Background Fever is a common biological phenomenon during the occurrence of infections and tumors in the body. However, it is unclear whether the increase in body temperature of tumor patients has an impact on immune cells and patient prognosis, as well as the underlying molecular regulatory mechanisms. Its effects on the patient’s disease outcome and immunotherapy efficacy are also uncertain. Clarifying the impact mechanism of fever on immune cells will provide new ideas for predicting tumor prognosis and formulating immunotherapy strategies. Methods T cells were cultured at 37.0°C and 39.5°C for 3, 24, and 72 h. Cell samples were collected for transcriptome RNA sequencing. Gene set enrichment analysis (GSEA) and TCellSI analysis were used to explore the effects of high-temperature culture on T cell function. Thermosensitive genes related to high-temperature induction were identified via differential gene analysis. A risk scoring model was constructed based on these thermosensitive genes using the least absolute shrinkage and selection operator (LASSO) algorithm and subsequently validated utilizing independent queue data (GSE68645). Independent prognostic factors were determined through univariate and multivariate Cox regression analyses. The impact of each factor on patient prognosis was evaluated using Kaplan-Meier survival curves. In addition, the impact of lactate dehydrogenase A (LDHA) expression on patient immune characteristics was evaluated using algorithms such as CIBERSORT and ESTIMATE. Results The study results indicated that T cell immune activation pathways were significantly enriched under fever stimulation, especially the Toll-like receptor signaling, macrophage activation, and interleukin-6 production. Differential gene analysis identified 840 thermosensitive genes, of which seven highly correlated genes were used to construct a risk scoring model based on the LASSO algorithm. This model successfully distinguished between high- and low-risk groups of lung adenocarcinoma (LUAD) patients in The Cancer Genome Atlas cohort. Patient prognosis in the high-risk group was significantly worse. The prognostic ability of the model was validated in the GSE68645 cohort. In addition, three independent prognostic factors (LDHA, IRX5, and CIITA) were identified and utilized to construct a nomogram. IRX5 and CIITA were associated with better prognosis, while LDHA was associated with poor prognosis (P < 0.05). Further analysis showed that LDHA was significantly correlated (P < 0.05) with the APC_combination and parainflammation pathway enrichment, PDCD1 inhibitory immune checkpoint upregulation, and tumor mutation burden. Conclusion The present study identified thermosensitive genes and their molecular characteristics. The discovery of thermosensitive genes is closely related to the prognosis of non-small cell lung cancer patients. The research results also suggested that T cell fever-related genes are significantly correlated with the upregulation of PDCD1 inhibitory immune checkpoint and tumor mutation burden, providing new ideas for lung cancer prognosis and immunotherapy treatment response.