. TUBA1B expression also positively correlated with immune-related pathways like interferon-alpha/gamma response, IL-2/STAT5 signaling, and IL6/JAK/STAT3 signaling, indicating TUBA1B’s significant role in the tumor immune microenvironment (TIME).

To validate these findings, we examined TME-related pathways using TCGA immune data and a large-scale clinical study. Results confirmed TUBA1B’s close association with pathways involved in tumor proliferation and immune response. Figure 8A presents a heatmap showing correlations between TUBA1B and cellular processes in the TME. Red indicates positive and blue negative correlations, with intensity reflecting correlation strength. Data indicate that TUBA1B is positively associated with DNA replication, repair, and antigen processing and negatively correlated with immune checkpoint and CD8⁺ T cell functions, suggesting its dual role in promoting cell growth and suppressing immune responses. Figure 8B shows TUBA1B expression across various lung cancer cell lines, with notably higher expression in A549 and LLC cells compared to HBE (normal control). This upregulation in cancer cells may support their proliferation and survival. Knockdown of TUBA1B in A549 cells significantly reduced mRNA levels (Figure 8C), confirming the gene knockdown’s efficacy.

In a cell proliferation assay, fluorescence microscopy revealed numerous EdU-positive cells (red) in the control group, indicating active proliferation, while the si-TUBA1B group had fewer EdU-positive cells (Figure 8D), showing TUBA1B knockdown’s inhibitory effect on proliferation. Migration assay results showed more cells migrating through the Transwell membrane in the control group compared to the si-TUBA1B group (Figure 8E), suggesting that TUBA1B knockdown hinders cell migration. Similarly, invasion assays indicated that more cells invaded through the Matrigel in the control group compared to the si-TUBA1B group (Figure 8F), demonstrating that TUBA1B knockdown reduces cell invasion. Figure 8G is the statistical analysis of the migration and invasion results. In the CCK-8 assay, the results indicated a significant difference between the control group and the si-TUBA1B group. The control group exhibited a relatively high absorbance, while the si-TUBA1B group showed a notably lower absorbance, demonstrating that the knockdown of TUBA1B led to a marked reduction in cell viability and proliferation (Figure 8H), further confirming the role of TUBA1B in cell growth as detected by the CCK-8 assay. These findings underscore TUBA1B’s critical role in promoting cell proliferation, migration, and invasion, aligning its expression with oncogenic and immune-suppressive pathways. These results further support TUBA1B as an oncogene in cancer.

To deepen understanding of TUBA1B’s role in the immunosuppressive tumor microenvironment, we performed correlation analyses to examine TUBA1B expression, immune cell infiltration, and immune-related gene interactions. Immune data from ImmuCellAI and TIMER2 were analyzed independently (Figures 9A,B), consistently showing a positive correlation between TUBA1B expression and immunosuppressive cells like regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and cancer-associated fibroblasts (CAFs). This strong positive correlation suggests that high TUBA1B expression contributes to an immunosuppressive TME. Conversely, TUBA1B expression negatively correlated with immune killer cells, including NK/NKT cells, CD4⁺ T cells, and CD8⁺ T cells, which are essential for anti-tumor immunity, indicating that elevated TUBA1B levels may inhibit effective immune responses.

Our analysis of TUBA1B’s influence on immune-related genes further revealed a positive correlation with key immunosuppressive genes, such as TIGIT, PDCD1 (PD-1), LAG3, CTLA4, and CD274 (PD-L1) (Figure 10A). These genes play vital roles in immune checkpoint regulation and cancer cell immune evasion, suggesting that TUBA1B may impact immunosuppressive factor expression. Furthermore, TUBA1B expression correlated positively with TGF-β and IL-10, two factors associated with TAMs and CAFs, indicating that TUBA1B may influence these cells’ function within the TME. Additional analysis revealed close associations between TUBA1B and immunomodulatory genes like chemokines (Figure 10B), chemokine receptors (Figure 10C), and MHC genes (Figure 10D). Key genes such as CCR2, CCL2, CXCR4, and CCR5 are critical for TAM recruitment, while MHC genes are essential for immune recognition and regulation. These analyses highlight TUBA1B as a central player in modulating the immunosuppressive TME. The observed positive correlations between TUBA1B and immunosuppressive factors and immune-regulatory genes provide insight into TUBA1B’s role in immune response interactions within cancer.

In conclusion, our study reveals the complex relationships between TUBA1B expression, immune cell infiltration, and immune genes within the TME. The positive correlations between TUBA1B and immunosuppressive factors suggest its potential role in establishing an immunosuppressive TME, offering valuable insights for developing targeted therapies to modulate the TME and enhance immune-based cancer control strategies for improved patient outcomes.

Recent advances in tumor immunotherapy, especially with immune checkpoint inhibitors (ICIs), have shown notable clinical success. Predictive biomarkers like tumor mutational burden (TMB) and microsatellite instability (MSI) scores are now established as indicators of response to ICI therapy and overall prognosis. In this study, we examined the relationship between TUBA1B expression and TMB/MSI across seven cancer types (Figures 11A, B). Our findings revealed significant correlations, suggesting that TUBA1B expression may strongly influence patients’ immunotherapy responses. To further validate these findings, we analyzed a comprehensive immunotherapy dataset, assessing the association between TUBA1B expression and clinical response across different cancers. Kaplan-Meier analysis demonstrated that low TUBA1B expression correlated with better outcomes, including extended overall survival and higher response rates in patients receiving ICIs (Figures 11C, D). These results indicate TUBA1B expression’s crucial role in determining patient response to immunotherapy.

Furthermore, we explored the Cancer Drug Sensitivity Genomics (GDSC) database, integrating IC50 values for 265 small molecule drugs with mRNA expression data from 860 cell lines, to identify potential antitumor drugs associated with TUBA1B expression (Figure 11E). The top 30 drugs showing the strongest correlations with TUBA1B expression reinforce its value as a predictive biomarker, while also expanding precision treatment options for lung adenocarcinoma patients. These insights underline the importance of considering TUBA1B expression in therapeutic planning. By leveraging the connection between TUBA1B expression and small molecule drugs, we unlock new avenues for targeted therapies tailored to individual patient profiles. Additionally, understanding the microenvironment effect on drug resistance in the context of TUBA1B dysregulation is crucial. The complex interplay between the tumor microenvironment and TUBA1B could potentially modulate the efficacy of therapeutic agents, highlighting the need for a more comprehensive approach that integrates both genomic and microenvironmental factors to overcome drug resistance and optimize treatment strategies, thus offering promising improvements in personalized cancer care and outcomes.