29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 li_fei@fudan.edu.cn. 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 The inconsistency of the association between genes and cancer prognosis is often attributed to many variables that contribute to patient survival. Whether there exist the Genes Steadily Associated with Prognosis (GEARs) and what their functions are remain largely elusive. We have developed a novel method called "Multi-gradient Permutation Survival Analysis " (MEMORY) to screen the GEARs using RNA-seq data from the TCGA database. Then we employed a network construction approach to identify hub genes from GEARs, and utilized them for cancer classification. In the case of LUAD, the GEARs were found to be related to mitosis. Our analysis suggested that LUAD cell lines carrying PIK3CA mutations exhibit increased drug resistance. For BRCA, the GEARs were related to immunity. The analysis revealed that CDH1 mutation might influence immune infiltration through the EMT process in BRCA. We further explored the prognostic relevance of mitosis and immunity through their respective scores. This study offers significant biological insights into GEARs and highlights their potential as robust prognostic indicators across diverse cancer types. Associated with Prognosis (GEAR), Hub Genes, Mitosis, Immune 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104

Cancer is one of the leading causes of death worldwide, with over 200 types identified. Despite of huge advancements in the field, accurately predicting cancer patient prognosis still remains a significant challenge1,2. Previous studies have highlighted the complexity of this task, which is influenced by various factors including cancer type, clinical stage, therapeutic interventions, nursing care, unexpected comorbidities, and other non-cancer related illnesses that may interplay3-6. Furthermore, the gene expression plays a crucial role in predicting cancer patient prognosis, such as HER2, VEGF, Ki67, etc7-12. Nevertheless, there is still a need for further improvement in prognostic accuracy to better inform treatment decisions and patient outcomes.

Survival analysis is commonly used to assess the correlation between genes and cancer patient prognosis13. However, inconsistent findings are frequently observed, even within the same type of cancer. For instance, studies exploring the correlation between CCND1 and prognosis in NSCLC reported contrasting results, including positive correlation, inverse correlation, or negligible influence14-16. These discrepancies can be attributed to various influential factors, such as differences in sample size, cohort characteristics (including cancer subtypes and tumor staging) and variations in therapy approaches17. Such observations raise an important question:

whether there exist Genes Steadily Associated with Prognosis (GEARs) that consistently correlate with patient outcomes across different conditions, particularly considering the varying sample sizes used in different studies? Affirmative answers to this question would have significant implication not only for the development of more accurate prognostic models but also for enhancing our understanding of cancer biology. The Cancer Genome Atlas (TCGA) is a comprehensive cancer genomics project initiated in the United States. It consists of transcriptome data, genomic data, and clinical information pertaining to 33 different cancer types, making it the largest cancer clinical sample database currently available. In this study, we developed a novel method called "Multi-gradient Permutation Survival Analysis" (MEMORY) with the utilization of TCGA RNA-seq data, and accessed the potential existence of GEARs across 15 cancer types, each comprising a cohort 106 107 of over 200 patients. Furthermore, we also evaluated the prognostic predictive power of these GEARs and explored their potential biological functions in driving cancer progression. 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135

GEARs are a group of genes consistently and significantly correlate with patient survival, independent of the sample size. To identify these GEARs, we developed a novel method called "Multi-gradient Permutation Survival Analysis " (MEMORY). This method allows us to assess the correlation between a specific gene and cancer patient prognosis using available transcriptomic dataset (Figure 1A, Table 1). Initially, we started with a sampling number at 10% of the cohort size initially, and gradually increased the sampling number with each 10% increment, which was analyzed with 1000 permutations. By calculating the statistical probability of each gene's association with patient survival, we can identify a group of

