Identification of a Six-Gene Prognostic Signature Characterized by Tumor Microenvironment Immune Profiles in Clear Cell Renal Cell Carcinoma

Screening and identification of immune-related genes for immunotherapy and prognostic assessment in colorectal cancer patients

Background

Increasing evidence indicates that the immune microenvironment plays a key role in the genesis and progression of colorectal cancer (CRC). This study aimed to establish an immune-related gene (IRG) signature and determine its clinical prognostic value in patients with CRC.

Methods

The RNA sequencing and associated clinical data of CRC were downloaded from The Cancer Genome Atlas (TCGA) database. We then screened for differentially expressed IRGs by intersecting with IRGs obtained from the Immunology Database and Analysis Portal. Functional enrichment analyses were carried out to determine the potential biological functions and pathways of the IRGs. We also explored the specific molecular mechanisms of the IRGs by constructing regulatory networks. Prognostic IRGs were obtained by LASSO regression analysis, and subsequently, gene models were constructed in the TCGA dataset to confirm the predictive capacity of these IRGs. Finally, we used the TIMER tool to assess the immune properties of prognostic IRGs and correlate them with immune cells.

Results

We identified 409 differentially expressed IRGs in patients with CRC. Kyoto Encyclopaedia of Genes and Genomes and Gene Ontology enrichment analyses suggested that these differentially expressed IRGs were significantly related to 102 cancer signalling pathways and various biological functions. Based on the prediction and interaction results, we obtained 59 TF–IRG, 48 miRNA–IRG, and 214 drug–IRG interaction networks for CRC. Four prognostic genes (POMC, TNFRSF19, FGF2, and SCG2) were developed by integrating 47 survival-related IRGs and 42 characteristic CRC genes. The results of gene model showed that patients in the low risk group had better survival outcomes compared to those in the high risk group. The expression of POMC, TNFRSF19, FGF2, and SCG2 was significantly correlated with immune cells.

Conclusion

This study identified some valid IRGs, and these findings can provide strong evidence for precision immunotherapy in patients with CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01329-2.

Computational construction of TME-related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

Background

The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.

Methods

The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.

Results

In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group.

Conclusion

The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma

Aim

The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.

Method

Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.

Results

ADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.

Conclusion

Here we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.