Dataset Information

Identification of a prognostic immune-related signature for small cell lung cancer.

ABSTRACT:

Purpose

As a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread, and poor outcomes. Despite its exquisite sensitivity to chemotherapy and radiotherapy, acquired drug resistance and tumor progression are typical. This study aimed to develop a robust signature based on immune-related genes to predict the outcome of patients with SCLC.

Methods

The expression data of 77 SCLC patients from George's cohort were divided into training set and testing set, and 1534 immune-related genes from ImmPort database were used to generate and validate the signature. Cox proportional hazards and the Kaplan-Meier analysis were used for developing and testing the prognostic signature. Single-sample gene set enrichment analysis was used to determine immune cell infiltration phenotypes.

Results

A 10-gene model comprising NR3C1, NR1D2, TANK, ARAF, HDGF, INHBE, LRSAM1, PLXNA1, PML, and SP1 with the highest frequency after 1000 interactions, was chosen to construct immune-related signature. This signature showed robust predictive value for SCLC patients' survival in both training and testing sets. This signature was weakly associated with the clinic pathological values like TNM stage. Furthermore, patients with low risk presented with activation of immune signal pathways, and specific immune cell infiltration with high levels of CD56^bright NK cells but low levels of CD8⁺ T cells, mast cells, and helper T cells.

Conclusion

The present study developed immune-related signature that may help predict the prognosis of SCLC patients, which reflects an unappreciated level of heterogeneity of immunophenotype associated with diverse prognosis for specific subsets in this highly lethal cancer type.

SUBMITTER: Xie Q

PROVIDER: S-EPMC8683526 | biostudies-literature |

REPOSITORIES: biostudies-literature

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