To further identify crucial genes within the GEARs, we conducted a significant survival network (SSN) and extracted the higher-ranked edges to construct the core survival network 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 (CSN) (Figure 2A, Table 4). We created 6 CSNs with varying numbers of edges ( 250, 500, 1000, 2000, 4000, 8000). The top 10 GEARs with highest degree in these networks were defined as hub genes 46. We then classified the samples using the hub genes derived from these networks and evaluated their predictive power for patient survival. We determined that the optimal number of edges for the network ranged from 1000 to 2000, as it demonstrated the best clustering effect of hub genes (Figure 2B, Supplementary Figure 3A-O, Supplementary Figure 4A-O). Furthermore, we conducted GOEA on the hub genes selected from the network with 1000 edges and found that the results were consistent with the GEAR analysis (Table 5). Specifically, the hub genes in LIHC, LGG, and LUAD were enriched in mitosis-related pathways, whereas the hub genes in BRCA and UCEC were enriched in immune-related pathways (Figure 2C). For instance, the 9 hub genes in LUAD were associated with functions related to mitosis, whereas the 8 hub genes of BRCA were associated with immune-related functions (Figure 2D-E).

Furthermore, we conducted survival-dependent analyses on the hub genes of LGG, LIHC, and LUAD. These analyses revealed that mitosis-related hub genes are closely associated with cancer cell viability, especially those hub genes that are correlated with multiple cancer types such as CDC20, TOP2A, BIRC5 and TPX247-50 (Supplementary Figure 5A-C). The importance of these genes is well established. For instance, TPX2, a hub gene in all three cancers, is known to play a crucial role in normal spindle assembly during mitosis and is essential for cell proliferation51. The significance of these hub genes for the survival of cancer cells suggests that the expression levels of these hub genes can be used to screen for inhibitors of tumor cell growth. By integrating the Genomics of Drug Sensitivity in Cancer (GDSC) and Connectivity Map (cMAP) databases, we identified 76 individual compounds that are able to effectively suppress the expression of the 10 hub genes in LUAD cell lines (Figure 2F, Supplementary Figure 5D-E). These compounds also significantly inhibit LUAD cell growth and may serve as potential therapeutic agents for the treatment of LUAD. We then analyzed the LUAD dataset, which had a larger sample size compared to LGG and LIHC. Based on the expression of the hub genes, we classified the LUAD samples into three 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 subgroups: mitosis low (ML), mitosis medium (MM), and mitosis high (MH) (Figure 2G, terminal respiratory unit (TRU), the proximal-proliferative (PP), and the proximalinflammatory (PI) subgroups52. Our analysis revealed that the ML subgroup was primarily enriched with TRU subgroup and a small number of samples from the PP subgroup, but not the PI subgroup. The MH subgroup showed high expression of mitosis-related genes and predominantly encompassed PI and PP subgroups, whereas the MM group exhibited intermediate levels of mitosis-related gene expression (Figure 2G-H). This suggests that the new categorization method can help identify new factors that influence patient prognosis (Supplementary Figure 5F-G).

Distinct genetic mutation landscapes characterize different clusters of LUAD The expression of hub genes provided crucial indicators for distinguishing various subgroups of LUAD. However, the underlying mechanisms driving these variations in expression patterns remain unknown. In our subsequent research, we aimed to explore the genomic differences, particularly in terms of gene mutations, among different LUAD subgroups. Initially, we analyzed the variations of genetic mutation landscape of different LUAD subgroups by assessing the tumor mutation burden (TMB). Interestingly, we found significant differences in TMB among the various subgroups of LUAD (Figure 3A). Analysis of common driver genes further revealed distinct proportions of tumors with ALK and ROS1 fusions in the favorable prognosis ML and MM subgroups, compared to tumors with mutations in KRAS, EGFR, BRAF, and ERBB2 (Figure 3B). Furthermore, we observed unique mutation characteristics in each of these 3 subgroups (Figure 3C-E). For example, TP53 mutation, a prevalent tumor suppressor gene mutation, was frequently observed in the MH subgroup with a mutation rate of 73%, compared to mutation rates of 14% in the ML subgroup and 39% in the MM subgroup. Additionally, genes such as CSMD3, RP1L1, ZFHX4 exhibited similar trend in mutation frequency across the three subgroups. These findings indicate substantial genomic differences among these three LUAD subgroups based on the hub genes. 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249

We identified gene mutations that showed significant changes through gene dependence analysis (Figure 4A). To further the functional implications of these mutations, we enriched them using a pathway system called Nested Systems in Tumors (NeST)53. The results revealed notable differences in multiple functional pathways, including androgen receptor, cell cycle, TP53, EGFR, IL-6, and PIK3CA-related pathways (Figure 4B). To gain further insights into the functional implications of these different mutations, we assessed the gene dependency of cells with these mutations using the DepMap database. Importantly, we observed that the pathway differences between the MM and MH clusters were primarily associated with PIK3CA-related pathways. Previous studies have reported that PIK3CA mutation confers resistance in colorectal cancer, lung cancer, and breast cancer54-56. Therefore, we aimed to validate the role of PIK3CA mutations in drug resistance using public databases. We further analyzed A549 cells (a lung adenocarcinoma cell line with KRAS mutation) and SW1573 cells (a lung adenocarcinoma cell line with both KRAS and PIK3CA mutations) using their respective GDSC data and identified five potentially effective inhibitors (BMS345541, Dactinomycin, Epirubicin, Irinotecan, and Topotecan) from the previously screened set of 76 LUAD cell growth inhibitors. Strikingly, SW1573 cells exhibited increased resistance to all these 5 inhibitors when compared to A549 cells (Figure 4C). In line with this, clinical data also supports the notion that concurrent PIK3CA mutation is a poor prognostic factor for LUAD patients57. These findings suggest that PIK3CA mutation may contribute to drug resistance in LUAD.

Distinct clusters of BRCA exhibit different immune infiltration landscapes We further focused on the immune-related cancer types, with BRCA standing out as a representative immune-related cancer. This prompted us to conduct a more comprehensive analysis (Figure 1C). We hierarchically grouped the BRCA subgroups into three distinct clusters based on the expression of hub genes: Immune low (IL), Immune medium (IM), and Immune high (IH) (Figure 5A, Table 7). Interestingly, all the classifications based on the widely used PAM50 classification contained samples from the IL, IM, and IH subgroups, although there were minor differences in proportions58 (Figure 5B, Supplementary Figure 6A-B). In basal-type breast cancer, which generally has a poor prognosis, a set of samples 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 exhibited high immune infiltration and consequently better outcomes. Conversely, within the HER2 subgroups, known for a better prognosis, there were samples with poor outcomes. Integrating our method with traditional classification may enable a more detailed stratification of breast cancer samples. Mutation analysis revealed distinct patterns of gene mutations among the three subgroups, with significant differences in mutation frequencies of genes such as TP53, CDH1, and LRP2 (Figure 5C-E). We conducted a comparison of the proportions of immune cells across these three subgroups and observed a strong association between the overall immune cell proportion and the clustering results based on hub genes (Figure 5F, Supplementary Figure 6C-L). Specifically, the IH subgroup exhibited significantly higher proportions of CD8+ T cells and Treg cells, with mean percentages at 5.0% and 6.0%, respectively, compared to 1.4% and 3.2% in the IM subgroup, and 0.3% and 1.6% in the IL subgroup. These findings suggest the presence of distinct immune responses in these three subgroups, indicating a potential association between genetic mutation pattern and the immune microenvironment landscape.

Next, we explored the specific genomic factors influencing immune infiltration in BRCA. Mutation analysis revealed that CDH1 gene mutation ranked as the second most prevalent genetic alteration in BRCA samples, following TP53 mutation (Figure 5C-E). Previous study has indicated that CDH1 is involved in mechanisms regulating cell-cell adhesions, mobility, and proliferation of epithelial cells59. We found that CDH1-mutant samples exhibited significantly lower CDH1 expression compared to CDH1-wildtype samples, and CDH1 expression showed a close correlation with immune cell infiltration (Figure 5G, Supplementary Figure 6M). These results were consistent with clinical observations of CDH1 mutation and high immune infiltration in invasive lobular carcinoma of the breast 60. We further investigated how CDH1 mutation influenced immune infiltration. In BRCA, we observed a significant association between the expression of CDH1 and the expression of EMT marker genes VIM and TWIST2 (Supplementary Figure 6N-O)61,62. This suggests that the regulation of CDH1 is intricately linked with EMT process. Through calculating the EMT score, we observed a positive correlation between the EMT score and the proportion of immune infiltration (Figure 5H). Consequently, CDH1 might influence immune infiltration 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 through the EMT process.

Mitotic and immune signatures predict patient prognosis at pan-cancer level We further sought to explore the prognostic relevance of mitosis and immune by calculating their scores and assessing their correlation with patient outcomes at the pan-cancer level. The scores for these biological processes were computed using RNA-seq data from the TCGA database, and the median score was used as a cut-off to categorize patients into different groups. Out of the 33 cancer types analyzed, the prognosis of 17 cancer types showed a significant correlation with at least one of these two pathways (Figure 6). Specifically, 10 cancer types were exclusively associated with the mitosis score, 2 cancer types were exclusively associated with the immune score, and 5 cancer types exhibited a correlation with both mitosis and immune scores simultaneously. Overall, the identification of mitosis and immune-related biological processes as significant prognostic factors at the pan-cancer level suggests their potential utility as valuable biomarkers for predicting patient prognosis.

Due to the multifaceted nature of variables affecting cancer patient prognosis, it remains uncertain whether there exist a set of genes steadily associated with cancer prognosis， regardless of sample size and other factors. Here, we utilized the MEMORY method to address this question and discovered that all the cancer types have GEARs. We observed significant variation in the number of GEARs among different cancer types, indicative of cancer type-specific patterns. The substantial heterogeneity in driver gene and mortality rates among various cancer types could potentially explain this phenomenon. For example, THCA, known for its low malignancy, displays the fewest genetic expression alterations among all the studied cancer types. This observation could be correlated with the relatively high fiveyear survival rate in THCA, which exceeds 50% even in advanced stage63. In contrast, PRAD, despite of a favorable prognosis similar to THCA, exhibits a significantly higher number of genetic expression alterations.

The gene expression profiles and clinical information of 33 cancers were obtained from the TCGA database and downloaded by the GDC data website (https: //portal.gdc.cancer.gov/).

Gene expression data of cancer patients were obtained from the RNA-seq expression matrix of TCGA after TPM normalization. We randomly sampled the gene expression data according to the gradient. The sampling strategy was as follows: Ten gradient increases in sample size were pre-set, ranging from approximately 10% to about 100%, with intervals of 10%. Random samples were taken from total samples of each cancer 1000 times, based on each pre-set sample size. Multiple sampling matrix was obtained after the sampling strategy was performed in each gradient. The survival analysis was performed for every sampling matrix by “survival” and “survminer”. “survival” and “survminer” are R packages for survival analysis. The survival analysis method was as follows according to the median expression value of every gene, every sampling matrix was divided into a high and low expression group and performed survival analysis by “survival” and “survminer”. We used 1 for significant survival analysis results (P < 0.05) and 0 for non-significant results (P > 0.05).

The survival analysis matrices were integrated to a significant-probability matrix by a = ∑10=010 × × 1000 (1) where the Aij is the value from row i and column j in the significant-probability matrix. We defined the sampling size kj reached saturation when the max value of column j was equal to 1 in a significant-probability matrix. The least value of kj was selected, and the genes with their corresponding Aij greater than 0.8 were extracted as GEAR. 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444

We also defined survival analysis similarity (SAS) as the similarity of the effect on patient prognosis in two genes. The SAS computational formula is as follows: =

∩ ∪ −2( ∩ )+1 (2) Where A and B are results of 1000 survival analyses at the first sampling gradient, which are sequences of 0 and 1 of length 1000. ∩ means the number of events that are simultaneously 1 in both A and B. ∪ means the size of the union of A and B. The SAS of significant genes with total genes was calculated, and the significant survival network was constructed. Then some SAS values at the top of the rankings were extracted, and the SAS was visualized to a network by Cytoscape93. The network was named core survival network (CSN). The degree of each node was calculated, and the nodes with the top10 degrees were defined hub genes. The effect of molecular classification by hub genes is indicated that 1000 to 2000 was a range that the result of molecular classification was best. One thousand was chosen as the number of SAS. Therefore, one thousand was selected as the number of SAS values for constructed significant survival network.

Gene ontology has been used to classify genes based on functions. The gene functions were divided three types, includes molecular function (MF), biological process (BP), and cellular component (CC). ClusterProfiler is an R package for gene set enrichment analysis94. The gene ontology enrichment analysis (GOEA) was processed in significant gene sets and hub genes by ClusterProfiler.

Tumor samples were genotyped. First, the expression matrix of hub genes corresponding to cancer types was extracted from RNA-seq data. The matrix M is log-transformed after adding a pseudo count of 1: ′ =

2( + 1). Finally, ′ was grouped by hierarchical clustering.

Calculation of tumor mutation burden (TMB) and differential mutation The TMB and differential mutation gene analysis were carried out to explore the differences between different genotyped samples at the genomic level. The data are from the gene preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 mutation data of TCGA tumor samples, and the grouping information is from the hub gene hierarchical clustering results. Maftools is an R package for the analysis of somatic variant data, which can export results in form of charts and graph95. The calculation of TMB and difference mutation analysis were used by the maftools.

Gene dependence refers to the extent to which genes are essential for cell proliferation and survival. The Cancer Dependency

Map (Depmap, https://depmap.org/portal/) database provides genome-wide gene dependence data for large number of tumor cell lines96. Gene mutation data of cell lines were obtained from the

CCLE database (https://sites.broadinstitute.org/ccle). The genes appearing in the gene mutation data of cell lines were extracted and sorted into mutation list. Each gene in the list was then analyzed for survival-dependent differences. For each gene in the mutation list, we compared the gene dependence score (S) between cell lines with and without the gene mutation. Specifically, cell lines from the CCLE mutation dataset harboring a mutation in a given gene were classified into one group, while cell lines without the mutation constituted the control group. S for the mutation (Sm) and wild-type (Swt) cell lines were then extracted from the DepMap database. A two-sample t-test was used to assess the statistical significance of differences between Sm and Swt. The test yielded a P-value, with an alpha level set at 0.05, to determine the significance of the difference in means. Dm was defined as gene mutation dependence, the computational formula was as follows: Subsequently, we conducted a standardization assessment of Dm, employing the following 2( ̅

− ̅ ) ̅ + ̅ (4) In this study, we categorized mutations identified in cell lines into three groups based on their impact on gene dependency. Functional mutations refer to the mutations that exhibit 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 significant changes in gene dependency scores, with a P-value < 0.05 and Sdm > 0.1. Nonfunctional mutations were those without significant changes in gene dependency scores compared to wildtype. Subtype-associated functional mutations were a subset of functional mutations that show statistically significant differences in occurrence frequency across different LUAD subtypes.

The IC50 data of 76 compounds of LUAD cell lines were obtained from Genomics of Drug Sensitivity in Cancer (GDSC) database97. The hub gene expression data of A549 cell lines after treatment with 76 compounds was obtained from

Immune infiltration data was calculated by quantiseq method99. Immunedeconv was an R package for unified access to computational methods for estimating immune Cell fractions from bulk RNA-seq data100. The data were from the gene expression data of TCGA tumor samples after TPM was standardized. Then the immune cell fractions of tumor samples were calculated by quantiseq method invoking immunedeconv.

GSVA is an R package for calculate biological function score based on a single sample101. The data was from TCGA in the calculation process, and the software package was GSVA. The gene set of the immune score was from other study (Table 8) 99. The gene sets that calculated the mitosis score were obtained from gene ontology (GO:0140014).

This work was supported by the National Key Research and Development Program of China (grants 2022YFA1103900 to H.J., F.L.; 2020YFA0803300 to H.J.); the National Natural Science Foundation of China (grants 82341002 to H.J., 32293192 to H.J., 82030083 to H.J.,

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Xinlei Cai1, Yi Ye2, Xiaoping Liu1, Zhaoyuan Fang4,5, Luonan Chen1,2,6, Fei Li3*, Hongbin 1Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences,